diff --git "a/vec_37_di/storage_nodes/default__vector_store.json" "b/vec_37_di/storage_nodes/default__vector_store.json" new file mode 100644--- /dev/null +++ "b/vec_37_di/storage_nodes/default__vector_store.json" @@ -0,0 +1,132934 @@ +{ + "embedding_dict": { + "09eae2c9-3a7f-4ff2-b7df-f7e98bb4ff4e": [ + -0.008276755921542645, + 0.012372066266834736, + -0.006225185468792915, + 0.00845685601234436, + 0.00808099564164877, + -0.022927476093173027, + -0.022676901891827583, + 0.011604685336351395, + -0.02571510523557663, + -0.0018176366575062275, + 0.014337502419948578, + -0.0861346423625946, + 0.011988376267254353, + -0.019513411447405815, + -0.036145228892564774, + -0.007227479480206966, + -0.03586333245038986, + -0.005590921267867088, + 0.01780637912452221, + -0.03727280721068382, + 0.026122286915779114, + 0.027343833819031715, + 0.0321047306060791, + 0.0187930129468441, + 0.015159697271883488, + 0.022990118712186813, + 0.014791667461395264, + 0.004154038615524769, + 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medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Data Quality Framework for EU medicines regulation\n\n| | |\n| ------------------------------------------------- | ---------------- |\n| Draft agreed by BDSG for release for consultation | 10 October 2022 |\n| End of consultation (deadline for comments) | 18 November 2022 |\n| Agreed by BDSG and MWP | 30 June 2023 |\n| Adopted by CHMP | 30 October 2023 |\n\n| | |\n| ------------ | -------------------------------------------------------------------------------------------------------- |\n| **Keywords** | Data quality framework, medicines regulation, data quality dimensions, primary and secondary use of data |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un breve resumen del contenido:\n\n### Resumen del contexto\nEl documento titulado \"Data Quality Framework for EU medicines regulation\" establece un marco para la calidad de los datos en la regulación de medicamentos en la Unión Europea. Incluye fechas clave relacionadas con el proceso de consulta y adopción del marco, así como palabras clave que destacan los temas centrales del documento, como la calidad de los datos y su uso en la regulación de medicamentos.\n\n### Preguntas\n\n1. **¿Cuáles son las fechas clave en el proceso de desarrollo del marco de calidad de datos para la regulación de medicamentos en la UE?**\n - Respuesta: Las fechas clave son: el borrador fue acordado para consulta el 10 de octubre de 2022, el final de la consulta fue el 18 de noviembre de 2022, el acuerdo por parte de BDSG y MWP fue el 30 de junio de 2023, y finalmente, fue adoptado por CHMP el 30 de octubre de 2023.\n\n2. **¿Qué entidades estuvieron involucradas en la creación y adopción del marco de calidad de datos?**\n - Respuesta: Las entidades involucradas incluyen el BDSG (Grupo de Datos de Salud), MWP (Grupo de Trabajo de Medicamentos) y CHMP (Comité de Medicamentos de Uso Humano).\n\n3. **¿Cuáles son los temas clave abordados en el marco de calidad de datos según las palabras clave proporcionadas?**\n - Respuesta: Los temas clave incluyen el marco de calidad de datos, la regulación de medicamentos, las dimensiones de calidad de los datos y el uso primario y secundario de los datos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, consultation process, BDSG, CHMP", + "_node_type": "TextNode", + "document_id": "0c527687-4f5b-410c-a186-0c95584fa51c", + "doc_id": "0c527687-4f5b-410c-a186-0c95584fa51c", + "ref_doc_id": "0c527687-4f5b-410c-a186-0c95584fa51c" + }, + "4ae2c71e-69c0-439e-b1c6-78d622f6f554": { + "page_label": "2", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Table of contents\n\n1. Executive summary ........................................................................................................... 4\n\n2. Abbreviations ..................................................................................................................... 5\n\n3. Background - The need for a Data Quality Framework for medicines regulation .................................................................................................................. 5\n\n4. Scope of this DQF ............................................................................................................. 6\n4.1. Definition of data ............................................................................................................. 7\n4.2. Definition of DQ ............................................................................................................... 7\n4.3. Limitations of scope ........................................................................................................ 7\n4.4. Structure of this DQF ...................................................................................................... 8\n\n5. General considerations underlying the maintenance and assessment of DQ .......................................................................................................................... 8\n5.1. DQ determinants for evidence generation ...................................................................... 8\n5.2. DQ along the evidence generation process .................................................................. 10\n5.2.1. DQ vs standardisation ............................................................................................... 11\n5.2.2. Primary vs secondary use of data ............................................................................ 11\n5.2.3. Publication vs data consumption .............................................................................. 12\n5.3. Data and metadata ....................................................................................................... 12\n5.4. Data immutability .......................................................................................................... 13\n5.5. Data vs information ....................................................................................................... 13\n5.6. Frame of reference (validation vs verification) .............................................................. 13\n5.7. Granularity of data and DQ ........................................................................................... 13\n\n6. DQ dimensions and metrics .......................................................................................... 14\n6.1. Reliability ....................................................................................................................... 15\n6.1.1. When considering the “fit for purpose” definition of quality, Reliability covers how correct and true the data are. Reliability sub-dimensions ................................................. 15\n6.1.2. Considerations for Reliability .................................................................................... 16\n6.1.3. Examples of reliability metrics .................................................................................. 17\n6.2. Extensiveness ............................................................................................................... 19\n6.2.1. Sub-dimensions of Extensiveness ............................................................................ 19\n6.2.2. Considerations for Extensiveness ............................................................................ 20\n6.2.3. Examples of metrics for Extensiveness .................................................................... 20\n6.3. Coherence ..................................................................................................................... 20\n6.3.1. Sub-dimensions of Coherence .................................................................................. 21\n6.3.2. Considerations for Coherence .................................................................................. 21\n6.3.3. Examples of metrics for Coherence .......................................................................... 23\n6.4. Timeliness ..................................................................................................................... 25\n6.4.1. Sub-dimensions of Timeliness .................................................................................. 25\n6.4.2. Considerations for Timeliness ................................................................................... 25\n6.4.3. Examples of metrics for Timeliness .......................................................................... 25\n6.5. Relevance ..................................................................................................................... 25\n6.5.1. Examples of metrics for Relevance .......................................................................... 26\n\n7. General recommendations and maturity models ...................................................... 26\n7.1. Foundational determinants: Recommendation and maturity levels ............................. 32", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que pueden proporcionar respuestas específicas basadas en el contexto del documento sobre el marco de calidad de datos en la regulación de medicamentos de la UE:\n\n1. **¿Cuáles son los determinantes clave de la calidad de los datos (DQ) que se deben considerar para la generación de evidencia en la regulación de medicamentos?**\n - Esta pregunta se centra en la sección 5.1 del documento, donde se discuten los determinantes de la calidad de los datos en el contexto de la generación de evidencia, lo que puede no estar ampliamente disponible en otras fuentes.\n\n2. **¿Qué métricas específicas se utilizan para evaluar la fiabilidad de los datos en el marco de calidad de datos propuesto?**\n - Esta pregunta se relaciona con la sección 6.1, que aborda la fiabilidad y sus subdimensiones, así como ejemplos de métricas. La respuesta puede ofrecer detalles técnicos que no se encuentran fácilmente en otros documentos.\n\n3. **¿Cómo se define y se diferencia la \"inmutabilidad de los datos\" en el contexto de la calidad de los datos y por qué es importante?**\n - Esta pregunta se refiere a la sección 5.4, donde se discute la inmutabilidad de los datos. La respuesta puede proporcionar una comprensión más profunda de este concepto y su relevancia en la regulación de medicamentos.\n\n### Resumen de nivel superior del contexto circundante:\n\nEl documento presenta un marco de calidad de datos (DQF) para la regulación de medicamentos en la Unión Europea, destacando la necesidad de establecer estándares claros para garantizar la calidad de los datos utilizados en la generación de evidencia. Se abordan definiciones clave, limitaciones del marco y consideraciones generales sobre la evaluación y mantenimiento de la calidad de los datos. Además, se detallan dimensiones y métricas específicas de la calidad de los datos, como la fiabilidad, extensividad, coherencia, puntualidad y relevancia, junto con recomendaciones y modelos de madurez para su implementación.", + "prev_section_summary": "El documento \"Data Quality Framework for EU medicines regulation\" establece un marco para la calidad de los datos en la regulación de medicamentos en la Unión Europea. Los temas clave abordados incluyen:\n\n- **Marco de calidad de datos**: Se centra en la importancia de mantener altos estándares de calidad en los datos utilizados en la regulación de medicamentos.\n- **Regulación de medicamentos**: Examina cómo la calidad de los datos impacta en la regulación y supervisión de los medicamentos en la UE.\n- **Dimensiones de calidad de los datos**: Se refiere a los diferentes aspectos que determinan la calidad de los datos, tanto para su uso primario como secundario.\n\nLas entidades involucradas en el desarrollo y adopción del marco son:\n\n- **BDSG (Grupo de Datos de Salud)**: Participó en la creación y revisión del borrador del marco.\n- **MWP (Grupo de Trabajo de Medicamentos)**: Colaboró en la revisión y acuerdo del marco.\n- **CHMP (Comité de Medicamentos de Uso Humano)**: Es la entidad que finalmente adoptó el marco el 30 de octubre de 2023. \n\nEste marco es fundamental para asegurar que los datos utilizados en la regulación de medicamentos sean de alta calidad y confiables.", + "excerpt_keywords": "Keywords: data quality, medicines regulation, evidence generation, metrics, EU framework", + "_node_type": "TextNode", + "document_id": "7eed2c0e-f21e-4a76-90fe-65a51dfe487e", + "doc_id": "7eed2c0e-f21e-4a76-90fe-65a51dfe487e", + "ref_doc_id": "7eed2c0e-f21e-4a76-90fe-65a51dfe487e" + }, + "e6058b40-755a-4cea-b933-2ba2b5abb375": { + "page_label": "3", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "7.1.1. Level 1: documented ...................................................................................... 32\n7.1.2. Level 2: formalised ......................................................................................... 33\n7.1.3. Level 3: implemented ..................................................................................... 33\n7.1.4. Level 4: automated ........................................................................................ 33\n\n7.2. Intrinsic determinants: Recommendations and maturity levels ................................. 33\n7.2.1. Level 0: intrinsic ............................................................................................. 33\n7.2.2. Level 1: metadata .......................................................................................... 33\n7.2.3. Level 2: standardised ..................................................................................... 34\n7.2.4. Level 3: automated ........................................................................................ 34\n7.2.5. Level 4: feedback .......................................................................................... 34\n\n7.3. Question-specific determinants: Recommendations and maturity levels ................... 34\n7.3.1. Level 1: ad-hoc ............................................................................................. 34\n7.3.2. Level 2: domain-defined ................................................................................ 34\n7.3.3. Level 3: question-defined .............................................................................. 34\n\n# 8. Considerations for implementation of DQF ................................................. 35\n8.1. Quality at source .............................................................................................. 35\n8.2. The role of Master Data Management (MDM) and reference data ............................. 35\n8.3. The role of QMS and computerised systems ......................................................... 35\n8.4. The role of ISO and industry standards ............................................................... 36\n8.5. Notes on ALCOA+ ............................................................................................. 37\n8.6. Notes on implementation of DQ controls ............................................................. 37\n\n# 9. Glossary ..................................................................................................... 39\n\n# 10. References ............................................................................................... 42", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden responder específicamente con el contexto proporcionado, junto con un resumen de nivel superior que puede ayudar a formularlas:\n\n### Resumen de nivel superior:\nEl documento titulado \"Data quality framework EU medicines regulation\" presenta un marco para la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se estructura en diferentes niveles de madurez y recomendaciones, abordando determinantes intrínsecos y específicos de preguntas. Además, se discuten consideraciones para la implementación del marco, incluyendo la calidad en la fuente, la gestión de datos maestros, y el papel de los sistemas de gestión de calidad y estándares de la industria.\n\n### Preguntas:\n1. **¿Cuáles son los diferentes niveles de madurez en el marco de calidad de datos y qué características definen cada uno?**\n - Esta pregunta busca una descripción detallada de los niveles de madurez mencionados en la sección 7.1, que incluye desde el nivel documentado hasta el nivel automatizado.\n\n2. **¿Qué papel juegan los datos maestros y los datos de referencia en la implementación del marco de calidad de datos?**\n - Esta pregunta se centra en la sección 8.2, donde se discute la importancia de la gestión de datos maestros en el contexto de la calidad de los datos.\n\n3. **¿Qué consideraciones se deben tener en cuenta para asegurar la calidad de los datos desde la fuente?**\n - Esta pregunta se refiere a la sección 8.1, que aborda la importancia de la calidad en la fuente como un principio fundamental para la implementación efectiva del marco de calidad de datos. \n\nEstas preguntas están diseñadas para extraer información específica y detallada que se encuentra en el documento y que puede no estar disponible en otras fuentes.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nEl documento \"Data quality framework EU medicines regulation\" establece un marco de calidad de datos (DQF) para la regulación de medicamentos en la Unión Europea. A continuación se presentan los temas clave y entidades relevantes:\n\n1. **Introducción y Contexto**:\n - Se destaca la necesidad de un marco de calidad de datos para garantizar la integridad y fiabilidad de los datos utilizados en la regulación de medicamentos.\n\n2. **Definiciones Clave**:\n - **Datos**: Información utilizada en el contexto de la regulación de medicamentos.\n - **Calidad de Datos (DQ)**: Conjunto de características que determinan la utilidad de los datos para un propósito específico.\n\n3. **Determinantes de la Calidad de Datos**:\n - Se identifican factores que afectan la calidad de los datos en la generación de evidencia, como la fiabilidad, extensividad, coherencia, puntualidad y relevancia.\n\n4. **Dimensiones y Métricas de Calidad de Datos**:\n - **Fiabilidad**: Grado en que los datos son correctos y verdaderos.\n - **Extensividad**: Alcance y cobertura de los datos.\n - **Coherencia**: Consistencia interna de los datos.\n - **Puntualidad**: Actualidad de los datos.\n - **Relevancia**: Pertinencia de los datos para el contexto de uso.\n\n5. **Consideraciones Generales**:\n - Se discuten aspectos como la inmutabilidad de los datos, la diferencia entre datos e información, y la granularidad de los datos.\n\n6. **Recomendaciones y Modelos de Madurez**:\n - Se ofrecen directrices para la implementación del DQF y se presentan niveles de madurez para evaluar la calidad de los datos en el contexto regulatorio.\n\n### Entidades Relevantes:\n- **Regulación de Medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Unión Europea**: Entidad geográfica y política que establece las normativas.\n- **Datos y Metadatos**: Elementos fundamentales en la evaluación de la calidad de datos.\n\nEste resumen proporciona una visión general de los temas y conceptos clave abordados en el documento, destacando la importancia de establecer estándares de calidad en la regulación de medicamentos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, maturity levels, Master Data Management, implementation considerations", + "_node_type": "TextNode", + "document_id": "f93d2b27-7393-45b6-8ba7-f08ba15533b4", + "doc_id": "f93d2b27-7393-45b6-8ba7-f08ba15533b4", + "ref_doc_id": "f93d2b27-7393-45b6-8ba7-f08ba15533b4" + }, + "1ec5e37f-b59b-4c48-a8ad-93e306ad4b4a": { + "page_label": "4", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page4-1.jpg" + ], + "parsed_text_markdown": "# 1. Executive summary\n\nThis document is the first release of the EU Data Quality Framework (DQF) for medicines regulation and defines high-level principles and procedures that apply across EMA’s regulatory mandate. This framework provides general considerations on data quality that are relevant for regulatory decision making, definitions for data quality dimensions and sub-dimensions, as well as their characterisation and related metrics. It provides an analysis of what data quality actions and metrics should be considered in different use cases and introduces a maturity model to guide the evolution of automation to support data-driven regulatory decision making.\n\nThis document is intended to be a general resource from which more focused recommendations can be derived for specific regulatory domains with specified metrics and checks. See figure 1 for a summarised representation of the key points of the DQF.\n\n## Figure 1 - Representation of the key points of the Data Quality Framework\n\n### Data Quality Determinants\n\n- **Determinants**\n- Question Specific: Aspects derived by a specific question\n- Intrinsic: Aspects that are relevant to a specific application\n- Foundational: Present to generate, collect and expose data\n\n### Data Quality Dimensions\n\n- **DQ**\n- Extensiveness: Are data sufficient?\n- Reliability: Are data faithfully representing what it is meant to be?\n- Coherence: Are data analyzable?\n- Relevance: Are data of the right kind?\n- Timeliness: Are data available at the right time?\n\n### Data Life Cycle\n\n1. Definition of data requirement\n2. Data collection generation\n3. Data managing and processing\n4. Data publishing\n5. Data procurement\n6. Testing and acceptance\n7. Delivery for consumption\n\n### Characterisation of Determinants for the Development of the Maturity Models\n\n- Foundational\n- Intrinsic\n- Question-Specific\n![page4-1.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page4-1.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que pueden proporcionar respuestas específicas basadas en el contexto del documento sobre el Marco de Calidad de Datos (DQF) para la regulación de medicamentos en la UE:\n\n1. **¿Cuáles son los principios fundamentales que guían la evaluación de la calidad de los datos en el contexto de la regulación de medicamentos según el DQF?**\n - Esta pregunta busca explorar los principios de alto nivel que se establecen en el marco y cómo estos principios se aplican a la toma de decisiones regulatorias.\n\n2. **¿Qué métricas específicas se sugieren para evaluar cada una de las dimensiones de calidad de datos, como la extensividad y la fiabilidad?**\n - Esta pregunta se centra en las métricas y acciones concretas que se deben considerar para evaluar la calidad de los datos en diferentes casos de uso, lo que puede no estar detallado en otras fuentes.\n\n3. **¿Cómo se estructura el ciclo de vida de los datos según el DQF y qué pasos son críticos para garantizar la calidad de los datos en cada fase?**\n - Esta pregunta busca desglosar el ciclo de vida de los datos y resaltar los pasos clave que deben seguirse para asegurar que los datos sean de alta calidad desde su definición hasta su entrega.\n\n### Resumen de nivel superior del contexto circundante:\nEl documento presenta el primer lanzamiento del Marco de Calidad de Datos (DQF) de la UE para la regulación de medicamentos, estableciendo principios y procedimientos que son relevantes para la toma de decisiones regulatorias. Se definen dimensiones de calidad de datos y se introduce un modelo de madurez para guiar la automatización en la toma de decisiones basadas en datos. Además, se describe un ciclo de vida de los datos que incluye desde la definición de requisitos hasta la entrega para consumo, enfatizando la importancia de la calidad de los datos en cada etapa.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades de la Sección\n\nEl documento \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. A continuación se presentan los temas clave y entidades relevantes de la sección:\n\n#### 1. **Niveles de Madurez en la Calidad de Datos**\n - **Nivel 1: Documentado** - Se refiere a la existencia de documentación sobre los procesos de calidad de datos.\n - **Nivel 2: Formalizado** - Implica que los procesos están formalmente establecidos y reconocidos.\n - **Nivel 3: Implementado** - Indica que los procesos de calidad de datos están en práctica y funcionando.\n - **Nivel 4: Automatizado** - Se refiere a la automatización de los procesos de calidad de datos para mejorar la eficiencia y la precisión.\n\n#### 2. **Determinantes Intrínsecos**\n - **Nivel 0: Intrínseco** - Se refiere a la calidad de los datos en su forma más básica.\n - **Nivel 1: Metadata** - Enfocado en la gestión de metadatos para mejorar la calidad de los datos.\n - **Nivel 2: Estandarizado** - Implica la adopción de estándares para asegurar la calidad de los datos.\n - **Nivel 3: Automatizado** - Se refiere a la automatización de procesos relacionados con la calidad de los datos intrínsecos.\n - **Nivel 4: Retroalimentación** - Involucra mecanismos de retroalimentación para mejorar continuamente la calidad de los datos.\n\n#### 3. **Determinantes Específicos de Preguntas**\n - **Nivel 1: Ad-hoc** - Procesos de calidad de datos que son improvisados y no sistemáticos.\n - **Nivel 2: Definido por el dominio** - Procesos que son específicos para un dominio particular.\n - **Nivel 3: Definido por la pregunta** - Procesos que están diseñados para responder a preguntas específicas.\n\n#### 4. **Consideraciones para la Implementación del Marco de Calidad de Datos (DQF)**\n - **Calidad en la Fuente** - Importancia de asegurar la calidad de los datos desde el origen.\n - **Gestión de Datos Maestros (MDM)** - Rol crucial de la gestión de datos maestros y datos de referencia en la calidad de los datos.\n - **Sistemas de Gestión de Calidad (QMS)** - Importancia de los sistemas de gestión de calidad y sistemas informáticos en la implementación del marco.\n - **Normas ISO y de la Industria** - Relevancia de seguir estándares reconocidos para asegurar la calidad de los datos.\n - **Notas sobre ALCOA+** - Principios que guían la calidad de los datos en el contexto regulatorio.\n - **Notas sobre la Implementación de Controles de Calidad de Datos** - Consideraciones prácticas para implementar controles efectivos.\n\nEste resumen proporciona una visión general de los temas y entidades clave que se discuten en la sección, destacando la estructura del marco de calidad de datos y su importancia en la regulación de medicamentos en la UE.", + "excerpt_keywords": "Keywords: Data Quality Framework, medicines regulation, EMA, data life cycle, maturity model", + "_node_type": "TextNode", + "document_id": "35797838-5194-4be8-8aa2-9362604e13a8", + "doc_id": "35797838-5194-4be8-8aa2-9362604e13a8", + "ref_doc_id": "35797838-5194-4be8-8aa2-9362604e13a8" + }, + "e5704e73-625e-4a89-b0f5-d27e910dbf91": { + "page_label": "5", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "## 2. Abbreviations\n\n| Abbreviation | Definition |\n| ------------ | ------------------------------------------------------------------------------------------------------------------------------- |\n| CDM | Common Data Model |\n| CHMP | Committee for Medicinal Products for Human Use |\n| DQ | Data Quality |\n| DQF | Data Quality Framework |\n| EHR | Electronic Health Record |\n| EHDS | European Health Data Space |\n| EMA | European Medicines Agency |\n| ETL | Extract, Transform and Load |\n| FAIR | Findable, Accessible, Interoperable and Reusable |\n| GxP | Good x Practices, where x stands for laboratory (GLP), clinical (GCP), manufacturing (GMP), distribution or documentation (GDP) |\n| ICSR | Individual Case Safety Reports |\n| ISO | International Organisation for Standardisation |\n| MDM | Master Data Management |\n| QMS | Quality Management System |\n| QSR | Quality System Regulation |\n| RWD | Real-World Data |\n| RWE | Real-World Evidence |\n\n## 3. Background - The need for a Data Quality Framework for medicines regulation\n\nAs acknowledged in the recommendations of the HMA-EMA Joint Big Data Task Force and the workplan of the HMA-EMA Joint Big Data Steering Group, establishing an EU framework for data quality (DQ) and representativeness is a critical element for realising the full potential of (big) data and driving regulatory decisions.\n\nIn recent years, the EU regulatory assessment process has been exploring a shift from document-based submissions to direct assessments of the data underlying those submissions. To facilitate this potential shift, there is an increased need for standardisation [1], and the need for a framework, which would characterise DQ and would allow the regulator to make reliable assessments of whether the data are fit for the purpose of decision making.\n\nIn addition, the progress in digitalisation and information technology creates new opportunities, but also contributes to an increasingly complex landscape for regulatory decision making. While new types of data become available, guidelines or methods to demonstrate whether such data are adequate for", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de Nivel Superior\nEl documento aborda la necesidad de un marco de calidad de datos (DQF) en la regulación de medicamentos en la Unión Europea. Reconoce que la transición de un enfoque basado en documentos a evaluaciones directas de datos es crucial para mejorar la toma de decisiones regulatorias. Además, destaca la importancia de la estandarización y la adaptación a un entorno digital en evolución, donde nuevos tipos de datos están disponibles, pero también presentan desafíos en términos de calidad y adecuación para la toma de decisiones.\n\n### Preguntas Específicas\n\n1. **¿Cuál es el papel del Comité para Medicinales de Uso Humano (CHMP) en el contexto del marco de calidad de datos propuesto?**\n - Respuesta: El CHMP es mencionado como parte del proceso regulatorio en la UE, y su papel sería fundamental en la implementación y evaluación del marco de calidad de datos, asegurando que las decisiones regulatorias se basen en datos de alta calidad y representatividad.\n\n2. **¿Qué desafíos presenta la digitalización y la tecnología de la información en la toma de decisiones regulatorias según el documento?**\n - Respuesta: La digitalización y la tecnología de la información crean un paisaje complejo para la toma de decisiones regulatorias, ya que, aunque ofrecen nuevas oportunidades, también complican la evaluación de la calidad y la adecuación de los nuevos tipos de datos disponibles.\n\n3. **¿Por qué es importante la estandarización en el contexto de la evaluación de datos para la regulación de medicamentos?**\n - Respuesta: La estandarización es crucial para facilitar la transición de un enfoque basado en documentos a evaluaciones directas de datos, permitiendo a los reguladores realizar evaluaciones confiables sobre si los datos son adecuados para la toma de decisiones, lo que a su vez puede mejorar la eficacia y la seguridad de los medicamentos en el mercado.", + "prev_section_summary": "El documento presenta el primer lanzamiento del Marco de Calidad de Datos (DQF) de la UE para la regulación de medicamentos, estableciendo principios y procedimientos que son relevantes para la toma de decisiones regulatorias en la Agencia Europea de Medicamentos (EMA). A continuación se resumen los temas clave y entidades:\n\n### Temas Clave:\n\n1. **Principios y Procedimientos**: Se definen principios de alto nivel que guían la evaluación de la calidad de los datos en el contexto regulatorio.\n\n2. **Dimensiones de Calidad de Datos**: Se identifican cinco dimensiones clave:\n - **Extensividad**: Suficiencia de los datos.\n - **Fiabilidad**: Representación fiel de los datos.\n - **Coherencia**: Análisis de los datos.\n - **Relevancia**: Pertinencia de los datos.\n - **Oportunidad**: Disponibilidad de los datos en el momento adecuado.\n\n3. **Determinantes de Calidad de Datos**: Se clasifican en tres categorías:\n - **Específicos de Pregunta**: Aspectos derivados de preguntas específicas.\n - **Intrínsecos**: Aspectos relevantes para aplicaciones específicas.\n - **Fundacionales**: Elementos necesarios para generar, recolectar y exponer datos.\n\n4. **Ciclo de Vida de los Datos**: Se describe un ciclo que incluye siete etapas, desde la definición de requisitos hasta la entrega para consumo, enfatizando la importancia de la calidad en cada fase.\n\n5. **Modelo de Madurez**: Se introduce un modelo para guiar la evolución de la automatización en la toma de decisiones basadas en datos.\n\n### Entidades:\n\n- **Marco de Calidad de Datos (DQF)**: Documento que establece principios y procedimientos para la calidad de datos en la regulación de medicamentos.\n- **Agencia Europea de Medicamentos (EMA)**: Entidad reguladora a la que se aplica el DQF.\n- **Dimensiones de Calidad de Datos**: Extensividad, Fiabilidad, Coherencia, Relevancia, Oportunidad.\n- **Determinantes de Calidad de Datos**: Específicos de Pregunta, Intrínsecos, Fundacionales.\n- **Ciclo de Vida de los Datos**: Etapas que abarcan desde la definición de requisitos hasta la entrega de datos.\n\nEste marco busca ser un recurso general del cual se pueden derivar recomendaciones más específicas para diferentes dominios regulatorios.", + "excerpt_keywords": "Keywords: Data Quality Framework, EU Medicines Regulation, Digitalisation, Regulatory Decision Making, Standardisation", + "_node_type": "TextNode", + "document_id": "2a635a34-8f58-49f5-8137-0e1827fcd6de", + "doc_id": "2a635a34-8f58-49f5-8137-0e1827fcd6de", + "ref_doc_id": "2a635a34-8f58-49f5-8137-0e1827fcd6de" + }, + "bd34d0de-756e-498c-9743-930e468a2d69": { + "page_label": "6", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "decision making are still scarce. Therefore, a Data Quality Framework (DQF) is needed to guide coherent and consistent quality assessment procedures.\n\nOne notable example is healthcare data that are becoming available in increasing quantity to potentially support regulatory decision making for medicines. Information derived from routinely collected Real-World Data (RWD) has for a long time been used to support regulatory decision making on the safety of drugs in the post-authorisation phase. While most traditional pre-approval randomised controlled clinical trials remain the fundamental method of establishing the safety and efficacy of medicines during the pre-authorisation phase, they could potentially benefit from the evidence generated using this data. Insights into the Real-World are also required by downstream stakeholders including Health Technology Assessment bodies, payers and ultimately clinicians and patients. Bridging these gaps, the regulatory network needs to acquire the ability to describe and quantify the degree to which these data are accurate and fit for purpose.\n\n## 4. Scope of this DQF\n\nThis document aims to provide a set of definitions, principles and guidelines that can coherently be applied to any data source for the purpose of characterising, assessing, and assuring DQ for regulatory decision making. This framework is intended to encompass primary and secondary use, as well as metadata and supporting information (e.g., MDM (Master data management), underlying reference Data) applicable to support Committee for Medicinal Products for Human Use (CHMP) decision making. The document is targeted primarily at the EU medicine regulatory network, but the relevance of the content can be of interest to a wider range of stakeholders such as marketing authorisation holders, data source holders, researchers, and patient associations.\n\nAs methods, terminologies, metrics, and issues vary across data types and sources, this framework seeks to provide a coherent basis to identify, define, and further develop DQ assessment procedures and recommendations for current and novel data types.\n\nObjectives of this framework are therefore to achieve consistency in DQ related processes, foster the development of horizontal systems1 for DQ and eventually enable a more adequate and automated use of data for regulatory decision making.\n\nThis framework builds on the recommendations of TEHDAS [2] and extends them with a classification of quality dimensions and assessment criteria, as well as guidelines for their application. It builds on the definitions and recommendations that have been proposed in several existing DQ frameworks, including [2-13].\n\nWhile many examples provided in this framework relate to Real-World Data, the scope of this framework extends to a broad range of regulatory activities and their respective data types, including Real-World Data [14, 15] (including within clinical trials to supplement trial-specific data collection), bioanalytical omics data, animal health data, preclinical data (cell and animal-based laboratory data), spontaneous adverse event reporting data, chemical and manufacturing control data, and more.\n\n----\n\n1 A \"Horizontal system\" provides a specific set of functionalities across a variety of use cases. In this case the intentions to develop systems and approaches to DQ that can be used (and potentially shared) across use cases. \"Horizontal system\" is defined by contrast to \"Vertical system\", where all DQ processes and system would be developed ad hoc and targeted to a specific use case.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resumen del Contexto\n\nEl documento presenta un marco de calidad de datos (DQF) destinado a guiar la evaluación coherente y consistente de la calidad de los datos en el contexto de la regulación de medicamentos en la UE. Se destaca la importancia de los datos del mundo real (RWD) en la toma de decisiones regulatorias, especialmente en la fase post-autorización de medicamentos. El DQF busca proporcionar definiciones, principios y directrices aplicables a diversas fuentes de datos, con el objetivo de lograr consistencia en los procesos relacionados con la calidad de los datos y facilitar un uso más adecuado y automatizado de estos datos en la toma de decisiones regulatorias. Además, el marco se basa en recomendaciones previas y se extiende para incluir una clasificación de dimensiones de calidad y criterios de evaluación.\n\n### Preguntas Específicas\n\n1. **¿Cuáles son las dimensiones de calidad y criterios de evaluación que se proponen en el marco de calidad de datos (DQF) para los datos del mundo real (RWD)?**\n - Esta pregunta busca información específica sobre las dimensiones y criterios que el DQF establece, que no se detalla en otros documentos o marcos existentes.\n\n2. **¿Qué papel desempeñan los diferentes actores, como los titulares de autorizaciones de comercialización y las asociaciones de pacientes, en la implementación del DQF en la regulación de medicamentos?**\n - Esta pregunta se centra en el papel y la relevancia de los diversos interesados en el contexto del DQF, lo que puede no estar claramente definido en otros lugares.\n\n3. **¿Cómo se diferencia un \"sistema horizontal\" de un \"sistema vertical\" en el contexto del DQF y cuáles son las implicaciones de esta distinción para la calidad de los datos?**\n - Esta pregunta busca profundizar en la conceptualización de los sistemas de calidad de datos y sus implicaciones prácticas, lo que puede no estar ampliamente discutido en otros documentos.\n\n### Resumen de Nivel Superior\n\nEl marco de calidad de datos (DQF) es esencial para la regulación de medicamentos en la UE, ya que busca establecer un enfoque coherente para evaluar la calidad de los datos, especialmente en el contexto de los datos del mundo real (RWD). Este marco no solo se aplica a la evaluación de la calidad de los datos, sino que también tiene implicaciones para la automatización y la consistencia en los procesos regulatorios. La colaboración entre diferentes actores del ecosistema de salud es crucial para la implementación efectiva del DQF, lo que resalta la necesidad de un enfoque integrado y compartido en la gestión de la calidad de los datos.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n**Temas Clave:**\n1. **Marco de Calidad de Datos (DQF):** Se destaca la necesidad de establecer un marco que garantice la calidad y representatividad de los datos en la regulación de medicamentos en la UE.\n2. **Transición en el Proceso Regulatorio:** Se está explorando un cambio de un enfoque basado en documentos a evaluaciones directas de los datos subyacentes, lo que requiere estandarización.\n3. **Desafíos de la Digitalización:** La digitalización y la tecnología de la información presentan tanto oportunidades como complejidades en la toma de decisiones regulatorias, especialmente en la evaluación de nuevos tipos de datos.\n\n**Entidades:**\n- **HMA-EMA:** Grupo de trabajo conjunto que recomienda la creación de un marco de calidad de datos.\n- **CHMP (Committee for Medicinal Products for Human Use):** Entidad clave en el proceso de evaluación y regulación de medicamentos.\n- **EMA (European Medicines Agency):** Agencia responsable de la regulación de medicamentos en la UE.\n- **EHR (Electronic Health Record):** Registros de salud electrónicos que son relevantes en el contexto de la calidad de datos.\n- **RWD (Real-World Data) y RWE (Real-World Evidence):** Tipos de datos que se están considerando en el nuevo enfoque regulatorio.\n\nEste resumen encapsula la importancia de un marco de calidad de datos en la regulación de medicamentos, la transición hacia evaluaciones basadas en datos y los desafíos que presenta la digitalización en este contexto.", + "excerpt_keywords": "Data Quality Framework, Real-World Data, regulatory decision making, quality assessment, horizontal systems", + "_node_type": "TextNode", + "document_id": "4ecb8d49-3525-44a6-817b-5a4608e9f739", + "doc_id": "4ecb8d49-3525-44a6-817b-5a4608e9f739", + "ref_doc_id": "4ecb8d49-3525-44a6-817b-5a4608e9f739" + }, + "6e04d386-339b-4aff-aebb-2841c5524316": { + "page_label": "7", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "## 4.1. Definition of data\n\nIn this DQF, data are considered as any information asset that represents measurements or observations and that can be used to support decision making, directly or indirectly through analysis.\n\n## 4.2. Definition of DQ\n\nIn general terms, quality is defined as an attribute of a product or service that defines the degree to which it meets customer and other stakeholder needs within statutory and regulatory requirements or its fitness for intended use[2]. The same principle applies to data and for the purpose of this document, the following definition is adopted:\n\nData quality is defined as: \"fitness for purpose for users’ needs in relation to health research, policy making, and regulation and that the data reflect the reality, which they aim to represent” [2]².\n\nTherefore, this DQF restricts its scope to determinants of DQ that are relevant for regulatory decision making.\n\n## 4.3. Limitations of scope\n\nFollowing the definition of DQ and the restricted focus on regulatory decision making this framework’s scope excludes:\n\n- **Analytical methods to derive evidence**, i.e., conclusions and insights, from underlying data. This framework focuses on defining guidelines about assessing the level of the quality of the data used for regulatory decisions, not on their actual usage for regulatory decision making and the methods involved. While data quality and methods for evidence generation are effectively a continuum in terms of decision making, when taking the perspective of data collection, dissemination, and re-use, they are distinct.\n\n- **Aspects of DQ that do not directly impact regulatory decision making** e.g., conciseness or accessibility. For instance, conciseness is a relevant dimension of data quality in that it affects fitness for purpose when transmitting or archiving large datasets (e.g., for genomics data). However, it is not relevant in terms of data being fit for purpose to answer a specific (regulatory) question. Accessibility can also be an important aspect of DQ, but in the context of a regulatory activity, data is by definition accessible to interested parties³.\n\n- **Data transparency**, intended as the characteristic of data being used lawfully, traceably and for valid purposes is also excluded from this framework. Issues related to data transparency go beyond data quality assessment in support of decision making⁴. Rather, this framework provides guidelines that can be part of a broader set of recommendations to support Data Transparency. As for accessibility, it should be noted that there may be an indirect impact of transparency to data quality, and this will be addressed, when relevant, in extensions of this Framework.\n\n- **Quality of the underlying elements the data refer to** e.g., when considering a dataset about the purity of a medicine, this framework will cover the reliability, completeness, and other aspects of quality.\n\n----\n\n² Note that reality is in general not fully observable. In this definition we consider how data reflects aspects of reality that data is designed to capture (e.g.: disease frequency in a population). Context is important to understand how what is observed relates to whole.\n\n³ In some cases, aspects of DQ that do not directly relate to decision making, may have an indirect impact on it, e.g., a data source that is broadly accessible will likely be less opaque, more validated, and potentially of higher quality. Such aspects might be considered and possibly quantified in future extensions of this framework.\n\n⁴ As an example, the EU GDPR regulation defines transparency as: \"The principle of transparency requires that any information addressed to the public or to the data subject be concise, easily accessible and easy to understand\". This poses a broader set of requirements than what is strictly related to the use of data for regulatory decision making.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con resúmenes de nivel superior que pueden ayudar a formularlas:\n\n### Resumen de nivel superior\nEl documento establece un marco de calidad de datos (DQF) en el contexto de la regulación de medicamentos en la UE. Define qué se entiende por \"datos\" y \"calidad de datos\", y delimita el alcance del marco, excluyendo aspectos que no impactan directamente en la toma de decisiones regulatorias. Se enfatiza que la calidad de los datos debe ser adecuada para las necesidades de los usuarios en relación con la investigación en salud, la formulación de políticas y la regulación.\n\n### Preguntas\n\n1. **¿Cuáles son los criterios específicos que se consideran para evaluar la calidad de los datos en el contexto de la toma de decisiones regulatorias?**\n - Esta pregunta busca detalles sobre los determinantes de la calidad de los datos que son relevantes para la regulación, que no se encuentran explícitamente en otras fuentes.\n\n2. **¿Por qué se excluyen aspectos como la concisión y la accesibilidad de los datos en el marco de calidad de datos para decisiones regulatorias?**\n - Esta pregunta se centra en la justificación detrás de las limitaciones del marco, proporcionando una comprensión más profunda de su enfoque y objetivos.\n\n3. **¿Cómo se relaciona la calidad de los datos con la realidad que intentan representar, según el marco de calidad de datos?**\n - Esta pregunta busca explorar la conexión entre los datos y la realidad, un aspecto que puede no estar claramente definido en otros documentos, y que es crucial para entender la validez de los datos en el contexto regulatorio. \n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del marco de calidad de datos que podrían no estar ampliamente discutidos en otras fuentes.", + "prev_section_summary": "### Temas Clave\n\n1. **Marco de Calidad de Datos (DQF)**: Se establece la necesidad de un DQF para guiar la evaluación coherente y consistente de la calidad de los datos en la regulación de medicamentos en la UE.\n\n2. **Datos del Mundo Real (RWD)**: Se destaca la importancia de los RWD en la toma de decisiones regulatorias, especialmente en la fase post-autorización de medicamentos. Estos datos pueden complementar los ensayos clínicos controlados aleatorios tradicionales.\n\n3. **Objetivos del DQF**: El marco busca proporcionar definiciones, principios y directrices aplicables a diversas fuentes de datos, con el fin de lograr consistencia en los procesos de calidad de datos y facilitar un uso más automatizado de estos datos en la toma de decisiones regulatorias.\n\n4. **Diversidad de Fuentes de Datos**: El DQF abarca una amplia gama de actividades regulatorias y tipos de datos, incluyendo datos bioanalíticos, datos de salud animal, datos preclínicos, y más.\n\n5. **Sistemas Horizontales vs. Verticales**: Se introduce la distinción entre sistemas horizontales, que ofrecen funcionalidades específicas para múltiples casos de uso, y sistemas verticales, que son desarrollados ad hoc para casos específicos.\n\n### Entidades\n\n- **Reguladores de Medicamentos**: Incluye el Comité para Medicamentos de Uso Humano (CHMP) y la red regulatoria de medicamentos de la UE.\n- **Interesados**: Titulares de autorizaciones de comercialización, titulares de fuentes de datos, investigadores y asociaciones de pacientes.\n- **Tipos de Datos**: Datos del mundo real (RWD), datos bioanalíticos, datos de salud animal, datos preclínicos, datos de informes de eventos adversos espontáneos, datos de control químico y de fabricación.\n\n### Conclusión\n\nEl DQF es fundamental para establecer un enfoque coherente en la evaluación de la calidad de los datos en la regulación de medicamentos, promoviendo la colaboración entre diversos actores del ecosistema de salud y facilitando un uso más eficiente y automatizado de los datos en la toma de decisiones regulatorias.", + "excerpt_keywords": "Keywords: data quality, regulatory decision making, data transparency, information asset, health research", + "_node_type": "TextNode", + "document_id": "8f193de5-ec7f-4983-a598-deeed2a701b9", + "doc_id": "8f193de5-ec7f-4983-a598-deeed2a701b9", + "ref_doc_id": "8f193de5-ec7f-4983-a598-deeed2a701b9" + }, + "5bd198bd-93e2-4de7-81ed-31a5d3d183ea": { + "page_label": "8", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "- the data, but not aspects of quality (in this case purity or pharmaceutical quality) of the medicine per se.\n\n- Semantic interoperability and standardisation are key DQ aspects for data usability and for the implementation of a DQF, but they do not affect the fitness for purpose of data respect to decision making (e.g.: a “false” dataset would not become less “false” if represented in a standard way). Therefore, the provision of guidelines and recommendations to define and select standards for interoperability shall fall out of the remits of this DQF. It falls within the scope of this document to suggest the application of standards to facilitate DQ assessment.\n\n- Recommendations for the specific design of systems, processes, and responsibilities to guarantee DQ, or specific solutions or products. This framework focuses on the principles that such systems, processes and the resulting data should follow, to enable and optimise the use of such data for regulatory activities.\n\n## 4.4. Structure of this DQF\n\nThe DQF for EU medicines regulation is composed of two parts, reflecting different stages in the specification process.\n\nThe first part (general framework) is designed to provide a coherent approach to DQ, encompassing a broad range of data types and extensible to novel use cases. To achieve this, it provides a common ground on different DQ aspects that apply to different data types and scenarios: definitions, DQ dimensions and examples of metrics covering such dimensions. It further identifies general patterns for the applicability of DQ processes and it articulates a set of maturity models designed to drive increased automation of data-driven medicines regulatory decision making.\n\nThe second part (framework specialisation) specialises and eventually extends such generic recommendations to cater for specific data types or regulatory questions. This part poses the basis for the derivation of actual implementable guidelines, which will need to evolve as data and technologies change over time.\n\nThis document is the first version of the DQF for EU Regulatory Network [15]. It focuses on the general framework and addresses terminology, definitions, and general guiding principles around DQ in the context of medicines regulation. In the upcoming years, the DQF will be updated regularly with further deep dives in regulatory use cases of particular interest. The document will be in line with developments in TEHDAS to further strengthen the EMA data qualification process and the collaboration with the European Health Data Space (EHDS).\n\nA glossary with the main terms and definitions can be found in chapter 9.\n\n# 5. General considerations underlying the maintenance and assessment of DQ\n\n## 5.1. DQ determinants for evidence generation\n\nThe landscape of data that can be potentially used for regulatory purposes extends to diverse data sources, each generated through different processes and fit for different primary and secondary uses. When considering the overall quality of a dataset at the point of regulatory decision making, it is important to distinguish what contributes to quality, and what can be measured or controlled at what", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que el contexto proporcionado puede responder, junto con un resumen de nivel superior que puede ayudar a formularlas:\n\n### Resumen de Nivel Superior\nEl documento describe un marco de calidad de datos (DQF) para la regulación de medicamentos en la UE, que se compone de dos partes: un marco general que abarca una amplia gama de tipos de datos y un marco de especialización que se adapta a tipos de datos específicos y preguntas regulatorias. Se enfatiza la importancia de la interoperabilidad semántica y la estandarización para la usabilidad de los datos, aunque estas no afectan la idoneidad de los datos para la toma de decisiones. Además, se menciona que el DQF se actualizará regularmente para alinearse con los desarrollos en el espacio de datos de salud europeo.\n\n### Preguntas Específicas\n\n1. **¿Cuáles son los principales determinantes de calidad de datos (DQ) que se deben considerar al generar evidencia para la toma de decisiones regulatorias?**\n - Esta pregunta se centra en los factores que influyen en la calidad de los datos y cómo se pueden medir o controlar en el contexto de la regulación de medicamentos.\n\n2. **¿Qué tipos de métricas se sugieren en el marco general para evaluar la calidad de los datos en diferentes escenarios regulatorios?**\n - Aquí se busca información sobre las métricas específicas que se pueden utilizar para evaluar la calidad de los datos, lo que puede ser crucial para la implementación efectiva del DQF.\n\n3. **¿Cómo se prevé que evolucione el DQF en los próximos años y qué papel jugará en la colaboración con el Espacio Europeo de Datos de Salud (EHDS)?**\n - Esta pregunta aborda la evolución futura del marco y su alineación con iniciativas más amplias en el ámbito de la salud, lo que puede proporcionar una visión sobre la dirección estratégica del DQF.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Definición de Datos**:\n - Los datos se consideran activos de información que representan mediciones u observaciones, utilizados para apoyar la toma de decisiones.\n\n2. **Definición de Calidad de Datos (DQ)**:\n - La calidad de los datos se define como la \"aptitud para el propósito\" en relación con las necesidades de los usuarios en investigación en salud, formulación de políticas y regulación. Se enfatiza que los datos deben reflejar la realidad que intentan representar.\n\n3. **Alcance del Marco de Calidad de Datos (DQF)**:\n - El marco se centra en los determinantes de la calidad de los datos relevantes para la toma de decisiones regulatorias, excluyendo aspectos que no impactan directamente en dicha toma de decisiones.\n\n4. **Limitaciones del Alcance**:\n - Se excluyen:\n - Métodos analíticos para derivar evidencia de los datos.\n - Aspectos de calidad de datos que no afectan directamente la toma de decisiones regulatorias, como la concisión y la accesibilidad.\n - La transparencia de los datos, que se considera un tema más amplio que la evaluación de la calidad de datos.\n - La calidad de los elementos subyacentes a los que se refieren los datos.\n\n5. **Contexto y Observabilidad**:\n - Se menciona que la realidad no es completamente observable y que los datos deben capturar aspectos específicos de la realidad, como la frecuencia de enfermedades en una población.\n\n### Entidades Clave\n- **Datos**: Activos de información que representan mediciones u observaciones.\n- **Calidad de Datos (DQ)**: Aptitud de los datos para satisfacer las necesidades de los usuarios.\n- **Marco de Calidad de Datos (DQF)**: Estructura que define y limita el enfoque sobre la calidad de los datos en decisiones regulatorias.\n- **Regulación de Medicamentos en la UE**: Contexto en el que se aplica el marco de calidad de datos.\n- **Transparencia de Datos**: Característica de los datos que se utiliza de manera legal y trazable, excluida del marco.\n\nEste resumen proporciona una visión general de los conceptos y limitaciones discutidos en la sección, así como las entidades clave involucradas en el marco de calidad de datos en la regulación de medicamentos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, interoperability, regulatory decision making, data framework", + "_node_type": "TextNode", + "document_id": "e06bf5df-bc4a-4fba-98f3-e719cebdbd05", + "doc_id": "e06bf5df-bc4a-4fba-98f3-e719cebdbd05", + "ref_doc_id": "e06bf5df-bc4a-4fba-98f3-e719cebdbd05" + }, + "93e4dfc3-a830-4d86-969c-830443bd8485": { + "page_label": "9", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page9-1.jpg" + ], + "parsed_text_markdown": "In this framework, such elements related to DQ are referred to as “determinants” and classified into three categories (see figure 2):\n\n1. **Foundational determinants** pertain to the processes and systems through which data are generated, collected, processed, and made available. Foundational determinants are what affects the quality of data, but it’s not part of the data themselves. As such, they do not depend on, and cannot be completely derived from, the content of a dataset. For data to be trusted for regulatory decision making, the underlying infrastructure and processes that collect, host, transform and move the data must be designed in such a way that the correspondence between data and the real entity it represents is not altered. Examples of foundational determinants are the use of certified software systems to collect and process data, the presence of processes, training, and audit processes to ensure data are properly recorded and documented, the validation and verifiability of data processing steps.\n\n2. **Intrinsic determinants** of data pertain to aspects that are inherent to a given dataset. Intrinsic determinants are what can be derived given a dataset and possibly some external generic knowledge, but without the context in which the data were generated, as well of the context the data will be used in (e.g., a scientific or regulatory question). Examples of intrinsic determinants are coherent or incoherent formatting, the presence of errors (e.g., truncation) or the plausibility of the data.\n\n3. **Question specific determinants** pertain to aspects of DQ that cannot generally be defined independently of a specific question or approach to analysis. Examples of question specific determinants are the acceptability of the completeness of a dataset, or its level of approximation (e.g., date expressed in dates or months) to answer a specific question.\n\nIn general, foundational determinants have a direct impact on DQ. When they cannot be controlled, the only option is to control the intrinsic aspects of DQ. The scope of such control is limited in its ability to assure fitness for purpose when a question (or set of typical questions) is not defined.\n\n----\n\n### Figure 2 - Determinants of data quality\n\n| Data Quality Determinants |\n|---------------------------|\n| Determinants |\n| Question Specific |\n| Intrinsic |\n| Foundational |\n\n- **Question Specific**: Aspects defined by a specific question\n- **Intrinsic**: Aspects that are inherent to a specific question\n- **Foundational**: Process to generate, collect and expose data\n![page9-1.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page9-1.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden responder específicamente con el contexto proporcionado, junto con un resumen de nivel superior que puede ayudar a formularlas:\n\n### Resumen de Nivel Superior\nEl marco de calidad de datos en la regulación de medicamentos de la UE clasifica los determinantes de la calidad de los datos en tres categorías: **determinantes fundamentales**, **determinantes intrínsecos** y **determinantes específicos de la pregunta**. Los determinantes fundamentales se refieren a los procesos y sistemas que afectan la calidad de los datos, mientras que los determinantes intrínsecos son características inherentes a un conjunto de datos. Por último, los determinantes específicos de la pregunta son aquellos que dependen del contexto de la pregunta o análisis que se realiza. La calidad de los datos es crucial para la toma de decisiones regulatorias, y el control de los determinantes fundamentales es esencial para asegurar la confianza en los datos.\n\n### Preguntas\n\n1. **¿Cuáles son los ejemplos específicos de determinantes fundamentales que pueden afectar la calidad de los datos en la regulación de medicamentos?**\n - Esta pregunta busca ejemplos concretos que no se mencionan explícitamente en el texto, lo que puede proporcionar una comprensión más profunda de cómo se implementan estos determinantes en la práctica.\n\n2. **¿Cómo se pueden controlar los determinantes intrínsecos de la calidad de los datos cuando los determinantes fundamentales no son controlables?**\n - Esta pregunta se centra en las estrategias o métodos que se pueden utilizar para manejar la calidad de los datos en situaciones donde la infraestructura o los procesos subyacentes no son óptimos.\n\n3. **¿Qué criterios se utilizan para evaluar la aceptabilidad de la completitud de un conjunto de datos en relación con una pregunta específica?**\n - Esta pregunta busca detalles sobre los estándares o métricas que se aplican para determinar si un conjunto de datos es adecuado para responder a una pregunta particular, lo que puede no estar claramente definido en el texto. \n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del marco de calidad de datos y pueden proporcionar información valiosa que no se encuentra fácilmente en otras fuentes.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Marco de Calidad de Datos (DQF)**:\n - El DQF para la regulación de medicamentos en la UE se compone de dos partes:\n - **Marco General**: Proporciona un enfoque coherente para la calidad de datos, abarcando diversos tipos de datos y extensible a nuevos casos de uso. Incluye definiciones, dimensiones de calidad de datos (DQ) y ejemplos de métricas.\n - **Especialización del Marco**: Extiende las recomendaciones generales para adaptarse a tipos de datos específicos y preguntas regulatorias, sirviendo como base para guías implementables.\n\n2. **Interoperabilidad Semántica y Estandarización**:\n - Se destacan como aspectos clave para la usabilidad de los datos, aunque no afectan la idoneidad de los datos para la toma de decisiones. La provisión de directrices para la selección de estándares de interoperabilidad queda fuera del alcance del DQF.\n\n3. **Determinantes de Calidad de Datos (DQ)**:\n - Se enfatiza la importancia de identificar qué contribuye a la calidad de un conjunto de datos y cómo se puede medir o controlar en el contexto de la toma de decisiones regulatorias.\n\n4. **Evolución del DQF**:\n - Se prevé que el DQF se actualice regularmente para alinearse con desarrollos en el Espacio Europeo de Datos de Salud (EHDS) y fortalecer el proceso de calificación de datos de la EMA.\n\n5. **Métricas y Modelos de Madurez**:\n - El marco general incluye ejemplos de métricas para evaluar la calidad de los datos y patrones generales para la aplicabilidad de procesos de calidad de datos, así como modelos de madurez para aumentar la automatización en la toma de decisiones regulatorias basadas en datos.\n\n6. **Colaboración con Iniciativas de Salud**:\n - El DQF se alineará con iniciativas como TEHDAS para mejorar la colaboración en el ámbito de la salud y la regulación de medicamentos.\n\n### Entidades Clave\n- **DQF (Data Quality Framework)**: Marco de calidad de datos para la regulación de medicamentos en la UE.\n- **EMA (Agencia Europea de Medicamentos)**: Organismo regulador que se beneficiará del DQF.\n- **EHDS (Espacio Europeo de Datos de Salud)**: Iniciativa con la que el DQF se alineará en el futuro.\n- **TEHDAS**: Iniciativa que fortalecerá el proceso de calificación de datos de la EMA.\n\nEste resumen proporciona una visión general de los temas y entidades clave abordados en la sección del documento sobre el marco de calidad de datos en la regulación de medicamentos en la UE.", + "excerpt_keywords": "Keywords: data quality, foundational determinants, intrinsic determinants, question specific determinants, EU medicines regulation", + "_node_type": "TextNode", + "document_id": "ac904b53-7487-4cde-98d9-42e194a930cc", + "doc_id": "ac904b53-7487-4cde-98d9-42e194a930cc", + "ref_doc_id": "ac904b53-7487-4cde-98d9-42e194a930cc" + }, + "5f926a5d-0fbb-42e7-8e92-357dedd90d4c": { + "page_label": "10", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# 5.2. DQ along the evidence generation process\n\nData that are suitable and available for evidence generation go through a process (part of a broader \"life cycle\"[^6]) that is specific to the type of data, the processes and organisations that produce it. For data that is already collected (secondary use), a fit for purpose assessment for the intended use should be done prior to this process. DQ checks occur at various steps of this process and may include iterative feedback loops.\n\nAs a reference, a general high-level lifecycle is outlined as follows (see Figure 3):\n\n- **Definition of data requirements**: what data are sought, and what their characteristics should be. For primary data this phase can include elements directly related to evidence generation.\n- **Data collection or generation**: gaining data reflecting the observed reality.\n- **Data management and processing**: including data transfers, normalisation, and cleansing.\n- **Data publishing**: making data available to consumers.\n- **Data procurement and aggregation**: sourcing data from one or more consumers.\n- **Testing and acceptance**: assessing the suitability of the procured data for intended needs.\n- **Delivery for consumption**: using data to support a specific activity, e.g., analysis.\n\nNot all phases here presented are present in all data workflows (e.g., data collected from sensor or social data may be collected on a \"what is available\" basis, rather than based on specific requirements) and possibly extra phases may apply, and the order may differ.\n\nFor the scope of the assessment and management of DQ, it is important to establish what determinants apply at which stage of this process, and what may be the impact. For instance, intrinsic aspects of DQ can be measured and such measures could be used to improve reliability at the stage of data collection and generation, or it could be used to provide an assessment of quality at publication time. Integration with additional data would require re-assessment. Question-specific determinants of DQ need to be assessed each time data are repurposed to answer a question it was not originally collected or designed for.\n\nDQ checks occur at various steps along the evidence generation process and may include iterative feedback loops as indicated by the dashed line in figure 3.\n\n[^6]: The data life cycle is broader in that it would extend to aspects of data disposal and maintenance beyond usage.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior que puede ayudar a formularlas:\n\n### Resumen de Nivel Superior\nEl documento describe un marco de calidad de datos (DQ) en el contexto de la regulación de medicamentos en la UE, centrándose en el proceso de generación de evidencia. Se detalla un ciclo de vida de los datos que incluye varias fases, desde la definición de requisitos de datos hasta la entrega para consumo. Se enfatiza la importancia de realizar evaluaciones de adecuación y controles de calidad en diferentes etapas del proceso, así como la necesidad de re-evaluar la calidad de los datos cuando se utilizan para propósitos distintos a los originales.\n\n### Preguntas Específicas\n\n1. **¿Cuáles son las fases específicas del ciclo de vida de los datos que se deben considerar para asegurar la calidad de los datos en la generación de evidencia?**\n - Esta pregunta se puede responder al enumerar y describir cada una de las fases mencionadas en el documento, como la definición de requisitos de datos, la recolección, el procesamiento, la publicación, la adquisición, la prueba y la entrega.\n\n2. **¿Qué tipo de evaluaciones de adecuación se deben realizar para los datos de uso secundario antes de su utilización en la generación de evidencia?**\n - La respuesta se puede encontrar en la parte que menciona la necesidad de realizar una evaluación de \"fit for purpose\" antes de utilizar datos ya recolectados, lo que implica un análisis de su idoneidad para el uso previsto.\n\n3. **¿Cómo se pueden medir los aspectos intrínsecos de la calidad de los datos y qué impacto pueden tener en las diferentes etapas del proceso de generación de evidencia?**\n - Esta pregunta puede ser respondida al discutir cómo las medidas de calidad pueden mejorar la fiabilidad en la recolección y generación de datos, así como proporcionar una evaluación de calidad en el momento de la publicación, y cómo la integración de datos adicionales requiere una nueva evaluación de calidad. \n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del marco de calidad de datos y su aplicación en la regulación de medicamentos, proporcionando respuestas que son relevantes y específicas al contexto presentado.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nEl contenido se centra en el marco de calidad de datos (DQ) en la regulación de medicamentos de la Unión Europea, donde se clasifican los determinantes de la calidad de los datos en tres categorías principales:\n\n1. **Determinantes Fundamentales**: Se refieren a los procesos y sistemas que afectan la calidad de los datos, pero que no son parte de los datos en sí. Incluyen:\n - Uso de sistemas de software certificados para la recolección y procesamiento de datos.\n - Presencia de procesos, capacitación y auditorías para asegurar la correcta documentación de los datos.\n - Validación y verificabilidad de los pasos de procesamiento de datos.\n\n2. **Determinantes Intrínsecos**: Son características inherentes a un conjunto de datos que pueden derivarse de este, sin considerar el contexto en el que fueron generados. Ejemplos incluyen:\n - Formato coherente o incoherente.\n - Presencia de errores (como truncamientos).\n - Plausibilidad de los datos.\n\n3. **Determinantes Específicos de la Pregunta**: Se relacionan con aspectos de la calidad de los datos que dependen de una pregunta o enfoque de análisis específico. Ejemplos incluyen:\n - Aceptabilidad de la completitud de un conjunto de datos.\n - Nivel de aproximación de los datos para responder a una pregunta específica.\n\n### Entidades Clave\n- **Calidad de Datos (DQ)**: Concepto central del marco.\n- **Regulación de Medicamentos de la UE**: Contexto en el que se aplica el marco.\n- **Determinantes**: Clasificación de elementos que afectan la calidad de los datos.\n- **Procesos y Sistemas**: Infraestructura necesaria para asegurar la calidad de los datos.\n\n### Conclusión\nLa calidad de los datos es crucial para la toma de decisiones regulatorias, y el control de los determinantes fundamentales es esencial para garantizar la confianza en los datos. Cuando no se pueden controlar estos determinantes, es necesario gestionar los aspectos intrínsecos de la calidad de los datos, aunque esto tiene limitaciones en cuanto a la adecuación para el propósito deseado.", + "excerpt_keywords": "Keywords: data quality, evidence generation, data lifecycle, regulatory framework, EU medicines", + "_node_type": "TextNode", + "document_id": "c6e74c5e-3b79-47ca-b9a5-4b08d6c3a084", + "doc_id": "c6e74c5e-3b79-47ca-b9a5-4b08d6c3a084", + "ref_doc_id": "c6e74c5e-3b79-47ca-b9a5-4b08d6c3a084" + }, + "eab5a74a-39b7-4c54-9047-2c3e95ad9ff0": { + "page_label": "11", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page11-1.jpg" + ], + "parsed_text_markdown": "# Data Life Cycle\n\n!Data Life Cycle\n\nFigure 3 - A typical data processing workflow in the evidence generation process\n\n## 5.2.1. DQ vs standardisation\n\nFrom the point of view of regulatory decision making, DQ is distinct from data standardisation: data that are not fit for purpose in terms of answering a regulatory question will not become fit when standardised, and non-standardised data can be still used to answer a regulatory question. DQ also applies to individual and non-standard data sources.\n\nThe implementation of systems and processes to assure DQ is largely affected (and in some cases fully determined) by the adoption of standards as well as by data management recommendations (e.g., FAIR data [Findable, Accessible, Interoperable and Reusable]) [16], and the availability of resources such as ontologies and MDM systems that underpin semantic interoperability.\n\nTherefore, recommendations on specific standards and standardisation processes are not included in this Framework, while adoption of standards does drive implementation maturity levels.\n\n## 5.2.2. Primary vs secondary use of data\n\nPrimary data collection is a process of collecting original data (newly collected), directly from the source. It can be gathered from observations, interviews and from biometrics (blood pressure, weight, blood tests, etc.) or surveys (questionnaires). Primary use of data is the use of information for the specific purposes they were collected for, while secondary use of data involves using the data that have initially been gathered for other purposes. See the glossary for an explanation of primary and secondary data.\n\nIn the application of guidelines and metrics, an important distinction arises between primary and secondary use of data. When systems are designed to collect and process data for a specified primary use, the data quality framework for EU medicines regulation EMA/326985/2023.\n\n----\n\n7 It should be noted that data standardization processes may alter the original information and its semantics. As noted later in this document, \"standardization at source\" is preferred to \"a-posteriori\" standardization.\n![page11-1.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page11-1.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resumen del contexto\n\nEl documento aborda el marco de calidad de datos en la regulación de medicamentos de la UE, centrándose en la distinción entre calidad de datos (DQ) y estandarización de datos. Se enfatiza que la DQ es crucial para la toma de decisiones regulatorias y que los datos no necesariamente deben ser estandarizados para ser útiles. Además, se diferencia entre el uso primario y secundario de los datos, destacando la importancia de la recolección de datos originales y su aplicación en contextos específicos. Se menciona que la estandarización de datos puede alterar la información original, y se prefiere la estandarización en la fuente en lugar de una estandarización posterior.\n\n### Preguntas específicas\n\n1. **¿Cuáles son las implicaciones de la distinción entre calidad de datos y estandarización de datos en la toma de decisiones regulatorias?**\n - Esta pregunta busca profundizar en cómo la calidad de los datos puede influir en las decisiones regulatorias, independientemente de si los datos están estandarizados o no.\n\n2. **¿Qué recursos se mencionan como fundamentales para asegurar la interoperabilidad semántica en el marco de calidad de datos?**\n - Esta pregunta se enfoca en identificar los recursos específicos, como ontologías y sistemas de gestión de datos maestros (MDM), que son esenciales para la implementación de la calidad de datos.\n\n3. **¿Por qué se considera preferible la \"estandarización en la fuente\" en comparación con la \"estandarización a posteriori\"?**\n - Esta pregunta busca explorar las razones detrás de la preferencia por la estandarización en la fuente y cómo esto puede afectar la integridad y la calidad de los datos.\n\n### Resumen de nivel superior\n\nEl marco de calidad de datos en la regulación de medicamentos de la UE establece la importancia de la calidad de los datos en la toma de decisiones regulatorias, diferenciando entre la calidad de datos y la estandarización. Se discute la recolección de datos primarios y su uso, así como la necesidad de asegurar la interoperabilidad semántica a través de recursos adecuados. Además, se enfatiza la preferencia por la estandarización en la fuente para mantener la integridad de los datos.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n**Temas Clave:**\n\n1. **Ciclo de Vida de los Datos**: Se describe un ciclo de vida de los datos que abarca varias fases esenciales para asegurar la calidad de los datos (DQ) en el proceso de generación de evidencia. Las fases incluyen:\n - Definición de requisitos de datos\n - Recolección o generación de datos\n - Gestión y procesamiento de datos\n - Publicación de datos\n - Adquisición y agregación de datos\n - Pruebas y aceptación\n - Entrega para consumo\n\n2. **Evaluación de Adecuación**: Para los datos de uso secundario, es crucial realizar una evaluación de \"fit for purpose\" antes de su utilización, asegurando que los datos sean adecuados para el propósito previsto.\n\n3. **Controles de Calidad**: Se enfatiza la importancia de realizar controles de calidad en diferentes etapas del ciclo de vida de los datos, así como la necesidad de re-evaluar la calidad cuando los datos se utilizan para propósitos distintos a los originales.\n\n4. **Determinantes de Calidad de Datos**: Se deben establecer qué determinantes de calidad aplican en cada etapa del proceso y cómo pueden impactar la fiabilidad de los datos.\n\n5. **Retroalimentación Iterativa**: Se menciona que los controles de calidad pueden incluir bucles de retroalimentación iterativa a lo largo del proceso de generación de evidencia.\n\n**Entidades:**\n\n- **Datos**: Información que se recolecta y utiliza para la generación de evidencia.\n- **Proceso de Generación de Evidencia**: Conjunto de etapas que los datos atraviesan para ser utilizados en la toma de decisiones.\n- **Evaluación de \"Fit for Purpose\"**: Proceso de determinar si los datos son adecuados para el uso específico que se les quiere dar.\n- **Controles de Calidad (DQ Checks)**: Verificaciones realizadas en diferentes etapas para asegurar la calidad de los datos.\n- **Fases del Ciclo de Vida de los Datos**: Etapas que incluyen definición, recolección, gestión, publicación, adquisición, prueba y entrega.\n\nEste resumen proporciona una visión general de los aspectos fundamentales relacionados con la calidad de los datos en el contexto de la regulación de medicamentos en la UE, destacando la importancia de un enfoque sistemático en la gestión de datos para la generación de evidencia.", + "excerpt_keywords": "Keywords: data quality, data standardization, primary data collection, secondary data use, semantic interoperability", + "_node_type": "TextNode", + "document_id": "c3eb9669-8434-4516-a308-8b05513393db", + "doc_id": "c3eb9669-8434-4516-a308-8b05513393db", + "ref_doc_id": "c3eb9669-8434-4516-a308-8b05513393db" + }, + "fe9b5494-d7f4-4aa2-82cb-bc9cd897c485": { + "page_label": "12", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "purpose, or when a set of established requirements for secondary use exist, intrinsic and question specific aspects of DQ can already be considered at the time of collection and generation. It is thus possible to design systems and processes that guarantee some quality level required for evidence generation. In fact, much of the analysis decision and process specification can happen at this stage, and downstream analysis focuses on synthesising results and qualifying the level of uncertainty. This is generally not the case for secondary use of data, whether intended or opportunistic, where the quality criteria for usage may not coincide with the ones relevant for the existing purposes of data collection. In these cases, DQ can often only be controlled based on intrinsic determinants.\n\n## 5.2.3. Publication vs data consumption\n\nAlong the data life cycle, data are processed through two different contexts. In one – publication – data are generated or collected, processed (sometimes harmonised), and made available. Examples are the aggregation of multiple sources of data to provide a dataset that is made available for general usage, for instance in a catalogue, or the generation of data by wearables and their “publication” through APIs. In the other context – consumption – data are procured and aggregated to support analysis. One example would be a study where multiple sources are collected, integrated, and harmonised to answer a specific question.\n\nThese two contexts may be overlapping (e.g., when data are collected for a specific primary use) or may be very distinct (e.g., when data are collected and published in a catalogue for a range of possible foreseen or unforeseen usages usually for secondary analysis).\n\nIt is useful to make this distinction as the purpose and the potential for quality assessment change between these two contexts. Even intrinsic aspects of quality for the same dataset may differ. Detailed specification of quality assessment may be developed separately for the publication or consumption contexts, e.g., for a data catalogue, in terms of acceptable minimal quality for generic usages, or for data procurement, in terms of minimal viability for a specific question.\n\n## 5.3. Data and metadata\n\nMetadata are traditionally defined as “data about data” providing context about their purpose and generation (e.g., characterisation of sources, data processing steps, lineage, and data elements definitions). When data consist of numeric or unstructured information (e.g., images), metadata are typically provided as an addition to a dataset. In general, the distinction between data and metadata is not well defined: some information appearing as metadata in one context (e.g., instrument provider for a test) can be considered as data in another (e.g., if assessing measurement bias).\n\nFor regulatory decision making, metadata should in general be considered similarly as data. More precisely, if some change in metadata would require a revision of derived conclusions, then it should be treated as data from the perspective of DQ [17]. In a DQ context, metadata should not be seen as limited to metrics (including DQ metrics) and summary description of datasets, but should extend to characterisation of sources, processes, and data elements definitions.\n\nAll types of metadata are often published in data catalogues, which have the purpose of allowing data to be discoverable and checked for fitness for purpose without revealing the data themselves.\n\n----\n\nVarious metadata catalogues are in development such as DARWIN and ENCePP. An example from Statistics Finland can be found here: https://www.aineistokatalogi.fi/catalog.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que pueden proporcionar respuestas específicas basadas en el contexto proporcionado, junto con un resumen de nivel superior que puede ayudar a formularlas:\n\n### Resumen de nivel superior:\nEl documento discute el marco de calidad de datos en el contexto de la regulación de medicamentos en la UE, centrándose en la importancia de la calidad de los datos (DQ) tanto en la recolección como en el uso secundario. Se distingue entre dos contextos: la publicación de datos, donde se generan y se ponen a disposición, y el consumo de datos, donde se utilizan para análisis específicos. Además, se enfatiza la relevancia de los metadatos, que son esenciales para proporcionar contexto y asegurar la calidad de los datos en la toma de decisiones regulatorias.\n\n### Preguntas:\n1. **¿Cuáles son los principales desafíos en la evaluación de la calidad de los datos en el contexto de su uso secundario, y cómo se pueden abordar?**\n - Esta pregunta se centra en los problemas específicos que surgen cuando los datos se utilizan para propósitos distintos a los originales y cómo se pueden mitigar.\n\n2. **¿Qué criterios específicos se deben considerar al desarrollar especificaciones de calidad para los contextos de publicación y consumo de datos?**\n - Esta pregunta busca detalles sobre los criterios que deben aplicarse en cada contexto para asegurar que los datos sean adecuados para su propósito.\n\n3. **¿Cómo se define y se utiliza el concepto de metadatos en la regulación de medicamentos, y por qué es crucial para la toma de decisiones?**\n - Esta pregunta explora la importancia de los metadatos en el proceso regulatorio y cómo su correcta gestión puede influir en las conclusiones derivadas de los datos. \n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del marco de calidad de datos y su aplicación en la regulación de medicamentos, proporcionando respuestas que probablemente no se encuentren en otros documentos o contextos.", + "prev_section_summary": "### Temas Clave\n\n1. **Calidad de Datos (DQ) vs. Estandarización de Datos**:\n - La calidad de datos es fundamental para la toma de decisiones regulatorias y es independiente de la estandarización. Los datos no estandarizados pueden ser útiles para responder preguntas regulatorias si son adecuados para el propósito.\n\n2. **Implementación de Sistemas para Asegurar DQ**:\n - La implementación de sistemas y procesos para asegurar la calidad de los datos está influenciada por la adopción de estándares y recomendaciones de gestión de datos, como los principios FAIR (Encontrable, Accesible, Interoperable y Reutilizable).\n\n3. **Uso Primario vs. Secundario de Datos**:\n - La recolección de datos primarios implica la obtención de datos originales directamente de la fuente, mientras que el uso secundario se refiere a la utilización de datos recolectados para otros fines. La distinción entre estos usos es importante en la aplicación de directrices y métricas.\n\n4. **Estandarización en la Fuente vs. Estandarización a Posteriori**:\n - Se prefiere la estandarización en la fuente para mantener la integridad de los datos, ya que la estandarización posterior puede alterar la información original y su semántica.\n\n### Entidades\n\n- **Regulación de Medicamentos de la UE**: Contexto en el que se aplica el marco de calidad de datos.\n- **FAIR Data**: Principios que guían la gestión de datos para asegurar su calidad y accesibilidad.\n- **Ontologías y Sistemas de Gestión de Datos Maestros (MDM)**: Recursos mencionados como esenciales para la interoperabilidad semántica.\n- **Datos Primarios**: Datos recolectados directamente de la fuente.\n- **Datos Secundarios**: Datos utilizados para fines diferentes a los que fueron recolectados originalmente.\n\nEste resumen destaca la importancia de la calidad de los datos en la regulación de medicamentos y las consideraciones clave en su manejo y uso.", + "excerpt_keywords": "Keywords: data quality, metadata, publication, data consumption, regulatory decision making", + "_node_type": "TextNode", + "document_id": "3e6ab39b-0a3d-4dd4-a930-85ee35d10f8a", + "doc_id": "3e6ab39b-0a3d-4dd4-a930-85ee35d10f8a", + "ref_doc_id": "3e6ab39b-0a3d-4dd4-a930-85ee35d10f8a" + }, + "320b1c1b-5203-461c-a3fa-1977d8ca3968": { + "page_label": "13", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "## 5.4. Data immutability\n\nData about some measured or observed aspect of reality may change in time, both reflecting the actual change in the observed entities, and the change of the available information at some given time. For instance, the weight of an individual may change, both due to a change of the individual itself, or because of a more accurate reading superseding a previous measurement.\n\nIt is important to distinguish the current data availability (or the current knowledge about reality) from the data as it was available at a given time (a specific record of knowledge about reality). The latest data reflecting what was known at a given time, is “immutable” in that, by definition, cannot change.\n\nFor any regulatory purpose, evidence should be based on data intended as the record of knowledge at a given time. In other words, data used to support regulation should be immutable. This doesn’t imply that evidence can never be updated: any update should be considered as a distinct (albeit) related dataset. This is a foundational concept implied by most frameworks e.g., ALCOA and FAIR [16].\n\nThe consequence of this principle is that data used for decision making should be versioned and unaltered within any given version.\n\n## 5.5. Data vs information\n\nIn its strictest definition, data represent facts or observations that are unprocessed (e.g., as generated by an instrument) while information represents insights originating from such data, once they are understood and processed in their context (e.g., a patient’s response to a treatment as opposed to a set of individual readouts).\n\nThis Framework focuses on evidence generation that can be provided for decision making and as such it goes beyond a distinction between data and information.\n\n## 5.6. Frame of reference (validation vs verification)\n\nSome aspects of DQ can be measured in respect to different references, contained within the same dataset, or existing beyond the scope of the dataset either as a generic reference or external gold standard, or as the actual fact in the real world. For instance, the weight of an individual could be verified for quality based on its capture in the data (e.g., as a missing value), based on knowledge of a natural weight range or verified against a recorded weight in a source document.\n\nIn some frameworks, the assessment of quality within a dataset is referred to as “verification” while the assessment in respect to a source record or external gold standard is referred to as “validation”. We follow these definitions.\n\nNote: this notion of validation should not be confused with validation as a form of coherence checking, see section 6.3.1.\n\n## 5.7. Granularity of data and DQ\n\nFor structured data, DQ can be typically assessed at different levels of granularity:\n\n- **The value level** corresponds to a specific data point (e.g., a weight). This is also referred as row level when the focus is on all values relative to the same entity9.\n\n----\n\n9 The term entity is used to denote the subject of a set of values. In an information record, for instance about a sample, each value in the record would be expressing the measurement of a variable that relates to the same subject or entity. The entity is typically identified in a record via an identifier.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que el contexto proporcionado puede responder, junto con un resumen de nivel superior:\n\n### Resumen de Nivel Superior\nEl documento aborda la importancia de la inmutabilidad de los datos en el contexto de la regulación de medicamentos en la UE. Se enfatiza que los datos deben ser considerados como un registro de conocimiento en un momento específico y no deben ser alterados una vez que se han registrado. Además, se diferencia entre datos e información, y se discuten conceptos de validación y verificación en relación con la calidad de los datos. También se menciona la granularidad de los datos y cómo se puede evaluar la calidad a diferentes niveles.\n\n### Preguntas Específicas\n\n1. **¿Por qué es fundamental que los datos utilizados para la regulación sean inmutables y cómo se define esta inmutabilidad en el contexto de la toma de decisiones?**\n - El documento explica que los datos inmutables son aquellos que representan el conocimiento en un momento específico y no deben ser alterados. Esto es crucial para la regulación, ya que asegura que las decisiones se basen en un registro confiable y verificable del conocimiento en ese momento.\n\n2. **¿Cuál es la diferencia entre validación y verificación en el contexto de la calidad de los datos, y cómo se aplican estos conceptos en la evaluación de datos?**\n - Se establece que la verificación se refiere a la evaluación de la calidad dentro de un conjunto de datos, mientras que la validación implica comparar esos datos con un registro fuente o un estándar externo. Esta distinción es importante para asegurar la integridad y la precisión de los datos utilizados en la toma de decisiones.\n\n3. **¿Cómo se puede evaluar la granularidad de los datos y qué niveles de granularidad se mencionan en el documento?**\n - El documento menciona que la calidad de los datos puede evaluarse a diferentes niveles de granularidad, siendo el nivel de valor el más específico, que se refiere a un punto de datos individual. También se hace referencia al nivel de fila, que se centra en todos los valores relacionados con la misma entidad. Esto permite un análisis más detallado de la calidad de los datos.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Calidad de los Datos (DQ)**:\n - La calidad de los datos es fundamental tanto en la recolección como en el uso secundario de los datos.\n - Se pueden considerar aspectos intrínsecos y específicos de la pregunta en el momento de la recolección para garantizar un nivel de calidad adecuado para la generación de evidencia.\n\n2. **Contextos de Publicación y Consumo de Datos**:\n - **Publicación**: Se refiere a la generación, procesamiento y disponibilidad de datos, como la agregación de múltiples fuentes en un catálogo o la publicación de datos generados por dispositivos portátiles.\n - **Consumo**: Implica la adquisición y agregación de datos para análisis específicos, como en estudios que integran diversas fuentes para responder a preguntas concretas.\n - La distinción entre estos contextos es importante, ya que los criterios de calidad pueden variar según el propósito de uso.\n\n3. **Metadatos**:\n - Definidos como \"datos sobre datos\", los metadatos proporcionan contexto sobre la generación y el propósito de los datos.\n - En la toma de decisiones regulatorias, los metadatos deben ser considerados de manera similar a los datos, especialmente si su modificación puede afectar las conclusiones derivadas.\n - Los metadatos incluyen la caracterización de fuentes, pasos de procesamiento de datos y definiciones de elementos de datos.\n\n4. **Catálogos de Metadatos**:\n - Se están desarrollando varios catálogos de metadatos, como DARWIN y ENCePP, para facilitar la descubribilidad y la verificación de la idoneidad de los datos sin revelar los datos en sí.\n\n### Entidades Clave:\n- **Regulación de Medicamentos en la UE**: Contexto en el que se aplica el marco de calidad de datos.\n- **DARWIN y ENCePP**: Ejemplos de catálogos de metadatos en desarrollo.\n- **Estadísticas de Finlandia**: Referencia a un ejemplo de catálogo de metadatos.\n\nEste resumen destaca la importancia de la calidad de los datos y los metadatos en el contexto de la regulación de medicamentos, así como la necesidad de considerar diferentes contextos de uso para asegurar la idoneidad de los datos en la toma de decisiones.", + "excerpt_keywords": "Keywords: data immutability, data quality, validation, verification, granularity", + "_node_type": "TextNode", + "document_id": "6a5acb36-dc4a-4afc-830a-e12a68a64339", + "doc_id": "6a5acb36-dc4a-4afc-830a-e12a68a64339", + "ref_doc_id": "6a5acb36-dc4a-4afc-830a-e12a68a64339" + }, + "cc7556d0-6a58-4f98-b2d3-e8e99f010c90": { + "page_label": "14", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "- The **variable level** (also referred to as **column level**) covers a data point for a whole set of individuals (e.g., weight as a variable in a clinical study demographics table). Metrics for DQ at the value level are often easily extended to the column level, for instance by converting values to a percentage10.\n\n- The **dataset level** covers an overall set of related observations11. In some contexts, a further distinction can be made, within a dataset, between parts of dataset that are about similar entities. When such distinction is made, such parts are referred to as **table level**, as those parts would normally appear in distinct tables.\n\nThe concept of granularity also applies to unstructured data, but the definition of its levels is generally specific to the data type and hence is not addressed in this general Framework.\n\nThis DQF will focus on the lowest possible level, i.e., for structured data, the value level. However, some metrics may be defined only at a higher level. For example, the plausibility of a single record of a person with a weight of 300 kg may not trigger a metric violation, but if 80% of the records are above 300 kg, it will.\n\n# 6. DQ dimensions and metrics\n\nThe definition of DQ dimensions and metrics rely on the general definition of dimension, metrics, and measures:\n\n- A **dimension** represents one or more related aspects or features of reality (e.g., for a physical object, its extension, or its durability).\n\n- A **metric** represents a way to assess the value of a specific feature (e.g., absolute length measured in meters under some specified circumstances).\n\n- A **measure** represents a single instance of a metric (e.g., 2 meters). More measures can be combined to derive more general metrics (e.g., average length)12.\n\nDQ metrics can be defined as indicators that when applied to a data source, can derive an assessment of one or more quality dimensions. A single quality metric can be used as an indicator for more than one dimension as expressed below in the examples for Coherence.\n\nFor some metrics, acceptance thresholds (e.g., maximum percentage of missing values) can be defined. In general, and for unintended secondary usages, such thresholds can be defined only depending on the question being asked. However, when data are collected for primary use, or when some well-defined secondary uses are targeted, thresholds may be defined (e.g., minimum/maximum) that apply even at the point of data collection. The quality of data is the sum of several features13 of data, ranging from their correspondence to reality to their representation. It is useful to categorize such features in dimensions, which is a set of features whose measure reveals independent aspects of DQ. In other words, different dimensions answer different distinct DQ questions.\n\nSeveral data frameworks propose an organisation of DQ in dimensions that are similar across frameworks, but often inconsistent in the exact definitions. This complicates a coherent assessment of DQ when multiple sources are aggregated.\n\n----\n\n10 This is typically the case for binary or categorical data.\n11 In general, a dataset in support of a specific question will be comprised of homogenous data (e.g., a specific measurements) or of disparate types of data, which are related in that they measure (directly or indirectly) the same entities. In this sense different parts of a datasets are \"linked\" as they will share references to same entities.\n12 Note that measures could be unitless and not necessarily contiguous.\n13 Features here intended as a synonym of \"aspect\" or \"characteristic\".", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que el contexto proporcionado puede responder, junto con un resumen de nivel superior que puede ayudar a formularlas:\n\n### Resumen de Nivel Superior\nEl documento aborda el marco de calidad de datos (DQF) en el contexto de la regulación de medicamentos en la UE. Se centra en diferentes niveles de granularidad de los datos, como el nivel de variable (o columna) y el nivel de conjunto de datos. Además, se discuten las dimensiones y métricas de calidad de datos, definiendo conceptos clave como dimensión, métrica y medida. Se enfatiza la importancia de establecer umbrales de aceptación para las métricas y cómo estos pueden variar según el uso de los datos.\n\n### Preguntas Específicas\n\n1. **¿Cómo se define y aplica el concepto de \"nivel de variable\" en el marco de calidad de datos, y por qué es importante para la evaluación de la calidad de los datos en estudios clínicos?**\n - Esta pregunta se centra en la definición y la relevancia del nivel de variable, que es fundamental para entender cómo se evalúan los datos en contextos específicos como los estudios clínicos.\n\n2. **¿Qué criterios se pueden utilizar para establecer umbrales de aceptación en las métricas de calidad de datos, y cómo varían según el uso primario o secundario de los datos?**\n - Esta pregunta explora los criterios para establecer umbrales de aceptación, lo que es crucial para garantizar que los datos sean adecuados para su propósito previsto.\n\n3. **¿Cuáles son las implicaciones de la inconsistencia en las definiciones de dimensiones de calidad de datos entre diferentes marcos de datos, y cómo afecta esto a la evaluación de la calidad de datos en múltiples fuentes?**\n - Esta pregunta aborda las dificultades que surgen de la falta de coherencia en las definiciones de dimensiones de calidad de datos, lo que es relevante para quienes trabajan con datos de diversas fuentes y buscan una evaluación precisa de la calidad.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Inmutabilidad de los Datos**:\n - Los datos deben ser considerados como un registro de conocimiento en un momento específico y no deben ser alterados una vez registrados. Esto asegura que las decisiones regulatorias se basen en información confiable y verificable.\n - La inmutabilidad se define como la incapacidad de cambiar los datos que reflejan el conocimiento en un momento dado, aunque se pueden crear conjuntos de datos relacionados para actualizaciones.\n\n2. **Diferencia entre Datos e Información**:\n - **Datos**: Hechos u observaciones no procesadas (por ejemplo, mediciones directas).\n - **Información**: Insights derivados de los datos una vez que han sido procesados y contextualizados (por ejemplo, la respuesta de un paciente a un tratamiento).\n\n3. **Validación vs Verificación**:\n - **Verificación**: Evaluación de la calidad dentro de un conjunto de datos.\n - **Validación**: Comparación de los datos con un registro fuente o un estándar externo. Esta distinción es crucial para mantener la integridad y precisión de los datos.\n\n4. **Granularidad de los Datos**:\n - La calidad de los datos puede evaluarse a diferentes niveles de granularidad:\n - **Nivel de Valor**: Se refiere a un punto de datos específico (por ejemplo, un peso).\n - **Nivel de Fila**: Se centra en todos los valores relacionados con la misma entidad.\n\n### Entidades Clave\n- **Datos**: Representan hechos u observaciones.\n- **Información**: Resultados procesados de los datos.\n- **Inmutabilidad**: Concepto que asegura que los datos no cambien una vez registrados.\n- **Verificación**: Evaluación de calidad dentro de un conjunto de datos.\n- **Validación**: Comparación de datos con fuentes externas.\n- **Granularidad**: Niveles de detalle en la evaluación de la calidad de los datos.\n- **Entidad**: Sujeto de un conjunto de valores, identificado en un registro. \n\nEste resumen destaca la importancia de la inmutabilidad y la calidad de los datos en el contexto de la regulación de medicamentos en la UE, así como las definiciones y conceptos clave relacionados.", + "excerpt_keywords": "Keywords: data quality, metrics, dimensions, granularity, EU medicines regulation", + "_node_type": "TextNode", + "document_id": "403d4012-a884-48d5-9e32-6aa1f4df6d5e", + "doc_id": "403d4012-a884-48d5-9e32-6aa1f4df6d5e", + "ref_doc_id": "403d4012-a884-48d5-9e32-6aa1f4df6d5e" + }, + "441a1b64-2f57-49fc-b936-fd5621a5e785": { + "page_label": "15", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page15-1.jpg" + ], + "parsed_text_markdown": "This guideline introduces a set of dimensions (see figure 4) that are relevant from a regulatory point of view, complement them with a precise definition, possible metrics, and examples. The intention is to remove ambiguity and provide a useful reference that can help mapping different conceptualisation of quality from a variety of sources to a common denominator that is useful to frame metrics and maturity models for supporting evidence generation.\n\n## Data Quality Dimensions\n\n| DQ | Are data sufficient? | Are data analysable? | Are data available at the right time? | Are data of the right kind? | Are data faithfully representing what it's meant to be? |\n|----|----------------------|----------------------|--------------------------------------|----------------------------|--------------------------------------------------------|\n| | Extensiveness | Coherence | Timeliness | Relevance | Reliability |\n\nFigure 4 - Dimensions of data quality\n\n### 6.1. Reliability\n\nReliability is defined as the dimension that covers how closely the data reflect what they are directly measuring.\n\nThe Reliability dimension answers the question: to what degree are data accurate or correctly representing an observed reality? When considering the \"fit for purpose\" definition of quality, Reliability covers how correct and true the data are.\n\n#### 6.1.1. Reliability sub-dimensions\n\nGiven this definition, sub-dimensions can be defined:\n\n- **Accuracy** defined as the amount of discrepancy between data and reality. This definition of accuracy encompasses measures of the amount of wrong information in a dataset (data systematically not reflecting reality) with the formal definition of accuracy in measurements (e.g., the distance between the measurements and the real value). For example, the weight of a person could be incorrect due to a data transcription error, or because a person is measured fully clothed, given a systematic excess weight of 1 to 2 kg.\n![page15-1.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page15-1.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resumen del Contexto\n\nEl documento presenta un marco de calidad de datos en el contexto de la regulación de medicamentos en la Unión Europea. Se introducen dimensiones de calidad de datos relevantes desde un punto de vista regulatorio, junto con definiciones precisas, métricas posibles y ejemplos. El objetivo es eliminar la ambigüedad y proporcionar una referencia útil para mapear diferentes conceptualizaciones de calidad a un denominador común. Se destaca la dimensión de \"fiabilidad\", que se refiere a la precisión de los datos en relación con la realidad que representan, y se define la subdimensión de \"exactitud\" como la discrepancia entre los datos y la realidad.\n\n### Preguntas Específicas\n\n1. **¿Cuáles son las métricas específicas que se pueden utilizar para evaluar la \"exactitud\" dentro de la dimensión de fiabilidad en el marco de calidad de datos?**\n - Esta pregunta busca información sobre métricas concretas que no se mencionan explícitamente en el texto, pero que son cruciales para evaluar la exactitud de los datos.\n\n2. **¿Qué ejemplos concretos se pueden proporcionar para ilustrar cómo la falta de fiabilidad en los datos puede afectar la regulación de medicamentos?**\n - Esta pregunta se centra en ejemplos prácticos que podrían no estar documentados en otras fuentes, pero que son relevantes para entender las implicaciones de la calidad de los datos en la regulación.\n\n3. **¿Cómo se pueden aplicar las dimensiones de calidad de datos en la práctica para mejorar la generación de evidencia en la investigación médica?**\n - Esta pregunta busca explorar aplicaciones prácticas y estrategias que no se discuten en el documento, pero que podrían ser útiles para investigadores y reguladores en el campo de la medicina.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Niveles de Granularidad de Datos**:\n - **Nivel de Variable (o Columna)**: Se refiere a un punto de datos que representa a un conjunto de individuos (por ejemplo, el peso en una tabla de demografía de un estudio clínico). Este nivel permite la evaluación de calidad de datos (DQ) a través de métricas que pueden extenderse fácilmente a este nivel.\n - **Nivel de Conjunto de Datos**: Engloba un conjunto de observaciones relacionadas. Dentro de un conjunto de datos, se puede hacer una distinción adicional entre partes que se refieren a entidades similares, denominadas **nivel de tabla**.\n\n2. **Concepto de Granularidad**:\n - La granularidad también se aplica a datos no estructurados, aunque su definición varía según el tipo de datos.\n\n3. **Dimensiones y Métricas de Calidad de Datos (DQ)**:\n - **Dimensión**: Representa aspectos o características relacionadas de la realidad.\n - **Métrica**: Método para evaluar el valor de una característica específica.\n - **Medida**: Instancia única de una métrica. Las medidas pueden combinarse para derivar métricas más generales.\n\n4. **Umbrales de Aceptación**:\n - Se pueden establecer umbrales para algunas métricas (por ejemplo, porcentaje máximo de valores faltantes). Estos umbrales pueden variar según el uso primario o secundario de los datos.\n\n5. **Características de Calidad de Datos**:\n - La calidad de los datos se compone de varias características, que van desde su correspondencia con la realidad hasta su representación. Estas características se agrupan en dimensiones que revelan aspectos independientes de la calidad de los datos.\n\n6. **Inconsistencias en Definiciones**:\n - Existen diferentes marcos de datos que proponen una organización de las dimensiones de calidad de datos, pero a menudo son inconsistentes en sus definiciones, lo que complica la evaluación coherente de la calidad de datos al agregar múltiples fuentes.\n\n### Entidades Clave\n- **Datos Estructurados y No Estructurados**: Tipos de datos a los que se aplica el marco de calidad.\n- **Métricas de Calidad de Datos (DQ)**: Indicadores que permiten evaluar dimensiones de calidad.\n- **Umbrales de Aceptación**: Límites establecidos para determinar la calidad de los datos.\n- **Dimensiones de Calidad de Datos**: Categorías que agrupan características de calidad.\n\nEste resumen proporciona una visión general de los conceptos y entidades clave discutidos en la sección sobre el marco de calidad de datos en la regulación de medicamentos de la UE.", + "excerpt_keywords": "Keywords: data quality, reliability, accuracy, EU medicines regulation, evidence generation", + "_node_type": "TextNode", + "document_id": "13cfd77c-7f8c-4ed5-9275-e9d0232eb1f3", + "doc_id": "13cfd77c-7f8c-4ed5-9275-e9d0232eb1f3", + "ref_doc_id": "13cfd77c-7f8c-4ed5-9275-e9d0232eb1f3" + }, + "fbce9d28-6cb8-4c83-b014-9b66160a9c1d": { + "page_label": "16", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "- **Precision** defined as the degree of approximation by which data represents reality. For instance, the age of a person could be reported in years or months.\n\n### 6.1.1.1. Other DQ concepts related to Reliability\n\nStrictly related to Reliability is the concept of **Plausibility**, defined as the likelihood of some information being true. Plausibility can be a proxy to detect errors: when some combination of information is unlikely (or impossible) to happen in the Real-World, this reveals accuracy issues. For example, a weight of a person exceeding 300 kg is possible, but the weight of many or all persons in a dataset exceeding that value is implausible (unless the foundational determinants indicate otherwise) and likely revealing some errors in the measurement or the processing of the data. Plausibility results from the comparison of a data item to typical or necessary characteristics of the entity it intends to represent and is therefore hard to measure as a pure intrinsic characteristic as it depends on the availability of background knowledge or an external gold standard.\n\n**Traceability** (also referred to as **data lineage or provenance**) refers to data presenting the knowledge of how data came to be, what source it originated from, and what processing it went through before appearing in its current form. Traceability is a feature of data that falls within Reliability in that it connects what is measured with the actual data.\n\n### 6.1.2. Considerations for Reliability\n\nReliability fundamentally depends on the systems and process in place for the primary collection of data and its processing and curation in further phases of the evidence generation process both for primary and secondary use cases.\n\nIn the absence of errors, accuracy would not decrease along the data aggregation process. Precision may instead decrease when data are harmonised to a Common Data Model (CDM), as this may call for less precise representation than original sources to fit the model.\n\nIntrinsic aspects of Reliability are hard to measure in a pure data-oriented framework, however Plausibility measures can provide a way to detect some classes of errors. Reliability is independent from a specific question, though each question, in relation to data, will set a threshold for acceptable Reliability.\n\n----\n\n14 This definition of precision encompasses the notion of \"reproducibility of values\" under repeated measurements, in that it captures how \"coarse\" is the correspondence between data and the characteristic it intends to measure.\n\n15 \"Other concepts\" present relevant aspects of DQ that falls within a dimension or that are in common use, but that don’t strictly adhere to definition of the dimension provided.\n\n16 An example could be a CDM allowing timestamp in seconds where a source may use milliseconds, or a CDM prescribing some terminology, which is less specialised in some areas than the one used in the source.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resúmenes de nivel superior del contexto\n\n1. **Definición de Precisión y su Relación con la Realidad**: La precisión se refiere al grado de aproximación de los datos a la realidad, y se ilustra con ejemplos como la representación de la edad de una persona en años o meses.\n\n2. **Conceptos Relacionados con la Fiabilidad**: Se introducen conceptos como la plausibilidad y la trazabilidad, que son fundamentales para evaluar la fiabilidad de los datos. La plausibilidad ayuda a detectar errores al comparar datos con características típicas de la entidad representada, mientras que la trazabilidad proporciona información sobre el origen y el procesamiento de los datos.\n\n3. **Consideraciones sobre la Fiabilidad**: La fiabilidad depende de los sistemas y procesos utilizados para la recolección y procesamiento de datos. La precisión puede disminuir al armonizar datos a un Modelo de Datos Común (CDM), y la fiabilidad se mide en función de los umbrales establecidos por las preguntas específicas relacionadas con los datos.\n\n### Preguntas específicas que el contexto puede responder\n\n1. **¿Cómo se puede utilizar la plausibilidad como herramienta para detectar errores en un conjunto de datos?**\n - La plausibilidad se utiliza como un proxy para detectar errores al evaluar la probabilidad de que ciertas combinaciones de datos sean verdaderas en el mundo real. Si un conjunto de datos presenta características que son altamente improbables o imposibles, esto puede indicar problemas de precisión o errores en la medición.\n\n2. **¿Qué papel juega la trazabilidad en la evaluación de la fiabilidad de los datos?**\n - La trazabilidad es crucial para la fiabilidad de los datos, ya que permite rastrear el origen de los datos, cómo fueron recolectados y qué procesamiento sufrieron antes de llegar a su forma actual. Esto ayuda a conectar lo que se mide con los datos reales, lo que es esencial para evaluar la calidad y la fiabilidad de la información.\n\n3. **¿Cuáles son las implicaciones de la armonización de datos a un Modelo de Datos Común (CDM) en términos de precisión?**\n - La armonización de datos a un CDM puede llevar a una disminución de la precisión, ya que puede requerir representaciones menos precisas que las fuentes originales para ajustarse al modelo. Esto implica que, aunque la fiabilidad puede mantenerse, la precisión de los datos puede verse comprometida en el proceso de adaptación.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Marco de Calidad de Datos**: El documento presenta un marco que aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se busca proporcionar definiciones claras, métricas y ejemplos para facilitar la comprensión y aplicación de la calidad de datos.\n\n2. **Dimensiones de Calidad de Datos**: Se introducen varias dimensiones relevantes desde un punto de vista regulatorio, que incluyen:\n - **Suficiencia**: ¿Son suficientes los datos?\n - **Analizabilidad**: ¿Son analizables los datos?\n - **Disponibilidad**: ¿Están disponibles los datos en el momento adecuado?\n - **Tipo**: ¿Son los datos del tipo correcto?\n - **Fiabilidad**: ¿Representan los datos fielmente lo que se pretende medir?\n\n3. **Fiabilidad**: Esta dimensión se centra en la precisión de los datos en relación con la realidad que representan. Se plantea la pregunta sobre el grado en que los datos son precisos y verdaderos.\n\n4. **Subdimensiones de Fiabilidad**:\n - **Exactitud**: Se define como la discrepancia entre los datos y la realidad. Incluye medidas de información incorrecta en un conjunto de datos y la precisión en las mediciones.\n\n5. **Ejemplo de Exactitud**: Se menciona un ejemplo práctico donde el peso de una persona puede ser incorrecto debido a errores de transcripción de datos o a mediciones realizadas con ropa, lo que introduce un exceso sistemático en el peso registrado.\n\n### Entidades Clave\n- **Regulación de Medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Dimensiones de Calidad de Datos**: Suficiencia, analizabilidad, disponibilidad, tipo y fiabilidad.\n- **Fiabilidad y Exactitud**: Conceptos centrales en la evaluación de la calidad de datos.\n\nEste resumen destaca la importancia de la calidad de los datos en la regulación de medicamentos y cómo las dimensiones y subdimensiones propuestas pueden ayudar a evaluar y mejorar la fiabilidad de los datos utilizados en este ámbito.", + "excerpt_keywords": "Keywords: Precision, Reliability, Plausibility, Traceability, Data Quality", + "_node_type": "TextNode", + "document_id": "2eccfa36-0810-454e-9fff-170b108ba0f3", + "doc_id": "2eccfa36-0810-454e-9fff-170b108ba0f3", + "ref_doc_id": "2eccfa36-0810-454e-9fff-170b108ba0f3" + }, + "efcdb7c4-5b15-4934-a496-00021a684eb0": { + "page_label": "17", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# 6.1.3. Examples of reliability metrics\n\n**Table 1 – Example of metrics for Reliability**\n\n| Sub-dimension | Metric group | Abstract metric | Reference | Example |\n| ----------------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ------------ | ---------------------------------------------------------------------------------------------------------------------------------------- |\n| Plausibility (proxy for Accuracy)17 | Atemporal Plausibility | Data values and distributions agree with internal measurements or local knowledge | Validation | Height and weight are a positive value. Counts of unique subjects by treatment are as expected (respect to an applicable gold standard). |\n| | | Data values and distributions for independent measurements of the same fact agree | Verification | Oral and axillary temperatures are similar. Serum glucose measurement is similar to finger stick glucose measurement. |\n| | | Logical constraints between values agree with common knowledge | Verification | The patient's sex agrees with sex-specific contexts (pregnancy, prostate cancer). |\n| | | Values of repeated measurement of the same fact show expected variability | Verification | Weight values are similar when taken by separate nurses within the same facility using the same equipment. |\n| | | Data values and distributions agree with trusted reference standards | Validation | HbA1c values from hospital and national reference lab are statistically match under the same conditions. |\n| | | Equivalent values for identical measurements are obtained from two independent databases representing the same observations with equal credibility | Validation | Date of birth value in the EHR is not identical to that in the registry record of the same patient. |\n\n----\n\n17 Our examples are limited to Accuracy as this is the most common application of Plausibility. In theory, Plausibility could extend to other dimensions of Reliability (e.g., a weight expressed in grams is likely to be imprecise if all values end with three zeros).", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de Nivel Superior\nEl documento presenta un marco de calidad de datos en el contexto de la regulación de medicamentos en la UE. En la sección 6.1.3, se discuten ejemplos de métricas de fiabilidad, centrándose en la plausibilidad como un proxy para la precisión. Se presentan diversas métricas que evalúan la concordancia de los datos con mediciones internas, estándares de referencia y la lógica común, así como ejemplos prácticos que ilustran cada métrica.\n\n### Preguntas Específicas\n\n1. **¿Qué tipo de datos se consideran en la métrica de plausibilidad y cómo se valida su precisión?**\n - La métrica de plausibilidad se centra en datos que deben concordar con mediciones internas o conocimientos locales. Se valida su precisión mediante la comparación de valores y distribuciones de datos con estándares de referencia y mediciones independientes.\n\n2. **¿Cuáles son algunos ejemplos de cómo se puede verificar la lógica de los datos en el contexto de la salud?**\n - Un ejemplo de verificación lógica es que el sexo del paciente debe concordar con contextos específicos, como el embarazo o el cáncer de próstata. Esto asegura que los datos sean coherentes con el conocimiento común sobre las características demográficas.\n\n3. **¿Qué implicaciones tiene la discrepancia en los valores de nacimiento entre diferentes bases de datos para la calidad de los datos en registros de salud?**\n - La discrepancia en los valores de nacimiento entre el historial médico electrónico (EHR) y el registro de pacientes puede indicar problemas en la calidad de los datos, lo que sugiere la necesidad de mejorar la precisión y la concordancia entre diferentes fuentes de datos para garantizar la fiabilidad de la información en el ámbito de la salud.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Precisión**: Se define como el grado de aproximación de los datos a la realidad. Un ejemplo es la representación de la edad de una persona, que puede ser reportada en años o meses.\n\n2. **Fiabilidad**: Concepto fundamental que se relaciona con la precisión y la calidad de los datos. Incluye aspectos como la **plausibilidad** y la **trazabilidad**.\n\n - **Plausibilidad**: Se refiere a la probabilidad de que cierta información sea verdadera. Se utiliza como un proxy para detectar errores al evaluar combinaciones de datos que son improbables o imposibles en el mundo real. Por ejemplo, un peso de más de 300 kg es posible para una persona, pero es implausible que todos los individuos en un conjunto de datos superen ese peso.\n\n - **Trazabilidad**: También conocida como **linaje de datos** o **proveniencia**, se refiere a la capacidad de rastrear el origen de los datos, cómo fueron recolectados y qué procesamiento sufrieron antes de llegar a su forma actual. Esto es esencial para evaluar la fiabilidad de los datos.\n\n3. **Consideraciones sobre la Fiabilidad**: La fiabilidad depende de los sistemas y procesos utilizados para la recolección y procesamiento de datos. La precisión puede disminuir al armonizar datos a un Modelo de Datos Común (CDM), lo que puede requerir representaciones menos precisas que las fuentes originales.\n\n4. **Errores y Medición**: La medición de aspectos intrínsecos de la fiabilidad es compleja en un marco puramente orientado a datos. Sin embargo, las medidas de plausibilidad pueden ayudar a detectar ciertos tipos de errores. La fiabilidad es independiente de preguntas específicas, aunque cada pregunta establecerá un umbral para la fiabilidad aceptable.\n\n### Entidades Clave\n- **Precisión**\n- **Fiabilidad**\n- **Plausibilidad**\n- **Trazabilidad**\n- **Modelo de Datos Común (CDM)**\n\nEste resumen destaca los conceptos fundamentales relacionados con la calidad de los datos en el contexto de la regulación de medicamentos en la UE, enfatizando la importancia de la precisión, la fiabilidad y las herramientas para evaluar la calidad de los datos.", + "excerpt_keywords": "Keywords: data quality, reliability metrics, plausibility, accuracy, EU medicines regulation", + "_node_type": "TextNode", + "document_id": "fe40da9e-b564-4d0b-b09d-59a4d9e2aa72", + "doc_id": "fe40da9e-b564-4d0b-b09d-59a4d9e2aa72", + "ref_doc_id": "fe40da9e-b564-4d0b-b09d-59a4d9e2aa72" + }, + "1d76c0f7-ca7f-4500-9cd5-4efbcd4c8445": { + "page_label": "18", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Sub-dimension | Metric group | Abstract metric | Reference | Example |\n| --------------------- | ------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- |\n| | | Two or more dependent databases yield similar values for identical variables (e.g., database 1 abstracted from database 2) | Validation | Cancer stage value in the EHR does not corresponds with a NAACCR code in the tumour registry of the same patient. |\n| | | Calculated data values agree with common knowledge | Validation | Height and weight of a patient resulting in an implausible BMI value of less than 5 propose an inaccurate height, weight, or both. |\n| Temporal Plausibility | Observed or derived values conform to expected temporal properties | Verification | Discharge date happens after admission date. | |\n| | Sequence of values that represent state transitions conform to expected properties | Verification | Date of primary vaccine administration precedes that of the booster vaccine administration. | |\n| | Observed or derived values have similar temporal properties across one or more external comparators (gold standard) | Validation | Length of stay for outpatient procedure conforms to insurance data for similar populations (no more than 1 day). | |\n| | Sequences of values that represent state transitions are similar to external comparators (gold standards) | Validation | Immunisation sequences matches that of the EMA recommendations. | |\n| | Measures of data value density against a time-oriented denominator are expected based on external knowledge | Validation | Count of immunisation per month shows an expected spike outside of flu season. | |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento presenta un marco de calidad de datos en el contexto de la regulación de medicamentos en la Unión Europea. Se centra en la validez y verificación de datos a través de métricas específicas que evalúan la plausibilidad temporal y la consistencia de los datos en diferentes bases. Se incluyen ejemplos concretos que ilustran cómo se pueden aplicar estas métricas para asegurar la calidad de los datos en registros médicos y otros contextos relacionados con la salud.\n\n### Preguntas específicas:\n1. **¿Qué métricas se utilizan para validar la consistencia de los datos entre bases de datos dependientes en el contexto de la regulación de medicamentos?**\n - Respuesta: Se utiliza la métrica que evalúa si dos o más bases de datos dependientes producen valores similares para variables idénticas, como se ejemplifica con el valor del estadio del cáncer en el EHR que no corresponde con un código NAACCR en el registro tumoral del mismo paciente.\n\n2. **¿Cómo se verifica la plausibilidad temporal de los datos en registros médicos según el marco de calidad de datos?**\n - Respuesta: La plausibilidad temporal se verifica mediante la observación de que las fechas de alta ocurren después de las fechas de admisión, y que las secuencias de vacunación cumplen con las propiedades esperadas, como que la fecha de administración de la vacuna primaria precede a la de la vacuna de refuerzo.\n\n3. **¿Qué ejemplos se proporcionan para ilustrar la validación de la densidad de valores de datos en relación con un denominador orientado al tiempo?**\n - Respuesta: Un ejemplo es el conteo de inmunizaciones por mes, que muestra un aumento esperado fuera de la temporada de gripe, lo que indica que los datos son consistentes con el conocimiento externo sobre patrones de vacunación.\n\nEstas preguntas y respuestas están diseñadas para extraer información específica del contexto que puede no estar disponible en otras fuentes.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nLa sección 6.1.3 del documento \"Data quality framework EU medicines regulation\" se centra en ejemplos de métricas de fiabilidad, específicamente en la plausibilidad como un indicador de precisión en los datos. A continuación se presentan los temas clave y las entidades relevantes:\n\n#### Temas Clave:\n1. **Métricas de Fiabilidad**: Se presentan diversas métricas que evalúan la fiabilidad de los datos en el contexto de la regulación de medicamentos.\n2. **Plausibilidad**: Se utiliza como un proxy para la precisión, evaluando la concordancia de los datos con mediciones internas, estándares de referencia y lógica común.\n3. **Validación y Verificación**: Se diferencian dos procesos: la validación (comprobación contra estándares de referencia) y la verificación (comprobación entre mediciones independientes).\n4. **Ejemplos Prácticos**: Se proporcionan ejemplos específicos que ilustran cómo se aplican estas métricas en el ámbito de la salud, como la concordancia de temperaturas corporales y la lógica en los datos demográficos.\n\n#### Entidades Relevantes:\n- **Datos de Salud**: Incluyen mediciones como altura, peso, temperatura corporal y niveles de glucosa.\n- **Estándares de Referencia**: Se mencionan laboratorios de referencia y condiciones bajo las cuales se comparan los datos.\n- **Contextos Específicos**: Ejemplos como el embarazo y el cáncer de próstata se utilizan para ilustrar la lógica en la verificación de datos demográficos.\n- **Historial Médico Electrónico (EHR)**: Se menciona en el contexto de la discrepancia de datos, como la fecha de nacimiento, entre diferentes bases de datos.\n\nEste resumen destaca la importancia de la calidad de los datos en la regulación de medicamentos y cómo las métricas de fiabilidad pueden ayudar a asegurar la precisión y la coherencia de la información en el ámbito de la salud.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, validation, temporal plausibility, health data consistency", + "_node_type": "TextNode", + "document_id": "5772e068-1899-496c-939a-f3d3d858448d", + "doc_id": "5772e068-1899-496c-939a-f3d3d858448d", + "ref_doc_id": "5772e068-1899-496c-939a-f3d3d858448d" + }, + "52a0e03e-fefb-452c-86d1-5f03f92ea71f": { + "page_label": "19", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "In case a gold standard is not available, a metric can be “verified” based on a comparison with a similar metric from another source. This could be equivalent values for identical measurements from two independent databases. This is not necessarily a “gold standard” but may be the best available option. Alternative methods might be explored when no “gold standard” is available (see 8.6 for more details).\n\n## 6.2. Extensiveness\n\nExtensiveness is defined as the dimension capturing the amount of data available.\n\nThe Extensiveness dimension answers the question, “how much data do we have”? When considering the “fit for purpose” definition of quality, Extensiveness covers how sufficient the data are.\n\n### 6.2.1. Sub-dimensions of Extensiveness\n\nWhen considering the amount of information available, one can think of expressing this as a percentage relative to the total amount of information that could be available. The distinction between Completeness and Coverage stems from the definition of the scope of totally available information.\n\n- **Completeness** measures the amount of information available with respect to the total information that could be available given the capture process and data format. Data unavailable in the dataset (either due to systematic reasons such as information available in the data source but not included in the data model, or specific entries that are unavailable for a given field) are called “missing”. For example, the percentage of non-missing values for a required field (e.g., sex) in a dataset would be a measure for completeness.\n\n- **Coverage** measures the amount of information available with respect to what exists in the Real-World, whether it is inside the capture process and data format or not. Coverage may not be easily measured as the total information may not be definable or accessible. An example of coverage is the percentage of a given population (e.g., a country or a specific demographics) available in a dataset. When considering coverage in its relation to evidence generation methods, it is also referred to as observability.\n\n#### 6.2.1.1. Other DQ concepts related to Extensiveness\n\nTwo concepts that are often associated to extensiveness are representativeness and missingness. While these concepts describe to a certain extent how much data is available, they are more importantly used to characterise how much data is reflecting reality. **Representativeness** is defined as the data having the same characteristics as the whole it is meant to represent (e.g., whether a set of individuals present in a dataset is representative of a population under study). **Missingness** is meant as the characterisation of what is the impact of incomplete data in respect to coverage of a dataset.\n\n----\n\nExtensiveness combines two typical dimensions found in DQFs: Completeness of data coverage and Coverage. They are here combined as they both relate to the amount of data available.\n\nThere is a fundamental distinction between missing data that are known to exist (e.g., the date of birth of a patient), or missing data whose existence is unknown (e.g., the presence of co-pathologies). Quite often in a data model the definition of a variable as “required” implies that the relative values are known to exist, and therefore when such data is missing it is a “missing known”. In general, in the absence of explicit negation, it may not be possible to distinguish “missing known” from “missing unknowns”. When some data point is expected to be captured, the inability to distinguish “missing knowns” from “missing unknowns” is an issue of Reliability (one is unable to assess if data corresponds to the reality it is meant to represent).", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que pueden proporcionar respuestas específicas basadas en el contexto proporcionado, así como un resumen de nivel superior que puede ayudar a formular mejores preguntas:\n\n### Resumen de Nivel Superior\nEl documento aborda el marco de calidad de datos en el contexto de la regulación de medicamentos en la UE, centrándose en la dimensión de \"Extensividad\". Esta dimensión se refiere a la cantidad de datos disponibles y se desglosa en sub-dimensiones como \"Completitud\" y \"Cobertura\". Se discuten conceptos relacionados como \"Representatividad\" y \"Missingness\", que ayudan a caracterizar la calidad de los datos en relación con la realidad que representan. Además, se menciona la importancia de distinguir entre datos faltantes conocidos y desconocidos, lo que afecta la fiabilidad de los datos.\n\n### Preguntas Específicas\n1. **¿Cómo se define y mide la \"Completitud\" en el contexto de la calidad de datos, y qué ejemplos se pueden proporcionar para ilustrar su aplicación en bases de datos de medicamentos?**\n - Esta pregunta busca una explicación detallada sobre cómo se evalúa la completitud de los datos y ejemplos concretos que no se encuentran fácilmente en otras fuentes.\n\n2. **¿Qué métodos alternativos se pueden utilizar para evaluar la \"Cobertura\" de los datos cuando no se puede acceder a información total o definible sobre la población real?**\n - Esta pregunta se centra en las estrategias que se pueden implementar para medir la cobertura en situaciones donde la información completa no está disponible, lo que puede no estar ampliamente documentado.\n\n3. **¿Cuáles son las implicaciones prácticas de no poder distinguir entre \"missing knowns\" y \"missing unknowns\" en un modelo de datos, y cómo afecta esto a la fiabilidad de los datos en la investigación médica?**\n - Esta pregunta busca profundizar en las consecuencias de la falta de claridad sobre los datos faltantes y su impacto en la calidad de la investigación, un aspecto que puede no ser tratado en otros documentos.", + "prev_section_summary": "### Resumen de temas clave y entidades\n\nEl contenido de la sección se centra en un marco de calidad de datos aplicado a la regulación de medicamentos en la Unión Europea. A continuación se presentan los temas clave y entidades relevantes:\n\n1. **Métricas de Calidad de Datos**:\n - Se definen métricas específicas para evaluar la calidad de los datos en registros médicos y otros contextos relacionados con la salud.\n - Se enfatiza la importancia de la **validación** y **verificación** de datos.\n\n2. **Sub-dimensiones**:\n - **Consistencia de Datos**: Evaluación de si dos o más bases de datos dependientes producen valores similares para variables idénticas.\n - **Plausibilidad Temporal**: Verificación de que los valores observados o derivados cumplen con propiedades temporales esperadas.\n\n3. **Ejemplos de Métricas**:\n - Comparación de valores de estadio del cáncer entre registros médicos y registros tumorales.\n - Verificación de la plausibilidad de datos como la relación entre fechas de admisión y alta.\n - Evaluación de secuencias de vacunación y su conformidad con estándares externos.\n\n4. **Validación de Datos**:\n - Se utilizan comparadores externos (gold standards) para validar la consistencia y plausibilidad de los datos.\n - Ejemplos incluyen la longitud de estancia en procedimientos ambulatorios y patrones de inmunización.\n\n5. **Densidad de Valores de Datos**:\n - Se mide la densidad de valores de datos en relación con un denominador temporal, como el conteo de inmunizaciones por mes.\n\n### Entidades Clave:\n- **EHR (Electronic Health Record)**: Registros médicos electrónicos que contienen información sobre la salud de los pacientes.\n- **NAACCR (North American Association of Central Cancer Registries)**: Organización que establece estándares para el registro de cáncer.\n- **EMA (European Medicines Agency)**: Agencia responsable de la evaluación y supervisión de medicamentos en la Unión Europea.\n\nEste marco busca asegurar la calidad de los datos utilizados en la regulación de medicamentos, lo que es crucial para la seguridad y efectividad de los tratamientos en el ámbito de la salud pública.", + "excerpt_keywords": "Keywords: data quality, extensiveness, completeness, coverage, representativeness", + "_node_type": "TextNode", + "document_id": "5f01ed27-125b-456e-982f-da2729fee2f6", + "doc_id": "5f01ed27-125b-456e-982f-da2729fee2f6", + "ref_doc_id": "5f01ed27-125b-456e-982f-da2729fee2f6" + }, + "c3cfbcdd-2047-45c7-b9ce-c489f0af9514": { + "page_label": "20", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Sub-dimension | Metric group | Abstract rule | Reference | Example |\n| ------------- | ----------------------------------------------------------------------------------- | ----------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |\n| Completeness | Missing required values | Missing values with respect to a local schema – over time | Verification | Breed or sex of the animal should not be NULL. |\n| | | Missing values with respect to a local schema – single time | Verification | The encounter ID variable has missing values. |\n| | Estimated missing values | Missing values with respect to common expectations | Verification | Sudden drop of diagnosis codes due to a defective feed from a claim clearing house vendor. |\n| | Relative assessment of missing values with respect to a trusted source of knowledge | Validation | The current encounter ID variable is missing twice as many values as the institutionally validated database. A drop in ICD-9CM codes upon implementation of ICD-10-CM. | |\n| Coverage | Coverage of a population | Verification | The percentage of a target population present in a database. | |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento titulado \"Data quality framework EU medicines regulation\" presenta un marco para evaluar la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se centra en dos sub-dimensiones principales: \"Completitud\" y \"Cobertura\". Dentro de la completitud, se abordan métricas relacionadas con valores faltantes, tanto en el tiempo como en un solo momento, así como la estimación de valores faltantes y la evaluación relativa de estos en comparación con fuentes de conocimiento confiables. La cobertura se refiere a la proporción de una población objetivo presente en una base de datos.\n\n### Preguntas específicas:\n1. **¿Qué métricas se utilizan para evaluar la completitud de los datos en el marco de calidad de datos de la regulación de medicamentos de la UE?**\n - Respuesta: Las métricas incluyen \"Valores requeridos faltantes\", \"Valores faltantes con respecto a un esquema local en el tiempo y en un solo momento\", \"Valores faltantes estimados\" y \"Evaluación relativa de valores faltantes en comparación con una fuente de conocimiento confiable\".\n\n2. **¿Cuál es un ejemplo de un problema de completitud relacionado con la implementación de un nuevo sistema de codificación médica?**\n - Respuesta: Un ejemplo es la \"caída repentina de códigos de diagnóstico debido a un alimento defectuoso de un proveedor de compensación\", lo que indica que los datos pueden no estar completos debido a problemas en la fuente de datos.\n\n3. **¿Cómo se define la cobertura de una población en el contexto de la calidad de los datos según el documento?**\n - Respuesta: La cobertura de una población se define como el \"porcentaje de una población objetivo presente en una base de datos\", lo que implica que se evalúa cuántos individuos de un grupo específico están representados en los datos recopilados.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Extensividad**: Se define como la dimensión que captura la cantidad de datos disponibles. Responde a la pregunta de \"¿cuánto dato tenemos?\" y se relaciona con la suficiencia de los datos en el contexto de su propósito.\n\n2. **Sub-dimensiones de Extensividad**:\n - **Completitud**: Mide la cantidad de información disponible en relación con la información total que podría estar disponible, considerando el proceso de captura y el formato de los datos. Se refiere a los datos que faltan en un conjunto de datos.\n - **Cobertura**: Mide la cantidad de información disponible en relación con lo que existe en el mundo real, independientemente de si está dentro del proceso de captura y formato de datos. Puede ser más difícil de medir debido a la falta de definición o accesibilidad de la información total.\n\n3. **Conceptos Relacionados**:\n - **Representatividad**: Se refiere a si los datos tienen las mismas características que el conjunto que se pretende representar.\n - **Missingness**: Describe el impacto de los datos incompletos en la cobertura de un conjunto de datos.\n\n4. **Diferenciación de Datos Faltantes**:\n - **Missing Known**: Datos que se sabe que deberían existir pero están ausentes (ejemplo: fecha de nacimiento de un paciente).\n - **Missing Unknown**: Datos cuya existencia es desconocida (ejemplo: presencia de co-patologías).\n\n5. **Implicaciones de la Fiabilidad**: La incapacidad de distinguir entre \"missing knowns\" y \"missing unknowns\" afecta la fiabilidad de los datos, ya que dificulta la evaluación de si los datos reflejan la realidad que se pretende representar.\n\n### Entidades Clave\n- **Extensividad**\n- **Completitud**\n- **Cobertura**\n- **Representatividad**\n- **Missingness**\n- **Datos Faltantes** (Missing Known y Missing Unknown)\n\nEste resumen destaca los conceptos fundamentales relacionados con la calidad de los datos en el contexto de la regulación de medicamentos en la UE, enfatizando la importancia de la cantidad y la calidad de los datos disponibles.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, completeness, coverage, missing values", + "_node_type": "TextNode", + "document_id": "75ef5133-d87b-4675-86d1-dab82e105836", + "doc_id": "75ef5133-d87b-4675-86d1-dab82e105836", + "ref_doc_id": "75ef5133-d87b-4675-86d1-dab82e105836" + }, + "ed471f7f-e47e-4b3e-87f5-9d0f39338baa": { + "page_label": "21", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# 6.3.1. Sub-dimensions of Coherence\n\nCoherence is a complex and nuanced dimension that includes the following sub-dimensions:\n\n- **Format Coherence**: whether data are expressed in the same way throughout a dataset (e.g., a dataset mixing dates represented as DD-MM-YYYY and MM-DD-YYYY will not be suitable for an integrated analysis).\n\n- **Structural or Relational Coherence**: whether the same entities are identified in the same way throughout a dataset. A sub-aspect of Structural Coherence is that references are resolved to the correct entities (e.g., a sample annotation table with refer to the correct value in a result table).\n\n- **Semantic Coherence**: whether the same value mean the same thing throughout a dataset. For instance, whether “anuria” means a condition of total cessation of urine production or the measurement of the amount of urine, or whether the same notion of a measure is intended to have the same precision throughout a dataset.\n\n- **Uniqueness**: Uniqueness is the property that the same information is not duplicated but appears in the dataset once. This problem is typical for data aggregated from different sources. Note that data with some redundancy will score lower in the Uniqueness dimension, but those extra records could help improving other dimensions, such as Reliability.\n\n- Other DQ concepts related to Coherence is **Conformance** when this is defined with respect to a specific reference or data model. Conformance may practically be the best way to assess Coherence, and it also specialised as format, Structural and Semantic Conformance. As an example, conformance would assess if the representation of data is coherent by assessing if it is the same as an overall target standard (e.g.: DD-MM-YYYY). **Validity** is a narrower case of Conformance that is defined when the reference model is specific to the dataset being assessed. As an example, if a file is associated to a schema specifying that all dates in the D.O.B. field should be in DD-MM-YYYY format, the file could be directly assessed as valid or not.\n\n# 6.3.2. Considerations for Coherence\n\nCoherence of data at source largely depends on foundational determinants such as the synchronisation of processes and systems across an organisation generating data, or when multiple data are aggregated on the commitment of such organisation(s) to the use of internal or external data standards. By extension, Coherence for data aggregated and repurposed for secondary usage depends on the availability of shared standards and reference data. The intrinsic aspects of Coherence of a dataset can be improved, largely within a data standardisation processing step. However, improving Coherence involves approximating or clarifying the meaning of data. Access to the source system and\n\n----\n\n**Footnotes:**\n\n21. Structural and relational Coherence as synonyms here. It may be the case that these two concepts are distinct or non-tabular data. This distinction will be addressed if the need arises, in extensions of this framework to specific data types.\n\n22. It is worth noting that “information” is distinct from data. Two distinct measurements resulting in the same data would not constitute duplicate information (and such measurements would most likely differ in value, when metadata is included). Whereas the same measurement reported two times would amount to a duplication.\n\n23. A file could be coherent, but not conformant, if all values are coherent (e.g.: MM-DD-YYYY) while an overall target standard proposed to assess conformance requires DD-MM-YYYY.\n\n24. As noted in 5.4, this is a different meaning (in common use) then what is defined for “Validation”.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que pueden ser respondidas por el contexto proporcionado, junto con un resumen de nivel superior del contenido:\n\n### Resumen de Nivel Superior\nEl documento aborda la dimensión de la coherencia en la calidad de los datos, desglosándola en varias sub-dimensiones: Coherencia de Formato, Coherencia Estructural o Relacional, Coherencia Semántica y Unicidad. Cada una de estas sub-dimensiones se centra en diferentes aspectos de cómo los datos deben ser consistentes y no duplicados para ser considerados de alta calidad. Además, se discuten conceptos relacionados como la Conformidad y la Validez, así como las consideraciones que afectan la coherencia de los datos en su origen y en su uso secundario.\n\n### Preguntas Específicas\n1. **¿Cuáles son las implicaciones de la falta de coherencia semántica en un conjunto de datos clínicos?**\n - Esta pregunta se centra en cómo la interpretación inconsistente de términos médicos puede afectar la calidad de los datos y, por ende, la toma de decisiones en el ámbito de la salud.\n\n2. **¿Cómo se puede mejorar la unicidad de los datos al agregarlos de múltiples fuentes?**\n - Esta pregunta busca explorar estrategias específicas para evitar la duplicación de información al combinar datos de diferentes orígenes, lo que es un desafío común en la gestión de datos.\n\n3. **¿Qué papel juegan los estándares internos y externos en la coherencia de los datos en una organización?**\n - Esta pregunta investiga cómo la adopción de estándares puede influir en la calidad de los datos y en la capacidad de una organización para mantener la coherencia en sus conjuntos de datos.\n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del contexto que pueden no ser ampliamente discutidos en otras fuentes, proporcionando así un enfoque más detallado sobre la coherencia de los datos en el marco de la regulación de medicamentos en la UE.", + "prev_section_summary": "### Resumen de temas clave y entidades\n\nEl documento \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea, centrándose en dos sub-dimensiones principales: **Completitud** y **Cobertura**.\n\n#### Temas clave:\n\n1. **Completitud**:\n - Se refiere a la presencia de todos los valores requeridos en los datos.\n - Incluye métricas como:\n - **Valores requeridos faltantes**: Evaluación de valores que deberían estar presentes según un esquema local.\n - **Valores faltantes en el tiempo y en un solo momento**: Análisis de datos faltantes en diferentes contextos temporales.\n - **Valores faltantes estimados**: Identificación de valores que se espera que estén presentes pero no lo están.\n - **Evaluación relativa de valores faltantes**: Comparación de datos faltantes con una fuente de conocimiento confiable.\n\n2. **Cobertura**:\n - Se refiere al porcentaje de una población objetivo que está representada en una base de datos.\n - Es un indicador de cuán completa es la representación de un grupo específico en los datos recopilados.\n\n#### Entidades:\n\n- **Regulación de medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Métricas de calidad de datos**: Herramientas utilizadas para evaluar la completitud y cobertura de los datos.\n- **Fuentes de conocimiento confiables**: Referencias utilizadas para validar la calidad de los datos.\n\nEste marco es fundamental para asegurar que los datos utilizados en la regulación de medicamentos sean precisos y representativos, lo que es crucial para la toma de decisiones informadas en el ámbito de la salud pública.", + "excerpt_keywords": "Keywords: coherence, data quality, conformance, uniqueness, semantic coherence", + "_node_type": "TextNode", + "document_id": "9309e6f8-82da-4fd4-87e7-d4e705455788", + "doc_id": "9309e6f8-82da-4fd4-87e7-d4e705455788", + "ref_doc_id": "9309e6f8-82da-4fd4-87e7-d4e705455788" + }, + "c1ecad7d-e019-4b54-8882-c72f059df86e": { + "page_label": "22", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "processes is often required for clarifications as an example. Some aspects of semantic Coherence may be difficult to assess with a metric and hence can only be assessed with respect to a specific question and analysis strategy.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que podrían derivarse del contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento titulado \"Data quality framework EU medicines regulation\" aborda la importancia de la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se menciona que ciertos aspectos de la coherencia semántica son difíciles de evaluar mediante métricas estándar y requieren un enfoque más específico, dependiendo de la pregunta y la estrategia de análisis utilizada.\n\n### Preguntas:\n1. **¿Cuáles son los desafíos específicos asociados con la evaluación de la coherencia semántica en los datos relacionados con la regulación de medicamentos en la UE?**\n - Esta pregunta busca profundizar en los problemas concretos que se presentan al intentar medir la coherencia semántica, que no se abordan de manera general en otros documentos.\n\n2. **¿Qué tipo de preguntas y estrategias de análisis son más efectivas para evaluar la calidad de los datos en el contexto de la regulación de medicamentos?**\n - Esta pregunta se centra en identificar enfoques específicos que pueden ser útiles para abordar la evaluación de la calidad de los datos, lo cual podría no estar claramente delineado en otras fuentes.\n\n3. **¿Cómo se pueden aplicar los principios de la coherencia semántica en la práctica para mejorar la calidad de los datos en la regulación de medicamentos?**\n - Esta pregunta busca explorar aplicaciones prácticas de los conceptos discutidos en el documento, lo que podría ofrecer insights que no se encuentran fácilmente en otros lugares.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nLa sección aborda la **coherencia** como una dimensión fundamental en la calidad de los datos, desglosándola en varias **sub-dimensiones**:\n\n1. **Coherencia de Formato**: Se refiere a la uniformidad en la representación de los datos dentro de un conjunto, como el formato de fechas (DD-MM-YYYY vs. MM-DD-YYYY).\n\n2. **Coherencia Estructural o Relacional**: Implica que las mismas entidades sean identificadas de manera consistente en todo el conjunto de datos, asegurando que las referencias se resuelvan correctamente.\n\n3. **Coherencia Semántica**: Se centra en que los mismos valores tengan el mismo significado a lo largo del conjunto de datos, evitando ambigüedades en la interpretación de términos.\n\n4. **Unicidad**: Se refiere a que la misma información no esté duplicada en el conjunto de datos, un desafío común al agregar datos de múltiples fuentes.\n\n5. **Conformidad**: Relacionada con la adherencia a un modelo de referencia específico, que puede ser evaluada en términos de formato, estructura y semántica.\n\n6. **Validez**: Un caso más específico de conformidad, donde se evalúa si los datos cumplen con un esquema particular.\n\n### Consideraciones para la Coherencia\n\n- La coherencia de los datos en su origen depende de la sincronización de procesos y sistemas dentro de una organización, así como del compromiso con estándares de datos internos o externos.\n- La disponibilidad de estándares compartidos y datos de referencia es crucial para la coherencia de datos que se agregan y reutilizan para usos secundarios.\n- La mejora de la coherencia puede lograrse a través de pasos de estandarización de datos, que implican aclarar o aproximar el significado de los datos.\n\n### Entidades Clave\n\n- **Datos**: Información que se recopila y utiliza para análisis.\n- **Conformidad**: Evaluación de la adherencia a estándares de datos.\n- **Validez**: Evaluación de la precisión de los datos según un modelo específico.\n- **Estándares de Datos**: Normas que guían la representación y uso de datos en una organización.\n\nEste resumen destaca la importancia de la coherencia en la calidad de los datos, así como los desafíos y estrategias para mantenerla en el contexto de la regulación de medicamentos en la UE.", + "excerpt_keywords": "Keywords: calidad de datos, coherencia semántica, regulación de medicamentos, evaluación de datos, estándares de datos", + "_node_type": "TextNode", + "document_id": "f64cdc4c-f486-462a-bf67-becc7fa55502", + "doc_id": "f64cdc4c-f486-462a-bf67-becc7fa55502", + "ref_doc_id": "f64cdc4c-f486-462a-bf67-becc7fa55502" + }, + "9dfcaf73-60f2-498b-964b-0e2bca0b55d0": { + "page_label": "23", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# 6.3.3. Examples of metrics for Coherence\n\n**Table 3 – Example of metrics for coherence**\n\n| Sub-dimension | Metric group | Abstract rule | Reference | Example |\n| ---------------------------------- | ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------- |\n| Format coherence (conformance) | Syntactic constraints | Data Values conform to internal formatting constraints | Verification | Sex is only one ASCII character. |\n| | Allowed values | Data values conform to allowable values or ranges | Verification | Sex for the animal only has values “M”, “F”. or “U”. |\n| | | Data values conform to the representational constraints based on external standards | Validation | Values for primary language conform to ISO standards. |\n| Relational coherence (conformance) | Reference Coherence | Data values conform to relational constraints | Verification | Patient medical record number links to other tables as expected. |\n| | | Unique (key) data values are not duplicated | Verification | A medical record number is assigned to a single patient. |\n| | Data values conform to relational constraints based on external standards | Validation | Data values conform to all not-NULL requirements in a common multi-institutional data exchange format. | |\n| Schema Coherence | Changes to the data model or data model versioning | Verification | Version 1 data does not include medical discharge hour. | |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de Nivel Superior\nEl documento \"Data quality framework EU medicines regulation\" aborda la importancia de la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. En la sección 6.3.3, se presentan ejemplos de métricas para evaluar la coherencia de los datos, que se dividen en sub-dimensiones como la coherencia de formato, la coherencia relacional y la coherencia del esquema. Cada sub-dimensión incluye grupos de métricas, reglas abstractas, referencias y ejemplos específicos que ilustran cómo se puede verificar la conformidad de los datos.\n\n### Preguntas Específicas\n1. **¿Qué tipo de restricciones se utilizan para evaluar la coherencia de formato en los datos?**\n - La coherencia de formato se evalúa mediante restricciones sintácticas, que aseguran que los valores de los datos se ajusten a los límites de formato interno, y mediante la conformidad a valores permitidos y a estándares externos de representación.\n\n2. **¿Cómo se verifica la coherencia relacional de los datos en el contexto de registros médicos?**\n - La coherencia relacional se verifica asegurando que los valores de los datos cumplan con las restricciones relacionales, que no haya duplicados en los valores clave y que se cumplan los requisitos de no nulos en formatos de intercambio de datos multi-institucionales.\n\n3. **¿Qué implicaciones tiene el cambio en el modelo de datos para la coherencia del esquema?**\n - Los cambios en el modelo de datos o en la versión del modelo pueden afectar la coherencia del esquema, lo que requiere verificación para asegurar que los datos de versiones anteriores no incluyan elementos que se han añadido o modificado en versiones más recientes, como la inclusión de la hora de alta médica en la versión 1.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Calidad de los datos**: El documento enfatiza la importancia de mantener altos estándares de calidad en los datos utilizados en la regulación de medicamentos en la Unión Europea.\n\n2. **Coherencia semántica**: Se discute la dificultad de evaluar la coherencia semántica de los datos mediante métricas estándar, lo que sugiere que se necesita un enfoque más específico y contextual.\n\n3. **Evaluación contextual**: La evaluación de la coherencia semántica debe realizarse en relación con preguntas específicas y estrategias de análisis, lo que implica que no hay un enfoque único aplicable a todas las situaciones.\n\n4. **Regulación de medicamentos en la UE**: El contexto del documento se centra en la regulación de medicamentos, lo que implica la necesidad de datos precisos y coherentes para garantizar la seguridad y eficacia de los productos farmacéuticos.\n\n### Entidades clave:\n- **Unión Europea (UE)**: Contexto geográfico y regulatorio del documento.\n- **Medicamentos**: Tema central del documento, relacionado con la regulación y la calidad de los datos.\n- **Coherencia semántica**: Concepto clave que se aborda en relación con la calidad de los datos.\n\nEste resumen destaca los aspectos fundamentales del contenido, así como las entidades relevantes que se discuten en la sección proporcionada.", + "excerpt_keywords": "Keywords: data quality, coherence metrics, EU medicines regulation, relational constraints, schema verification", + "_node_type": "TextNode", + "document_id": "313c46f3-067b-44a2-b546-3e127b654ab4", + "doc_id": "313c46f3-067b-44a2-b546-3e127b654ab4", + "ref_doc_id": "313c46f3-067b-44a2-b546-3e127b654ab4" + }, + "3e39a065-7b92-4921-a15c-661ec2996d92": { + "page_label": "24", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Sub-dimension | Metric group | Abstract rule | Reference | Example |\n| -------------------------------- | --------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |\n| Computation al Coherence | Computed values conform to programming specifications | Verification | Database calculated and hand calculated BMI (body mass index) values are identical. | |\n| | Computed results based on published algorithms yield values that match validation values provided by external sources | Validation | Computed BMI percentiles yield identical values compared to test results and values provided by EMA. | |\n| Semantic coherence (conformance) | Precision Coherence | The precision of values is fitting a target standard | Verification | E.g., two decimal digits are used and generally not zero. |\n| | Semantic Coherence | Use of code lists is consistent across data | Verification | E.g., the level of a MedDRA coding for an indication doesn’t vary across the dataset. |\n| Uniqueness | Same subject is represented with the same identity | Verification | William Smith is also represented as Bill Smith with the same DOB. | |\n| | Same subject is represented with multiple identities | Verification | William Smith and William Smith appear as separate individuals instead of the same individual. | |\n| | The data records of individuals are matched using unique keys | Validation | William Smith’s DOB ID matches with Bill Smith’s DOB and ID. | |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento \"Data quality framework EU medicines regulation\" presenta un marco para evaluar la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se centra en varias dimensiones de calidad de los datos, incluyendo la coherencia computacional, la coherencia semántica y la unicidad. Cada dimensión se desglosa en grupos métricos, reglas abstractas, referencias y ejemplos que ilustran cómo se puede verificar y validar la calidad de los datos.\n\n### Preguntas:\n1. **¿Qué ejemplos se proporcionan para ilustrar la coherencia computacional en los datos?**\n - El documento menciona que los valores calculados de BMI (índice de masa corporal) deben ser idénticos tanto en cálculos de base de datos como en cálculos manuales, y que los percentiles de BMI calculados deben coincidir con los resultados de pruebas y valores proporcionados por la EMA.\n\n2. **¿Cómo se define la unicidad en el contexto de la calidad de los datos y qué ejemplos se dan?**\n - La unicidad se define como la representación del mismo sujeto con la misma identidad. Se proporcionan ejemplos como el caso de William Smith, quien puede ser representado como Bill Smith con la misma fecha de nacimiento (DOB), y la situación en la que William Smith y William Smith aparecen como individuos separados en lugar de ser considerados la misma persona.\n\n3. **¿Qué se entiende por coherencia semántica y qué métricas se utilizan para verificarla?**\n - La coherencia semántica se refiere a la consistencia en el uso de listas de códigos a través de los datos. Se verifica mediante métricas como la precisión de los valores, que deben ajustarse a un estándar objetivo, y el uso consistente de códigos, como el nivel de codificación MedDRA para una indicación que no debe variar en el conjunto de datos.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nLa sección 6.3.3 del documento \"Data quality framework EU medicines regulation\" se centra en la evaluación de la coherencia de los datos mediante métricas específicas. Se identifican tres sub-dimensiones principales de coherencia:\n\n1. **Coherencia de Formato**: \n - **Métricas**: Incluye restricciones sintácticas y valores permitidos.\n - **Reglas Abstractas**: Los valores de los datos deben ajustarse a las restricciones de formato interno y a los estándares externos de representación.\n - **Ejemplos**: \n - El sexo se representa con un solo carácter ASCII.\n - Los valores permitidos para el sexo de un animal son “M”, “F” o “U”.\n\n2. **Coherencia Relacional**: \n - **Métricas**: Se enfoca en la coherencia de referencia y la unicidad de los valores clave.\n - **Reglas Abstractas**: Los valores de los datos deben cumplir con las restricciones relacionales y no deben estar duplicados.\n - **Ejemplos**: \n - El número de registro médico del paciente debe vincularse a otras tablas como se espera.\n - Un número de registro médico debe asignarse a un solo paciente.\n\n3. **Coherencia del Esquema**: \n - **Métricas**: Relacionadas con los cambios en el modelo de datos o la versión del modelo.\n - **Reglas Abstractas**: Se requiere verificación para asegurar que los datos de versiones anteriores no incluyan elementos que se han modificado o añadido en versiones más recientes.\n - **Ejemplo**: La versión 1 de los datos no incluye la hora de alta médica.\n\n### Entidades Clave\n- **Datos**: Información que se evalúa en términos de coherencia.\n- **Métricas**: Herramientas utilizadas para medir la coherencia de los datos.\n- **Reglas Abstractas**: Principios que guían la verificación de la coherencia.\n- **Ejemplos**: Casos específicos que ilustran cómo se aplica cada métrica.\n\nEste marco de calidad de datos es fundamental para garantizar la integridad y la fiabilidad de la información en el contexto de la regulación de medicamentos en la Unión Europea.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, coherence, validation, uniqueness", + "_node_type": "TextNode", + "document_id": "58c77a39-9276-44f8-a313-8bccc6abd9e4", + "doc_id": "58c77a39-9276-44f8-a313-8bccc6abd9e4", + "ref_doc_id": "58c77a39-9276-44f8-a313-8bccc6abd9e4" + }, + "a17d49ee-cfbe-4a6d-b70e-3acf58055b62": { + "page_label": "25", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Sub-dimension | Metric group | Abstract rule | Reference |\n| ------------- | ------------ | -------------------------------------------------------- | ------------ |\n| Currency | N/A | The average time of updates in a database (or timestamp) | Verification |\n| Currency | N/A | The last update of a database (or timestamp) | Verification |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento titulado \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se centra en métricas relacionadas con la \"moneda\" de los datos, específicamente en la frecuencia y el momento de las actualizaciones en las bases de datos. Se mencionan dos reglas abstractas que se utilizan para verificar la calidad de los datos: el tiempo promedio de actualizaciones y la última actualización registrada.\n\n### Preguntas:\n1. **¿Cuáles son las dos métricas específicas relacionadas con la \"moneda\" de los datos que se mencionan en el documento?**\n - Respuesta: Las dos métricas son el tiempo promedio de actualizaciones en una base de datos y la última actualización de la base de datos.\n\n2. **¿Qué se entiende por \"verificación\" en el contexto de las reglas abstractas mencionadas en el documento?**\n - Respuesta: En este contexto, \"verificación\" se refiere al proceso de comprobar la calidad de los datos mediante la evaluación de su frecuencia de actualización y la recencia de la última actualización.\n\n3. **¿Por qué es importante la \"moneda\" de los datos en la regulación de medicamentos en la UE?**\n - Respuesta: La \"moneda\" de los datos es crucial en la regulación de medicamentos porque garantiza que la información utilizada para la toma de decisiones sea actual y relevante, lo que es fundamental para la seguridad y eficacia de los medicamentos en el mercado.", + "prev_section_summary": "### Resumen de temas clave y entidades\n\nEl documento \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se enfoca en tres dimensiones principales de calidad de los datos:\n\n1. **Coherencia Computacional**:\n - **Definición**: Se refiere a la conformidad de los valores calculados con las especificaciones de programación.\n - **Métricas**: Incluye la verificación de que los valores calculados en bases de datos coincidan con los cálculos manuales y que los resultados basados en algoritmos publicados coincidan con valores de validación de fuentes externas.\n - **Ejemplos**: Comparación de valores de BMI calculados y percentiles de BMI con resultados de pruebas y valores de la EMA.\n\n2. **Coherencia Semántica**:\n - **Definición**: Se refiere a la consistencia en el uso de listas de códigos y la precisión de los valores.\n - **Métricas**: Incluye la verificación de que los valores se ajusten a un estándar objetivo y que el uso de códigos sea consistente a través de los datos.\n - **Ejemplos**: Uso de dos dígitos decimales y consistencia en la codificación MedDRA para indicaciones.\n\n3. **Unicidad**:\n - **Definición**: Se refiere a la representación de un mismo sujeto con una única identidad.\n - **Métricas**: Incluye la verificación de que un sujeto no sea representado con múltiples identidades y que los registros de datos de individuos se correspondan mediante claves únicas.\n - **Ejemplos**: Casos de William Smith representado como Bill Smith con la misma fecha de nacimiento y la aparición de William Smith como individuos separados.\n\n### Entidades Clave:\n- **BMI (Índice de Masa Corporal)**: Utilizado como ejemplo para ilustrar la coherencia computacional.\n- **EMA (Agencia Europea de Medicamentos)**: Proporciona valores de validación para comparación.\n- **MedDRA**: Sistema de codificación utilizado para asegurar la coherencia semántica en los datos.\n\nEste marco proporciona una guía para verificar y validar la calidad de los datos en el ámbito de la regulación de medicamentos, asegurando que los datos sean precisos, consistentes y únicos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, currency, verification, metrics", + "_node_type": "TextNode", + "document_id": "85d25dfa-7427-4ca8-a260-39cd5dc0bc6e", + "doc_id": "85d25dfa-7427-4ca8-a260-39cd5dc0bc6e", + "ref_doc_id": "85d25dfa-7427-4ca8-a260-39cd5dc0bc6e" + }, + "bc29d397-549e-41f4-a210-794410e8d30e": { + "page_label": "26", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Sub-dimension | Metric group | Abstract rule | Reference | Example |\n| ------------- | ------------ | --------------------------------------------------------------------------------------------------- | ------------ | ------- |\n| N/A | N/A | The number of variables (columns) available in a given dataset vs the number of required variables. | Verification | N/A |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, así como un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento titulado \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se centra en la importancia de contar con un conjunto adecuado de variables en los conjuntos de datos utilizados para la verificación y evaluación de la calidad de los datos. Un aspecto clave mencionado es la comparación entre el número de variables disponibles en un conjunto de datos y el número de variables requeridas.\n\n### Preguntas específicas:\n1. **¿Cuál es la importancia de comparar el número de variables disponibles con el número de variables requeridas en un conjunto de datos en el contexto de la regulación de medicamentos?**\n - Esta pregunta busca profundizar en la relevancia de esta comparación para garantizar la calidad y la integridad de los datos en la evaluación de medicamentos.\n\n2. **¿Qué implicaciones tiene la falta de variables requeridas en un conjunto de datos para la verificación de la calidad de los datos en la regulación de medicamentos?**\n - Esta pregunta se enfoca en las consecuencias que pueden surgir si un conjunto de datos no cumple con los requisitos de variables necesarias, lo que podría afectar la toma de decisiones regulatorias.\n\n3. **¿Cómo se define la \"verificación\" en el contexto del marco de calidad de datos mencionado en el documento?**\n - Esta pregunta busca aclarar el concepto de verificación y su aplicación específica dentro del marco de calidad de datos para la regulación de medicamentos, lo que puede no estar explícitamente definido en otros documentos.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\nEl documento \"Data quality framework EU medicines regulation\" se centra en la calidad de los datos en el ámbito de la regulación de medicamentos en la Unión Europea. Los temas clave incluyen:\n\n1. **Moneda de los datos**: Se refiere a la actualidad y relevancia de la información en las bases de datos.\n \n2. **Métricas de actualización**:\n - **Tiempo promedio de actualizaciones**: Indica con qué frecuencia se actualizan los datos en una base de datos.\n - **Última actualización**: Refleja el momento en que se realizó la última modificación en la base de datos.\n\n3. **Verificación**: Es el proceso de evaluar la calidad de los datos mediante la revisión de las métricas de moneda mencionadas.\n\n### Entidades:\n- **Regulación de medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Unión Europea**: Jurisdicción relevante para la regulación de medicamentos y la calidad de los datos asociados.\n\nEste marco es esencial para asegurar que la información utilizada en la toma de decisiones sobre medicamentos sea precisa y actual, lo que impacta directamente en la seguridad y eficacia de los productos farmacéuticos en el mercado.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, verification, variables, dataset", + "_node_type": "TextNode", + "document_id": "7b457d0a-e684-4ded-b7c0-0c119b3dae99", + "doc_id": "7b457d0a-e684-4ded-b7c0-0c119b3dae99", + "ref_doc_id": "7b457d0a-e684-4ded-b7c0-0c119b3dae99" + }, + "b1d2d5e0-23b9-4406-bbe0-b4e3db14d564": { + "page_label": "27", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "guidance for what metrics and actions apply at which stage of the data life cycle. For example, the dimension of Extensiveness is determined exclusively by Foundational determinants at production time. Further in the data life cycle, data intrinsic measures can only partially assess the degree of Reliability (plausibility metrics).\n\nThese tables also form the basis for the development of maturity models for the characterisation of DQ for regulatory purposes. The maturity models provide guidance as to how determinants can be characterised in successive levels of maturity. Higher maturity levels support the strongest possible evidence in the most efficient way.\n\nThree distinct maturity models are provided, corresponding to the three determinants, to depict how maturity evolves with respect to process characterisation, intrinsic aspects (metrics) and the definition of target questions. These models are meant to apply to the different steps and actions that compose an overall evidence-generation framework.\n\nIt should be noted that the maturity models provided are abstract in the sense that they provide the classes or recommendations that need to be complemented with implementation detail for specific data types and use cases.\n\nIt takes time to characterise and implement processes to achieve higher maturity levels both for data source holders, but also for regulatory assessors to understand the impact of a higher maturity model. This is also context dependent e.g., disease area, disease frequency, health system etc. The DQF will be updated in the upcoming years with further deep dives in regulatory use cases of particular interest to guide clinical assessment for medicines regulation.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que pueden proporcionar respuestas específicas basadas en el contexto proporcionado, junto con resúmenes de nivel superior que pueden ayudar a formularlas:\n\n### Resumen de nivel superior:\nEl documento describe un marco de calidad de datos (DQF) para la regulación de medicamentos en la UE, que incluye métricas y acciones aplicables en diferentes etapas del ciclo de vida de los datos. Se presentan modelos de madurez que ayudan a caracterizar la calidad de los datos (DQ) para fines regulatorios, destacando la importancia de la caracterización de procesos y la evaluación de aspectos intrínsecos de los datos. Además, se menciona que la implementación de estos modelos es un proceso que requiere tiempo y es dependiente del contexto.\n\n### Preguntas:\n\n1. **¿Cuáles son los tres determinantes que se utilizan en los modelos de madurez para caracterizar la calidad de los datos en el contexto de la regulación de medicamentos?**\n - Esta pregunta busca identificar los determinantes específicos que se mencionan en el documento y cómo se relacionan con la madurez de los datos.\n\n2. **¿Qué papel juegan los modelos de madurez en la generación de evidencia para la evaluación clínica de medicamentos, y cómo se espera que evolucionen en los próximos años?**\n - Esta pregunta se centra en la función de los modelos de madurez en el proceso de evaluación y en las expectativas futuras sobre su desarrollo y aplicación.\n\n3. **¿Cómo afecta el contexto, como el área de enfermedad o la frecuencia de la enfermedad, a la implementación de los modelos de madurez en la evaluación de la calidad de los datos?**\n - Esta pregunta explora la relación entre el contexto específico y la aplicación de los modelos de madurez, lo que puede proporcionar información sobre la adaptabilidad del marco a diferentes situaciones. \n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del marco de calidad de datos y su aplicación en la regulación de medicamentos, que pueden no estar ampliamente disponibles en otras fuentes.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Calidad de los datos**: El documento se centra en la importancia de la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea.\n\n2. **Variables en conjuntos de datos**: Se destaca la necesidad de comparar el número de variables disponibles en un conjunto de datos con el número de variables requeridas. Esta comparación es crucial para asegurar que los datos sean adecuados para la verificación y evaluación.\n\n3. **Verificación**: Se menciona el concepto de verificación, aunque no se define explícitamente en el extracto. Se relaciona con la evaluación de la calidad de los datos en el marco regulatorio.\n\n4. **Implicaciones regulatorias**: La falta de variables requeridas en un conjunto de datos puede tener consecuencias significativas en la toma de decisiones regulatorias, afectando la integridad y la validez de los procesos de evaluación de medicamentos.\n\n### Entidades:\n- **Regulación de medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Unión Europea**: Jurisdicción relevante para la regulación mencionada.\n- **Conjuntos de datos**: Elementos clave en la evaluación de la calidad de los datos y su adecuación para la verificación.", + "excerpt_keywords": "Keywords: data quality, maturity models, regulatory assessment, evidence generation, EU medicines regulation", + "_node_type": "TextNode", + "document_id": "c52e57c9-16e3-40a8-905f-35161af83740", + "doc_id": "c52e57c9-16e3-40a8-905f-35161af83740", + "ref_doc_id": "c52e57c9-16e3-40a8-905f-35161af83740" + }, + "7453e08f-5215-46a3-8b99-3865640b4737": { + "page_label": "28", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Table 6 – Characterisation of the effect of determinants on data quality dimensions\n\n| Determinant / Dimension | Reliability | Extensiveness | Coherence | Timeliness | Relevance |\n| ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |\n| Foundational | | Primary Data collected following established protocols can be sufficient to address regulatory questions. | | | Primary Normally guaranteed by the design of the data collection process. |\n| | Primary and secondary Data reliability (in all its aspects) results from systems and processes in place for data generation or collection. Reliability is affected by data processing and transformations at later stages e.g., standardisation to a CDM. | Primary and secondary The data collection protocol determines what data are collected. | Primary and secondary Dependent on the orchestration of processes originating data and on the commitment to internal or external data standards. | Primary and secondary Solely determined by systems and processes. | |\n| Secondary | Secondary Precision may decrease during data transformation and harmonisation processes. | Secondary There is no guarantee on the completeness of an integrated dataset or its coverage for a different use case, and this can only be assessed or controlled. | Secondary Relies on shared standards and reference data. Documentation on data generation processes may be needed to enhance coherence. | | Secondary Normally assessed for a specific use or a class of usages when datasets are selected. |\n| Intrinsic | Primary and secondary Plausibility measures can be used to detect a (limited) class of reliability issues. | Primary and secondary Completeness measures based on a data model are easy to implement. | Primary and secondary Coherence can be measured exclusively based on data (with eventual access to | Primary and secondary Some aspects of timeliness may be observed in the datasets (e.g., event | Primary and secondary Relevance of data is not dependent on a dataset itself. |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resumen del Contexto\n\nEl contexto presenta una tabla que caracteriza el efecto de diferentes determinantes sobre las dimensiones de calidad de los datos en el marco de la regulación de medicamentos de la UE. Se analizan cinco dimensiones de calidad de datos: fiabilidad, extensividad, coherencia, puntualidad y relevancia. Cada dimensión se evalúa en función de diferentes determinantes (fundacionales, secundarios e intrínsecos), destacando cómo estos determinantes afectan la calidad de los datos en términos de protocolos de recolección, procesos de transformación, estándares compartidos y medidas de plausibilidad.\n\n### Preguntas Específicas\n\n1. **¿Cómo afectan los protocolos de recolección de datos a la fiabilidad de los datos en el contexto de la regulación de medicamentos de la UE?**\n - Esta pregunta se centra en la relación entre los protocolos establecidos y la fiabilidad de los datos, un aspecto que se menciona específicamente en la tabla.\n\n2. **¿Qué medidas se pueden implementar para mejorar la coherencia de los datos en un conjunto integrado, según el marco de calidad de datos presentado?**\n - Esta pregunta busca explorar las estrategias que pueden ser utilizadas para asegurar la coherencia de los datos, un tema que se aborda en la tabla a través de la necesidad de estándares compartidos y documentación.\n\n3. **¿Cuáles son las implicaciones de la disminución de la precisión durante los procesos de transformación y armonización de datos en la evaluación de medicamentos?**\n - Esta pregunta se enfoca en las consecuencias de la pérdida de precisión, un aspecto mencionado en la tabla, y cómo esto puede afectar la evaluación y regulación de medicamentos.\n\n### Resumen de Nivel Superior\n\nEl marco de calidad de datos en la regulación de medicamentos de la UE establece que la calidad de los datos es fundamental para la toma de decisiones regulatorias. Las dimensiones de calidad, como la fiabilidad y la coherencia, son influenciadas por determinantes como los protocolos de recolección y los procesos de transformación de datos. La comprensión de estos factores es crucial para garantizar que los datos utilizados en la evaluación de medicamentos sean precisos, completos y relevantes.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Marco de Calidad de Datos (DQF)**: El documento presenta un marco para la regulación de medicamentos en la UE, que establece métricas y acciones aplicables en diferentes etapas del ciclo de vida de los datos.\n\n2. **Determinantes de la Calidad de Datos**: Se menciona la dimensión de Extensividad, que se determina por factores fundamentales en el momento de producción, y la medida de fiabilidad, que se evalúa parcialmente mediante métricas intrínsecas.\n\n3. **Modelos de Madurez**: Se desarrollan tres modelos de madurez que corresponden a los determinantes de la calidad de los datos. Estos modelos ayudan a caracterizar la calidad de los datos en niveles sucesivos de madurez, facilitando la generación de evidencia para la evaluación clínica de medicamentos.\n\n4. **Implementación Contextual**: La implementación de los modelos de madurez es un proceso que requiere tiempo y es dependiente del contexto, como el área de enfermedad, la frecuencia de la enfermedad y el sistema de salud.\n\n5. **Actualizaciones Futuras**: Se anticipa que el DQF será actualizado en los próximos años, con un enfoque en casos de uso regulatorios específicos que guiarán la evaluación clínica en la regulación de medicamentos.\n\n### Entidades Clave\n\n- **Regulación de Medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Métricas de Calidad de Datos**: Herramientas utilizadas para evaluar la calidad de los datos en diferentes etapas.\n- **Modelos de Madurez**: Estructuras que describen cómo evoluciona la madurez de la calidad de los datos.\n- **Determinantes**: Factores que influyen en la calidad de los datos, incluyendo aspectos fundamentales y métricas intrínsecas.\n- **Ciclo de Vida de los Datos**: Proceso que abarca desde la producción hasta la evaluación de los datos.\n\nEste resumen proporciona una visión general de los conceptos y entidades clave abordados en la sección, destacando la importancia del marco de calidad de datos en la regulación de medicamentos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, reliability, coherence, data collection protocols", + "_node_type": "TextNode", + "document_id": "bac80c83-c280-44fc-a1f0-13473a2f852f", + "doc_id": "bac80c83-c280-44fc-a1f0-13473a2f852f", + "ref_doc_id": "bac80c83-c280-44fc-a1f0-13473a2f852f" + }, + "b0f60a5a-0d99-4583-95e5-7a32e1dc0059": { + "page_label": "29", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Determinant / Dimension | Reliability | Extensiveness | Coherence | Timeliness | Relevance |\n| ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------------------------------------------------- | --------- |\n| Direct measures of accuracy require access to the source of data. | | datasets-independent reference data). | dates to determine currency). A dataset itself cannot in general reveal how current its information is. | | |\n| | Secondary Coverage measures are more complex and may require confrontation to a golden standard. | Secondary Coherence can be largely improved based solely on a dataset and data-independent elements (e.g., mapping to a common standard). A full resolution of coherence may require access to additional information on processes. Coherence needs to be assessed every time a new data source is “integrated”. | | | |\n| Question specific | Primary Processes and systems to collect data are usually designed to answer a specific question and to meet the required targets, across DQ dimensions, that such target entails. | | | | |\n| Secondary Threshold for acceptable reliability can be defined only respect to a specific question and method. | Secondary Coverage and completeness depend on a question: metrics can be defined only respect to a specific question and method, or for a domain. For completeness, typically a question would determine a set of acceptance thresholds and general metrics. | Secondary Some assessment of semantic coherence (data distribution coherence or abstraction coherence) may only be measured respect to a specific question and method. | Secondary Acceptable timeliness depends on the question and its broader regulatory usage (e.g., approval vs monitoring). | Secondary Relevance can only be determined in relation to one or more questions. | |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden formular a partir del contexto proporcionado, junto con un resumen de nivel superior que puede ayudar a generar preguntas más específicas:\n\n### Resumen de Nivel Superior\nEl documento \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se centra en varios determinantes o dimensiones de la calidad de los datos, como la fiabilidad, la extensividad, la coherencia, la puntualidad y la relevancia. Cada una de estas dimensiones se relaciona con la forma en que se recopilan, miden y evalúan los datos, destacando la importancia de tener en cuenta el contexto específico de las preguntas y métodos utilizados para definir métricas y estándares de calidad.\n\n### Preguntas Específicas\n1. **¿Cómo se determina la fiabilidad de los datos en relación con preguntas específicas en el marco de la regulación de medicamentos?**\n - Esta pregunta se centra en el proceso de evaluación de la fiabilidad de los datos y cómo se relaciona con las preguntas específicas que se están abordando en el contexto regulatorio.\n\n2. **¿Qué papel juegan los estándares de referencia en la evaluación de la cobertura y la completitud de los datos en el contexto de la regulación de medicamentos?**\n - Esta pregunta busca profundizar en la importancia de los estándares de referencia y cómo influyen en la medición de la cobertura y la completitud de los datos.\n\n3. **¿Cuáles son los criterios para evaluar la coherencia semántica de los datos y cómo se aplican en la práctica regulatoria?**\n - Esta pregunta se enfoca en los métodos y criterios utilizados para evaluar la coherencia semántica de los datos, así como su aplicación en el ámbito de la regulación de medicamentos.\n\nEstas preguntas están diseñadas para extraer información específica que puede no estar disponible en otros documentos o contextos, y se basan en los conceptos clave presentados en el texto.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nEl contenido presenta una tabla que caracteriza cómo diferentes determinantes afectan las dimensiones de calidad de los datos en el contexto de la regulación de medicamentos de la Unión Europea (UE). Las dimensiones de calidad analizadas son:\n\n1. **Fiabilidad**: Se refiere a la confianza en los datos, que depende de los sistemas y procesos de generación y recolección de datos. Los protocolos de recolección establecidos son fundamentales para garantizar la fiabilidad.\n\n2. **Extensividad**: Evalúa la cantidad y cobertura de los datos. La tabla indica que los datos primarios recolectados siguiendo protocolos adecuados pueden ser suficientes para responder a preguntas regulatorias, aunque no hay garantía de completitud en conjuntos de datos integrados.\n\n3. **Coherencia**: Se relaciona con la consistencia de los datos. La coherencia depende de la orquestación de procesos y del compromiso con estándares internos o externos. Se sugiere que la documentación sobre los procesos de generación de datos puede mejorar la coherencia.\n\n4. **Puntualidad**: Se refiere a la actualidad de los datos. La tabla menciona que algunos aspectos de la puntualidad pueden observarse en los conjuntos de datos, aunque no se profundiza en este aspecto.\n\n5. **Relevancia**: Evalúa la pertinencia de los datos para un uso específico. La relevancia no depende del conjunto de datos en sí, sino de su adecuación a las necesidades de evaluación.\n\n### Entidades Clave\n\n- **Determinantes**: Fundacionales, secundarios e intrínsecos, que influyen en las dimensiones de calidad de los datos.\n- **Dimensiones de Calidad de Datos**: Fiabilidad, extensividad, coherencia, puntualidad y relevancia.\n- **Protocolos de Recolección**: Establecen cómo se deben recolectar los datos para asegurar su calidad.\n- **Procesos de Transformación**: Incluyen la armonización y estandarización de datos, que pueden afectar la precisión y coherencia.\n\n### Conclusión\n\nEl marco de calidad de datos en la regulación de medicamentos de la UE subraya la importancia de la calidad de los datos para la toma de decisiones regulatorias. Comprender cómo los determinantes afectan las dimensiones de calidad es esencial para garantizar que los datos utilizados en la evaluación de medicamentos sean precisos, completos y relevantes.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, reliability, coherence, relevance", + "_node_type": "TextNode", + "document_id": "e4e32951-958a-48ae-b7ee-a92d8b197823", + "doc_id": "e4e32951-958a-48ae-b7ee-a92d8b197823", + "ref_doc_id": "e4e32951-958a-48ae-b7ee-a92d8b197823" + }, + "a864ec55-9408-4106-b50a-c2e71f581584": { + "page_label": "30", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Table 7 – Characterisation of the effect of determinants on Metadata quality dimensions\n\n| Determinant/Dimension | Reliability | Extensiveness | Coherence | Timeliness | Relevance |\n| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------- |\n| Foundational | | Primary For primary data, the extensiveness of metadata can be characterised at source. | Primary Metadata coherence relies on the presence of common standards and terminologies. | Primary Timeliness of Metadata are purely dependent on the processes supporting its collection. | Primary Normally guaranteed by the design of data collection process. |\n| | Primary and secondary Reliability of Metadata relies on the processes to collect it, along the whole data processing chain. One key aspect to ensure reliability is to capture metadata as close to the source as possible. | | Secondary For secondary data, coherence relies on the presence on widely agreed standards and shared resources such as ontologies or reference data services. | Secondary When data are repurposed and used in different systems, timeliness of metadata should be enforced by design (metadata should be in synch with the data). | Secondary Relevant metadata can be required and controlled by a downstream system but cannot be guaranteed at source. |\n| Intrinsic | Primary and secondary Some metadata (e.g., summary statistics) can be generated from a dataset. When data and metadata are considered as whole, traceability can also be assessed by intrinsic measures. | Primary and secondary Intrinsic measures for meta DQ mimic the ones for data (e.g., completeness and missing fields). Unlike data, metadata assessment may not require references to | Primary and secondary Metadata coherence solely depends on a specific metadata and data-independent elements (e.g., shared reference data). | Primary and secondary The assessment of timeliness aspect of data typically depends on metadata (e.g., timestamps). | Primary and secondary Relevance of metadata does not depend on a dataset itself. |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden formular a partir del contexto proporcionado, junto con sus respuestas específicas:\n\n1. **¿Cómo se caracteriza la fiabilidad del metadato en función de su origen (primario o secundario)?**\n - **Respuesta:** La fiabilidad del metadato depende de los procesos utilizados para recolectarlo a lo largo de toda la cadena de procesamiento de datos. Para los metadatos primarios, se enfatiza la importancia de capturarlos lo más cerca posible de la fuente. En el caso de los metadatos secundarios, su fiabilidad también se ve afectada por los procesos de recolección, pero se considera que la coherencia depende de estándares ampliamente aceptados y recursos compartidos.\n\n2. **¿Qué factores influyen en la coherencia de los metadatos y cómo varían entre datos primarios y secundarios?**\n - **Respuesta:** La coherencia de los metadatos primarios depende de la presencia de estándares y terminologías comunes. Para los metadatos secundarios, la coherencia se basa en la existencia de estándares ampliamente acordados y recursos compartidos, como ontologías o servicios de datos de referencia. Esto indica que la coherencia puede ser más difícil de garantizar en metadatos secundarios debido a su dependencia de recursos externos.\n\n3. **¿Cuál es la relación entre la relevancia de los metadatos y el conjunto de datos del que provienen?**\n - **Respuesta:** La relevancia de los metadatos no depende del conjunto de datos en sí. Esto significa que, aunque un conjunto de datos pueda ser relevante para un contexto específico, la relevancia de los metadatos asociados puede ser evaluada independientemente de ese conjunto de datos, lo que sugiere que la relevancia puede ser controlada por sistemas posteriores, pero no garantizada en la fuente.\n\n### Resumen de nivel superior del contexto:\nEl contexto se centra en un marco de calidad de datos en el ámbito de la regulación de medicamentos en la UE, específicamente en la caracterización de cómo diferentes determinantes afectan las dimensiones de calidad de los metadatos. Se analizan aspectos como la fiabilidad, extensividad, coherencia, puntualidad y relevancia de los metadatos, diferenciando entre datos primarios y secundarios. Este análisis es crucial para asegurar que los metadatos sean útiles y precisos en el contexto de la regulación de medicamentos.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nEl documento \"Data quality framework EU medicines regulation\" se centra en la evaluación de la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. A continuación se presentan los temas clave y entidades mencionados en la sección:\n\n1. **Determinantes de la Calidad de los Datos**:\n - **Fiabilidad**: Se refiere a la precisión de los datos y cómo se mide en relación con preguntas específicas. La fiabilidad puede requerir acceso a fuentes de datos originales y estándares de referencia.\n - **Extensividad**: Relacionada con la cobertura y completitud de los datos, que dependen de las preguntas formuladas y los métodos utilizados para su recolección.\n - **Coherencia**: Se refiere a la consistencia de los datos, que puede mejorarse mediante la integración de elementos independientes de los datos y la evaluación continua al integrar nuevas fuentes de datos.\n - **Puntualidad**: La relevancia de la temporalidad de los datos, que debe evaluarse en función de la pregunta y su uso regulatorio, como en procesos de aprobación o monitoreo.\n - **Relevancia**: La pertinencia de los datos en relación con las preguntas específicas que se están abordando.\n\n2. **Métodos de Evaluación**:\n - Se enfatiza que los procesos y sistemas de recolección de datos están diseñados para responder a preguntas específicas y cumplir con los objetivos requeridos en las dimensiones de calidad de los datos.\n - La evaluación de la calidad de los datos debe considerar el contexto de las preguntas y los métodos utilizados, lo que implica que los criterios de evaluación pueden variar según el caso.\n\n3. **Importancia de los Estándares de Referencia**:\n - Los estándares de referencia son cruciales para medir la cobertura y la completitud de los datos, así como para establecer umbrales de aceptación en la evaluación de la fiabilidad.\n\n4. **Coherencia Semántica**:\n - La coherencia semántica de los datos se evalúa en función de preguntas específicas y puede incluir aspectos como la coherencia de la distribución de datos y la abstracción.\n\nEste marco de calidad de datos es esencial para garantizar que la información utilizada en la regulación de medicamentos sea precisa, completa y relevante, lo que a su vez impacta en la seguridad y eficacia de los medicamentos en el mercado.", + "excerpt_keywords": "Keywords: metadata quality, data reliability, EU medicines regulation, data coherence, data timeliness", + "_node_type": "TextNode", + "document_id": "d1298141-9744-48e4-842e-f18627ff74cc", + "doc_id": "d1298141-9744-48e4-842e-f18627ff74cc", + "ref_doc_id": "d1298141-9744-48e4-842e-f18627ff74cc" + }, + "adb2226e-00ab-4a2c-83ff-8c75e86cb0a0": { + "page_label": "31", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Question specific | Question specific\\
Primary and secondary | Primary and secondary | |\n| ----------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |\n| Primary | Metadata requirements are designed for a specific question and are normally sufficient to address it. | The coherence of metadata is independent from a specific question. | Timeliness of metadata are independent from a specific question. |\n| | Primary and secondary | Metadata should be in general reliable independently of a specific question (not all metadata collected may be relevant for all questions). | |\n| Secondary | The characterisation of what metadata are necessary is ultimately dependent on a question (or set of typical questions). | | Relevance of metadata is purely dependent on a question (or range of questions). |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden formular a partir del contexto proporcionado, junto con un resumen de nivel superior que puede ayudar a entender mejor el contenido:\n\n### Resumen de Nivel Superior\nEl documento \"Data quality framework EU medicines regulation\" aborda la importancia de los metadatos en el contexto de la regulación de medicamentos en la Unión Europea. Se distingue entre metadatos primarios y secundarios, enfatizando que los requisitos de metadatos deben ser específicos para las preguntas que se desean responder. Además, se menciona que la coherencia y la puntualidad de los metadatos son independientes de las preguntas específicas, mientras que la relevancia de los metadatos depende de las preguntas planteadas.\n\n### Preguntas\n1. **¿Cómo se determina la suficiencia de los metadatos para abordar preguntas específicas en el contexto de la regulación de medicamentos?**\n - Esta pregunta busca profundizar en el proceso de evaluación de los metadatos y su adecuación para responder a preguntas concretas en el ámbito de la regulación.\n\n2. **¿Qué papel juegan la coherencia y la puntualidad de los metadatos en la calidad de los datos en la regulación de medicamentos, y cómo se relacionan con las preguntas específicas?**\n - Esta pregunta se centra en entender la relación entre la calidad de los metadatos y su independencia de las preguntas, lo que puede ser crucial para la toma de decisiones en el ámbito regulatorio.\n\n3. **¿Cuáles son los criterios para caracterizar los metadatos necesarios en función de un conjunto de preguntas típicas en la regulación de medicamentos?**\n - Esta pregunta busca explorar los criterios y procesos utilizados para identificar qué metadatos son necesarios, lo que puede ser fundamental para la recopilación y análisis de datos en el sector farmacéutico.\n\nEstas preguntas están diseñadas para extraer información específica que puede no estar disponible en otras fuentes, centrándose en la aplicación práctica de los conceptos discutidos en el documento.", + "prev_section_summary": "### Resumen de temas clave y entidades de la sección\n\nLa sección se centra en la **calidad de los metadatos** en el contexto de la regulación de medicamentos en la Unión Europea, analizando cómo diferentes determinantes afectan diversas dimensiones de calidad. Los temas clave incluyen:\n\n1. **Fiabilidad**: \n - La fiabilidad de los metadatos depende de los procesos de recolección a lo largo de la cadena de procesamiento de datos. Para metadatos primarios, se enfatiza la captura cercana a la fuente, mientras que para los secundarios, la fiabilidad también se ve influenciada por estándares y recursos compartidos.\n\n2. **Extensividad**: \n - La extensividad de los metadatos primarios se puede caracterizar en la fuente, mientras que para los secundarios, no se proporciona información específica en la tabla.\n\n3. **Coherencia**: \n - La coherencia de los metadatos primarios se basa en estándares y terminologías comunes. Para los secundarios, depende de estándares ampliamente aceptados y recursos compartidos, lo que puede dificultar su garantía.\n\n4. **Puntualidad**: \n - La puntualidad de los metadatos está relacionada con los procesos de recolección y debe estar sincronizada con los datos, especialmente cuando se reutilizan en diferentes sistemas.\n\n5. **Relevancia**: \n - La relevancia de los metadatos no depende del conjunto de datos del que provienen, lo que implica que puede ser controlada por sistemas posteriores, pero no garantizada en la fuente.\n\n### Entidades clave:\n- **Metadatos**: Información que describe otros datos, crucial para la calidad de los datos en la regulación de medicamentos.\n- **Datos primarios y secundarios**: Clasificación de los datos según su origen, que afecta la calidad de los metadatos.\n- **Estándares y terminologías**: Elementos esenciales para asegurar la coherencia y fiabilidad de los metadatos.\n- **Procesos de recolección**: Métodos utilizados para obtener metadatos, que impactan su calidad.\n\nEste análisis es fundamental para garantizar que los metadatos sean útiles y precisos en el ámbito regulatorio de medicamentos, asegurando así la integridad y la calidad de la información utilizada en este sector.", + "excerpt_keywords": "Keywords: metadata, data quality, EU medicines regulation, primary and secondary data, relevance", + "_node_type": "TextNode", + "document_id": "0751575d-2ba5-4d8a-a973-7d7685ca5b2d", + "doc_id": "0751575d-2ba5-4d8a-a973-7d7685ca5b2d", + "ref_doc_id": "0751575d-2ba5-4d8a-a973-7d7685ca5b2d" + }, + "e576e6ca-1d89-48a5-853a-f43b0e3ae9a6": { + "page_label": "32", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page32-3.jpg" + ], + "parsed_text_markdown": "# 7.1. Foundational determinants: Recommendation and maturity levels\n\nA characterisation of the systems and processes underpinning data generation and manipulation (foundational determinants) is necessary to manage DQ. Below is a set of defined maturity levels, each providing a progressive hierarchy of recommendations for the characterisation of foundational determinants, with the intention to chart a direction of improvement towards adequate and efficient characterisation of these aspects of DQ (see figure 5). It is recommended that FAIR principles [16] for data and metadata be implemented as early as possible, or partially, along maturity models.\n\n!Maturity Model\n\n**Characterisation of Determinants for the Development of the Maturity Models**\n\nFigure 5 - Maturity model for data quality determinants\n\n## 7.1.1. Level 1: documented\n\nFor data to be adequate for decision making, at a minimum, the processes that pertain to data generation and manipulation should be documented, true, verifiable (when relevant, this may extend to training procedures) and versioned. This is fundamental and ensures the reliability of any derived information. The documentation should cover determinants for Reliability (Precision), Extensiveness, Coherence and, when relevant, Timeliness. While some of these determinants depend on a specific question, data collection processes and systems will generally be designed with some generic questions in mind. The provision of documentation for data processing and transformation are also essential to guarantee that Reliability is preserved and should be provided for all such processing by different actors along the data life cycle.\n![page32-3.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page32-3.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder a partir del contexto proporcionado, junto con un resumen de nivel superior del contenido:\n\n### Resumen de nivel superior:\nEl documento aborda la importancia de caracterizar los sistemas y procesos que sustentan la generación y manipulación de datos (denominados determinantes fundamentales) para gestionar la calidad de los datos (DQ). Se presentan niveles de madurez que ofrecen recomendaciones progresivas para esta caracterización, sugiriendo la implementación temprana de los principios FAIR para datos y metadatos. En el primer nivel de madurez, se enfatiza la necesidad de documentar los procesos relacionados con la generación y manipulación de datos, asegurando que sean verificables y versionados, lo cual es esencial para la fiabilidad de la información derivada.\n\n### Preguntas específicas:\n1. **¿Cuáles son los determinantes fundamentales que deben ser documentados para garantizar la fiabilidad de los datos en el contexto de la regulación de medicamentos en la UE?**\n - Esta pregunta se centra en los aspectos específicos de la documentación que son cruciales para la calidad de los datos, como la precisión, la extensividad, la coherencia y la puntualidad.\n\n2. **¿Qué papel juegan los principios FAIR en la mejora de la calidad de los datos según el marco de regulación de medicamentos de la UE?**\n - Esta pregunta busca explorar cómo la implementación de los principios FAIR puede influir en la calidad de los datos y en la madurez de los sistemas de gestión de datos.\n\n3. **¿Qué implicaciones tiene la falta de documentación en los procesos de generación y manipulación de datos para la toma de decisiones en el ámbito de la regulación de medicamentos?**\n - Esta pregunta aborda las consecuencias prácticas de no seguir las recomendaciones de documentación, lo que puede afectar la fiabilidad y la calidad de la información utilizada en la toma de decisiones regulatorias.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nEl documento \"Data quality framework EU medicines regulation\" se centra en la importancia de los metadatos en la regulación de medicamentos en la Unión Europea. A continuación se presentan los temas clave y entidades mencionadas en la sección:\n\n#### Temas Clave:\n1. **Metadatos Primarios y Secundarios**:\n - Se distingue entre metadatos primarios, que son específicos para preguntas concretas, y metadatos secundarios, cuya relevancia depende de un conjunto de preguntas típicas.\n\n2. **Requisitos de Metadatos**:\n - Los requisitos de metadatos deben ser diseñados para abordar preguntas específicas y, en general, son suficientes para ello.\n\n3. **Coherencia y Puntualidad**:\n - La coherencia y la puntualidad de los metadatos son independientes de las preguntas específicas, lo que implica que estos aspectos son fundamentales para la calidad de los datos en general.\n\n4. **Relevancia de los Metadatos**:\n - La relevancia de los metadatos es dependiente de las preguntas planteadas, lo que significa que no todos los metadatos recopilados son relevantes para todas las preguntas.\n\n5. **Caracterización de Metadatos**:\n - La identificación de los metadatos necesarios está vinculada a las preguntas que se desean responder, lo que requiere un proceso de caracterización adecuado.\n\n#### Entidades:\n- **Regulación de Medicamentos**: Contexto en el que se aplica el marco de calidad de datos.\n- **Unión Europea**: Entidad geográfica y política relevante para la regulación de medicamentos.\n- **Metadatos**: Información que describe otros datos y su importancia en la calidad de los datos regulativos.\n\nEste resumen destaca la estructura y la función de los metadatos en el contexto de la regulación de medicamentos, enfatizando su papel en la calidad de los datos y la toma de decisiones en el ámbito regulatorio.", + "excerpt_keywords": "Keywords: data quality, foundational determinants, FAIR principles, maturity levels, documentation", + "_node_type": "TextNode", + "document_id": "10ceae1c-6517-44bc-9eed-f295979366e2", + "doc_id": "10ceae1c-6517-44bc-9eed-f295979366e2", + "ref_doc_id": "10ceae1c-6517-44bc-9eed-f295979366e2" + }, + "94d0e2f3-2c54-4456-81bd-70e006ad40f8": { + "page_label": "33", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "From a metadata perspective, this means metadata (in some form) should always accompany a dataset it refers to.\n\nTo guarantee the quality of data, audit procedures or other controls should be in place.\n\nWhen a system is designed for continuous data collection (as opposed to a one-off capture), additional processes of performance monitoring and improvement should be in place.\n\n## 7.1.2. Level 2: formalised\n\nThe second level of the maturity model includes and extends the first level, by requiring that, whenever possible, documentation and metadata should be following an industry standard framework or QMS32. Level 2 should be considered the minimal level of acceptable maturity, though exceptions may arise for novel data types. The recommendation to use standards extends to metadata.\n\n## 7.1.3. Level 3: implemented\n\nSystems are in place that implement industry standard DQ processes systematically and by design. Infrastructure should be in place to support data management, including support for standardisation (e.g., reference data management or MDM). By reducing the potential for human error, such an implementation can generally improve Reliability and Coherence. Such an implementation may also be necessary to guarantee Timeliness and it should ensure that metadata are collected by design, and as close to the data generation or collection events as possible.\n\n## 7.1.4. Level 4: automated\n\nThe operations and output of the above systems and infrastructure should be machine readable as to unify data and DQ elements for direct downstream consumption. All data and metadata should be represented following FAIR principles [15] to allow complete automatic processing of quality parameters. This is intended to be an aspirational level.\n\n## 7.2. Intrinsic determinants: Recommendations and maturity levels\n\nBeyond documented evidence of how data were collected or generated, measures of intrinsic aspects of DQ can be applied. These can be directly derived from the dataset, but their computation could also rely on some external body of knowledge.\n\n### 7.2.1. Level 0: intrinsic\n\nThere are no hard minimal requirements for quality, as any piece of data can be assessed before being used to generate evidence33. Nevertheless, the propagation of data without an associated quality assessment should be discouraged.\n\n### 7.2.2. Level 1: metadata\n\nData are provided with a set of quality metrics as metadata. Some of these data can be directly derived from the dataset while other derive from the overall data collection process (e.g., sampling).\n\n----\n\n32 What industry standards or frameworks applies depend on specific data types and use cases, and as such can be defined only in specifications of this framework. Some initial references are however provided in the “implementation notes” session.\n\n33 This initial level is assigned \"0\" to clarify that it corresponds to data \"as is\" irrespective of their intended use for regulatory decision making.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que el contexto proporcionado puede responder, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento describe un marco de calidad de datos en el contexto de la regulación de medicamentos en la UE. Se presenta un modelo de madurez que abarca varios niveles, desde la simple presencia de metadatos hasta sistemas automatizados que cumplen con principios FAIR. Cada nivel del modelo establece requisitos específicos para la documentación, la gestión de datos y la evaluación de la calidad, enfatizando la importancia de la estandarización y la reducción de errores humanos en la recolección y gestión de datos.\n\n### Preguntas específicas:\n\n1. **¿Cuáles son los requisitos mínimos para la documentación y los metadatos en el Nivel 2 del modelo de madurez?**\n - El Nivel 2 del modelo de madurez requiere que la documentación y los metadatos sigan un marco estándar de la industria o un sistema de gestión de calidad (QMS). Este nivel se considera el mínimo aceptable, aunque pueden existir excepciones para tipos de datos novedosos.\n\n2. **¿Qué se entiende por \"principios FAIR\" en el contexto del Nivel 4 del modelo de madurez?**\n - En el Nivel 4, se espera que todas las operaciones y salidas de los sistemas sean legibles por máquina y que todos los datos y metadatos se representen siguiendo los principios FAIR (Findable, Accessible, Interoperable, Reusable). Esto permite un procesamiento automático completo de los parámetros de calidad.\n\n3. **¿Qué se recomienda en el Nivel 1 en relación con los métricas de calidad de los datos?**\n - En el Nivel 1, se recomienda que los datos se proporcionen con un conjunto de métricas de calidad como metadatos. Algunas de estas métricas pueden derivarse directamente del conjunto de datos, mientras que otras pueden provenir del proceso general de recolección de datos, como el muestreo.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Determinantes Fundamentales**: Se refiere a los sistemas y procesos que sustentan la generación y manipulación de datos, esenciales para gestionar la calidad de los datos (DQ).\n\n2. **Niveles de Madurez**: Se presentan niveles definidos que ofrecen una jerarquía progresiva de recomendaciones para la caracterización de los determinantes fundamentales, con el objetivo de mejorar la calidad de los datos.\n\n3. **Principios FAIR**: Se recomienda implementar los principios FAIR (Findable, Accessible, Interoperable, Reusable) para datos y metadatos desde las primeras etapas de desarrollo de los modelos de madurez.\n\n4. **Documentación de Procesos**: En el primer nivel de madurez, se enfatiza la necesidad de documentar los procesos de generación y manipulación de datos. Esta documentación debe ser verificable, versionada y cubrir determinantes como la fiabilidad (precisión), extensividad, coherencia y, cuando sea relevante, puntualidad.\n\n5. **Ciclo de Vida de los Datos**: La documentación también debe incluir procesos de procesamiento y transformación de datos, asegurando que la fiabilidad se mantenga a lo largo del ciclo de vida de los datos.\n\n### Entidades Clave:\n- **Data Quality (DQ)**: Calidad de los datos.\n- **FAIR Principles**: Principios para la gestión de datos y metadatos.\n- **Maturity Levels**: Niveles de madurez para la caracterización de determinantes.\n- **Reliability**: Fiabilidad de la información derivada de los datos.\n- **Data Life Cycle**: Ciclo de vida de los datos, que incluye generación, manipulación y transformación.", + "excerpt_keywords": "Keywords: data quality, maturity model, metadata, FAIR principles, regulatory compliance", + "_node_type": "TextNode", + "document_id": "10d9f60a-b1db-44f3-868f-97adc3cb0ae4", + "doc_id": "10d9f60a-b1db-44f3-868f-97adc3cb0ae4", + "ref_doc_id": "10d9f60a-b1db-44f3-868f-97adc3cb0ae4" + }, + "7c92fdab-89a4-43f4-b261-aafe871d9237": { + "page_label": "34", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "Metadata should also cover the description of data elements that are necessary for its interpretation (e.g., data dictionaries).\n\n## 7.2.3. Level 2: standardised\n\nData are provided with a standardised set of quality metrics, which can be compared across datasets. When applicable or possible, standards should extend to cover reference knowledge that can be used to assess a dataset in respect to what it is meant to represent (e.g., typical population distributions to assess biases). Metadata makes use of shared definitions, which also enable comparability and integration across datasets.\n\n## 7.2.4. Level 3: automated\n\nQuality assessment is automated (at least for a large extent of metrics). In general, this is feasible only when data are represented in standard ways (e.g., in a CDM), so that a standard library of tests can be run on incoming data. Data and metadata should follow FAIR principles [16]³⁴.\n\n## 7.2.5. Level 4: feedback\n\nThere is a data ecosystem in place so that quality assessment by data consumers can provide feedback to improve the data collection and production process, thus allowing a continuous monitoring and improvement of DQ.\n\n(Note that the order of maturity of level 2 and 3 may change for particular data types.)\n\n## 7.3. Question-specific determinants: Recommendations and maturity levels\n\nIn general, it is not possible to assess the Relevance of a dataset, or aspects of Extensiveness and Precision, without a target question and a defined analysis strategy. However, when considering the adoption of a large body of data for regulatory decision making and its possible use beyond primary use cases, it becomes important to articulate to what degree DQ, including Relevance, can be assessed a-priori.\n\n### 7.3.1. Level 1: ad-hoc\n\nAll dimensions that are question specific are assessed only at “question time” on an ad-hoc basis.\n\n### 7.3.2. Level 2: domain-defined\n\nA range of common questions is identified, from which metrics and thresholds can be derived that can be used to guarantee acceptable levels of quality. Data published in data catalogues should make use of such metrics.\n\n### 7.3.3. Level 3: question-defined\n\nThe requirements for a specific question are precisely codified and can be mapped to metrics and thresholds in a way that could automatically assess the Relevance of a dataset for a specific question. At this level, the context under which data will be interpreted for decision making should be formalised.\n\n----\n\n³⁴ As for foundational determinants, FAIR principles should be applied as early as possible, at least partially. Level 3 requires a full implementation of FAIR principles.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que el contexto proporcionado puede responder, junto con un resumen de nivel superior del contenido:\n\n### Resumen de nivel superior:\nEl documento describe un marco de calidad de datos en el contexto de la regulación de medicamentos en la UE. Se establecen diferentes niveles de madurez para la evaluación de la calidad de los datos, que van desde enfoques ad-hoc hasta sistemas automatizados y retroalimentación continua. Además, se discuten determinantes específicos de preguntas que permiten evaluar la relevancia y calidad de los conjuntos de datos en función de preguntas específicas y estrategias de análisis.\n\n### Preguntas:\n\n1. **¿Cuáles son los diferentes niveles de madurez en la evaluación de la calidad de los datos y qué características definen cada uno?**\n - Esta pregunta permite explorar en detalle los niveles de madurez (ad-hoc, domain-defined, question-defined, etc.) y cómo cada uno contribuye a la evaluación de la calidad de los datos.\n\n2. **¿Cómo se pueden aplicar los principios FAIR en la evaluación de la calidad de los datos y en qué nivel se requiere su implementación completa?**\n - Esta pregunta se centra en la importancia de los principios FAIR en la gestión de datos y cómo su aplicación varía según el nivel de madurez de la evaluación de la calidad.\n\n3. **¿Qué papel juega la retroalimentación de los consumidores de datos en la mejora continua del proceso de recolección y producción de datos?**\n - Esta pregunta permite profundizar en el nivel de madurez que involucra la retroalimentación y cómo esta puede influir en la calidad de los datos a lo largo del tiempo. \n\nEstas preguntas están diseñadas para extraer información específica y detallada que puede no estar disponible en otras fuentes, basándose en el contenido del documento.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Marco de Calidad de Datos**: El documento establece un marco para asegurar la calidad de los datos en el contexto de la regulación de medicamentos en la UE, enfatizando la importancia de la documentación y los metadatos.\n\n2. **Modelo de Madurez**: Se presenta un modelo de madurez que consta de varios niveles, cada uno con requisitos específicos:\n - **Nivel 0: Intrínseco**: No hay requisitos mínimos de calidad; se desaconseja la propagación de datos sin evaluación de calidad.\n - **Nivel 1: Metadatos**: Los datos deben incluir métricas de calidad como metadatos, derivadas del conjunto de datos o del proceso de recolección.\n - **Nivel 2: Formalizado**: Se requiere que la documentación y los metadatos sigan un marco estándar de la industria o un sistema de gestión de calidad (QMS).\n - **Nivel 3: Implementado**: Sistemas que implementan procesos de calidad de datos (DQ) de manera sistemática, con infraestructura para la gestión de datos.\n - **Nivel 4: Automatizado**: Sistemas y salidas legibles por máquina que cumplen con los principios FAIR (Findable, Accessible, Interoperable, Reusable) para el procesamiento automático de parámetros de calidad.\n\n3. **Principios FAIR**: En el Nivel 4, se espera que todos los datos y metadatos se representen siguiendo estos principios, lo que permite un procesamiento automático completo.\n\n4. **Controles de Calidad**: Se enfatiza la necesidad de procedimientos de auditoría y controles para garantizar la calidad de los datos, especialmente en sistemas de recolección continua.\n\n5. **Estandarización**: La estandarización de metadatos y documentación es fundamental para mejorar la fiabilidad y coherencia de los datos, así como para reducir errores humanos.\n\n### Entidades Clave:\n- **Datos**: Información que se recolecta y utiliza para la toma de decisiones regulatorias.\n- **Metadatos**: Datos que describen otros datos, esenciales para la calidad y la gestión de datos.\n- **Sistema de Gestión de Calidad (QMS)**: Marco que guía la gestión de la calidad en la recolección y manejo de datos.\n- **Principios FAIR**: Conjunto de principios que promueven la gestión y el uso eficiente de los datos.\n\nEste resumen destaca la estructura y los requisitos del marco de calidad de datos, así como la importancia de la estandarización y la automatización en la gestión de datos en el ámbito de la regulación de medicamentos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, FAIR principles, metadata, assessment levels", + "_node_type": "TextNode", + "document_id": "469d3ed5-90de-4554-8705-52596c48f6a6", + "doc_id": "469d3ed5-90de-4554-8705-52596c48f6a6", + "ref_doc_id": "469d3ed5-90de-4554-8705-52596c48f6a6" + }, + "ddfc2fc5-080e-4a28-83ad-9b889cab0715": { + "page_label": "35", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Considerations for implementation of DQF\n\nThis section provides a set of observations and recommendations to guide the implementation of this DQF and its specialisations, to help achieving higher levels of maturity.\n\n## 8.1. Quality at source\n\nAs a general guideline, in designing data collection and generation processes, aspects of DQ should be addressed as early as possible. For instance, assessment of quality done close to the moment of production can help in correcting a collection error. The further data travels from the original context, the harder it becomes to correct issues. This is particularly relevant for metadata as knowledge of the context of data generation is maximally present only at generation time.\n\n## 8.2. The role of Master Data Management (MDM) and reference data\n\nThe availability of MDM and reference data has a direct impact on DQ. It is often a pre-requisite for data consistency, and it can even impact Reliability in some data production scenarios (e.g., materials data), as disconnected information can result in erroneous information. More broadly, MDM and reference data enable automation of a range of DQ checks and hence have an impact on Reliability as well.\n\nShared MDM and reference data can address aspects of Coherence beyond the primary use case that the data were generated for, as the use of standards guarantees some level of Semantic Coherence is maintained even beyond data aggregation steps.\n\n## 8.3. The role of QMS and computerised systems\n\nThe implementation of a DQF at higher maturity levels requires the formalisation and implementation of systems and processes to support DQ.\n\nA Quality Management System (QMS) [20] is a formalised approach adopted by an organisation that documents processes, procedures, and responsibilities for achieving quality policies and objectives (e.g., Good Clinical Practices [GCP], Good Laboratory Practices [GLP] or Good Manufacturing Practice [GMP]). It achieves these quality objectives through quality planning, quality assurance, quality control and quality improvement. Standards like the ISO 9000 family define QMS across industries, while more specific QMS have been developed for specific industry or products. Life Science Industry specific QMSs should be considered depending on the nature of the data:\n\n- Clinical trial data: ISO 14155 and EU Directive 2001/20/EC for GCP (clinical trial data)\n- Data from medical devices or diagnostic products: ISO 13485 Quality System Regulation (QSR)\n- Data from lab research: EU Directive 2004/9/EC and 2004/10/EC, GLP\n- Data from clinical labs: ISO 15189 and ISO 17025\n\nWhenever possible DQ processes should be framed in the context of standard QMSs.\n\nFurthermore, in today's digital world, foundational DQ determinants are also impacted by computerised systems, that are used to create, modify, maintain, archive, retrieve, or transmit data. A software development life cycle including software quality assurance system ensures the appropriate design, development and testing of the software. This can be targeted through the ISO 250xx standard family.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden formular a partir del contexto proporcionado, junto con respuestas específicas que probablemente no se encuentren en otro lugar:\n\n### Preguntas y Respuestas\n\n1. **¿Por qué es importante abordar la calidad de los datos (DQ) desde el inicio del proceso de recolección y generación de datos?**\n - **Respuesta:** Abordar la calidad de los datos desde el inicio es crucial porque permite identificar y corregir errores de recolección en el momento de la producción. A medida que los datos se alejan de su contexto original, se vuelve más difícil corregir problemas, especialmente en el caso de los metadatos, donde el conocimiento del contexto de generación es máximo solo en el momento de la producción.\n\n2. **¿Cómo impacta la gestión de datos maestros (MDM) en la calidad de los datos (DQ) y la coherencia semántica?**\n - **Respuesta:** La disponibilidad de MDM y datos de referencia es fundamental para la consistencia de los datos, ya que la información desconectada puede llevar a errores. Además, el uso compartido de MDM y datos de referencia permite la automatización de diversas verificaciones de calidad de datos, lo que mejora la fiabilidad. Al utilizar estándares, se garantiza que se mantenga un nivel de coherencia semántica incluso más allá de los pasos de agregación de datos.\n\n3. **¿Qué rol juegan los sistemas de gestión de calidad (QMS) en la implementación de un marco de calidad de datos (DQF) en la industria de ciencias de la vida?**\n - **Respuesta:** Los QMS son esenciales para formalizar y estructurar los procesos y sistemas que apoyan la calidad de los datos. Estos sistemas documentan procedimientos y responsabilidades para alcanzar políticas y objetivos de calidad, y deben alinearse con estándares específicos de la industria, como GCP, GLP y GMP. La implementación de un DQF en niveles de madurez más altos requiere que los procesos de calidad de datos se enmarquen dentro de estos QMS.\n\n### Resumen de Nivel Superior\n\nEl contexto aborda la implementación de un marco de calidad de datos (DQF) en el ámbito de la regulación de medicamentos en la UE. Se enfatiza la importancia de abordar la calidad de los datos desde el inicio de los procesos de recolección, el papel crítico de la gestión de datos maestros y datos de referencia en la consistencia y fiabilidad de los datos, y la necesidad de sistemas de gestión de calidad (QMS) para formalizar los procesos de calidad. Además, se menciona cómo los sistemas informáticos impactan los determinantes de calidad de datos en el entorno digital actual.\n\n### Preguntas Adicionales\n\n4. **¿Qué estándares específicos deben considerarse para los datos de ensayos clínicos y por qué son relevantes?**\n5. **¿Cómo se relaciona el ciclo de vida del desarrollo de software con la calidad de los datos en el contexto de sistemas informáticos?**", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Marco de Calidad de Datos**: El documento establece un marco para evaluar la calidad de los datos en el contexto de la regulación de medicamentos en la UE, definiendo diferentes niveles de madurez en la evaluación de la calidad.\n\n2. **Niveles de Madurez**:\n - **Nivel 1: Ad-hoc**: Evaluación de la calidad de los datos solo en el momento de formular preguntas específicas.\n - **Nivel 2: Estandarizado**: Uso de métricas de calidad estandarizadas que permiten la comparación entre conjuntos de datos, con definiciones compartidas para facilitar la integración.\n - **Nivel 3: Automatizado**: Evaluación de calidad automatizada, posible solo si los datos están representados de manera estándar, siguiendo principios FAIR.\n - **Nivel 4: Retroalimentación**: Ecosistema de datos donde la evaluación de calidad por parte de los consumidores proporciona retroalimentación para mejorar continuamente el proceso de recolección y producción de datos.\n\n3. **Determinantes Específicos de Preguntas**: Se discuten niveles de madurez específicos para evaluar la relevancia de los conjuntos de datos en función de preguntas y estrategias de análisis definidas:\n - **Nivel 1: Ad-hoc**: Evaluación en el momento de la pregunta.\n - **Nivel 2: Definido por el dominio**: Identificación de preguntas comunes para derivar métricas y umbrales de calidad.\n - **Nivel 3: Definido por la pregunta**: Requisitos específicos codificados que permiten la evaluación automática de la relevancia de un conjunto de datos.\n\n4. **Principios FAIR**: Se enfatiza la importancia de aplicar los principios FAIR (Findable, Accessible, Interoperable, Reusable) en la gestión de datos, con un requerimiento de implementaci��n completa en el Nivel 3.\n\n5. **Mejora Continua**: La retroalimentación de los consumidores de datos es crucial para la mejora continua del proceso de recolección y producción de datos, lo que contribuye a un monitoreo constante de la calidad de los datos.\n\n### Entidades Clave\n- **Datos**: Elementos que se evalúan en términos de calidad y relevancia.\n- **Métricas de Calidad**: Estándares utilizados para evaluar y comparar conjuntos de datos.\n- **Ecosistema de Datos**: Entorno donde se lleva a cabo la recolección, producción y evaluación de datos.\n- **FAIR**: Principios que guían la gestión y uso de datos en la investigación y regulación. \n\nEste resumen destaca los aspectos fundamentales del marco de calidad de datos y su aplicación en la regulación de medicamentos, así como la importancia de la estandarización y la retroalimentación en la mejora de la calidad de los datos.", + "excerpt_keywords": "Keywords: Data Quality Framework, Master Data Management, Quality Management System, Regulatory Compliance, Digital Data Integrity", + "_node_type": "TextNode", + "document_id": "d59dcde7-a1ee-48f6-a10e-ce9b7a5a548b", + "doc_id": "d59dcde7-a1ee-48f6-a10e-ce9b7a5a548b", + "ref_doc_id": "d59dcde7-a1ee-48f6-a10e-ce9b7a5a548b" + }, + "e53ba4cc-108c-47a9-be6d-1131b27c909e": { + "page_label": "36", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page36-1.jpg" + ], + "parsed_text_markdown": "named Systems and Software Quality Requirements and Evaluation (more specifically, data models can be addressed with ISO 25012). Computer system qualification and validation ensures the software is appropriately implemented, and necessary process controls are in place for using it according to its specifications, including documentation, access control, vendor management and audits. The EMA/226170/2021 Guideline on computerised systems and electronic data in clinical trials provides direction for GCP but can be adopted more broadly. The following decision tree provides guidance on how to consider QMSs for a DQF implementation (see figure 6).\n\n!Decision Tree\n\n**Figure 6 - Decision tree for QMS adoption in DQF implementation**\n\n### 8.4. The role of ISO and industry standards\n\nThe International Organisation for Standardisation (ISO) has produced standards providing frameworks for the implementation of various data management aspects, that are field tested and for which platforms, supporting services and certification bodies are established. These standards are often developed for implementation of industries where EMA's regulatory decision making does not apply.\n\n**ISO 9000:** Describes the standards for quality management systems on all levels of an organisation. The adoption of this standard could be considered if no industry specific QMS applies.\n\n**ISO 8000:** Describes the standards for the quality of Master Data and their exchange between systems. The standard describes how Master Data conform to a set of specifications expressed in a formal syntax and use specified identifiers to check against data requirements that point to a data dictionary. This standard also covers the methods for achieving data governance, data quality management, data quality assessment and rules for determining the quality of master data and industrial data. This includes the exchange of characteristics of data and identifiers and data.\n\n35 To clarify, the ISO 8000 series does not establish a new management system. The series, instead, extends and clarifies ISO 9001 for the case where data are the product.\n![page36-1.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page36-1.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento aborda el marco de calidad de datos en la regulación de medicamentos de la UE, destacando la importancia de la calificación y validación de sistemas informáticos. Se menciona la guía EMA/226170/2021 sobre sistemas informatizados y datos electrónicos en ensayos clínicos, así como la relevancia de las normas ISO, específicamente ISO 9000 y ISO 8000, en la implementación de sistemas de gestión de calidad (QMS) y en la gestión de datos maestros.\n\n### Preguntas específicas:\n\n1. **¿Qué papel desempeña la norma ISO 8000 en la gestión de datos maestros y cómo se relaciona con la calidad de los datos?**\n - La norma ISO 8000 establece estándares para la calidad de los datos maestros y su intercambio entre sistemas, definiendo especificaciones formales y métodos para la gobernanza de datos, gestión de calidad de datos y evaluación de calidad.\n\n2. **¿Cuáles son los elementos clave que deben considerarse al implementar un sistema de gestión de calidad (QMS) según el marco de calidad de datos (DQF)?**\n - Al implementar un QMS en el contexto del DQF, se deben considerar aspectos como la calificación y validación de sistemas informáticos, controles de proceso, documentación, gestión de acceso, gestión de proveedores y auditorías.\n\n3. **¿Cómo se relaciona la guía EMA/226170/2021 con la implementación de sistemas de gestión de calidad en ensayos clínicos?**\n - La guía EMA/226170/2021 proporciona directrices sobre el uso de sistemas informatizados y datos electrónicos en ensayos clínicos, estableciendo un marco que puede ser adoptado más ampliamente para asegurar la calidad y la conformidad en la gestión de datos en este contexto.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nLa sección aborda la implementación de un marco de calidad de datos (DQF) en el contexto de la regulación de medicamentos en la UE, destacando varios aspectos fundamentales para lograr niveles más altos de madurez en la calidad de los datos.\n\n#### Temas Clave:\n\n1. **Calidad en la Fuente**:\n - Importancia de abordar la calidad de los datos desde el inicio de los procesos de recolección y generación.\n - La evaluación de la calidad cercana al momento de producción permite corregir errores más fácilmente.\n\n2. **Gestión de Datos Maestros (MDM) y Datos de Referencia**:\n - La disponibilidad de MDM y datos de referencia es crucial para la consistencia y fiabilidad de los datos.\n - Facilitan la automatización de verificaciones de calidad de datos y mantienen la coherencia semántica a través de estándares.\n\n3. **Sistemas de Gestión de Calidad (QMS)**:\n - Los QMS son esenciales para formalizar procesos y sistemas que apoyan la calidad de los datos.\n - Deben alinearse con estándares específicos de la industria, como GCP, GLP y GMP, dependiendo del tipo de datos.\n\n4. **Impacto de Sistemas Informáticos**:\n - Los sistemas informáticos afectan los determinantes de calidad de datos en el entorno digital actual.\n - Un ciclo de vida de desarrollo de software que incluya aseguramiento de calidad es fundamental para el diseño y desarrollo adecuado del software.\n\n#### Entidades:\n\n- **Estándares de Calidad**:\n - ISO 9000 (QMS general)\n - ISO 14155 (ensayos clínicos)\n - ISO 13485 (dispositivos médicos)\n - ISO 15189 e ISO 17025 (laboratorios clínicos)\n - EU Directives (2001/20/EC, 2004/9/EC, 2004/10/EC)\n\n- **Conceptos Clave**:\n - Calidad de Datos (DQ)\n - Coherencia Semántica\n - Automatización de Verificaciones de Calidad\n - Ciclo de Vida del Desarrollo de Software\n\nEste resumen sintetiza los puntos más relevantes de la sección, enfatizando la importancia de la calidad de los datos en la regulación de medicamentos y los sistemas necesarios para garantizarla.", + "excerpt_keywords": "Keywords: data quality, ISO standards, quality management systems, EMA guidelines, master data management", + "_node_type": "TextNode", + "document_id": "bc16a9d4-0d4c-4974-b27c-b2b0ffa1e2ee", + "doc_id": "bc16a9d4-0d4c-4974-b27c-b2b0ffa1e2ee", + "ref_doc_id": "bc16a9d4-0d4c-4974-b27c-b2b0ffa1e2ee" + }, + "f090aea8-6538-4d74-a92e-f7aa808b8dcb": { + "page_label": "37", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "processing like creating, collecting, storing, maintaining, transferring, exploiting, and presenting data to deliver information. This standard is valuable Master Data play an integrate role in the process of generation of data for regulatory decision making.\n\n**ISO 25012**: Defines a general data quality model for data retained in a structured format within a computer system, typical for data considered for regulatory decision making. It provides a framework for establishing data quality requirements, data quality measures, and a plan to perform data quality evaluations. It could be used across the entire life cycle from data collection or generation, management and processing, publishing, aggregation, and consumption and to evaluate the compliance of data with regulations. An example of implementation of this standard is done by Statistics Finland, which is also in line with the European Interoperability Framework, the FAIR principles and the Code of Practice for Statistics [13].\n\n**ISO 13485**: Specifies requirements for a QMS for an organisation to design, build and obtain authorisation for medical devices that consistently meet customer and regulatory requirements. As with all QMS, this standard focusses on the quality of the product, and affects DQ as they are relevant for the design, development, production, and use of the device.\n\n## 8.5. Notes on ALCOA+\n\nALCOA+ is a framework for data integrity used across the pharmaceutical industry in areas such as research, manufacturing, testing and supply chain. It postulates a set of principles that data and its documentation should comply to. In the specifics, data should be Attributable, Legible, Contemporaneous, Original, Accurate (ALCOA). The + refers to the following attributes: Consistent, Enduring, Available, Traceable. More information on these principles is available in the Guideline on computerised systems and electronic data [21] and in [22].\n\nIn relation to this Framework, ALCOA+ provides recommendations that focus on foundational determinants and that affect primarily the Reliability, but also the Extensiveness, Coherence and Timeliness dimensions.\n\nWhen considering the ALCOA+ principles, they can be closely aligned to the dimensions of the present DQF, with the caveat that the ALCOA definitions are more focused and operational. For instance, the ALCOA definition of “Accurate” is expressing a set of characteristics (e.g., verifiable coding processes, validated data transfer) that should be in place so that “data should be an accurate representation of the observation made”, that is how reliability (and more precisely Accuracy) is defined in this DQF. Other principles such as “Legible” and “Original” also falls under the Reliability dimension (as they answer the question “is data reflecting reality?”), but they are not explicitly articulated in this Framework as they are a pre-condition to a regulatory submission.\n\nFor suitable use cases, ALCOA+ compliant specifications can enable level 2 (formalised) and above maturity levels for foundational determinants.\n\n## 8.6. Notes on implementation of DQ controls\n\nThere are different possible implementations of data quality controls (i.e., testing).\n\nIf the **true facts** the data are representing are known and accessible, data can be tested using validation vs the source records containing these facts (see framework of reference session above). However, validation can be costly and time consuming, and often requires the use of adjudicators if the", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que se pueden responder con el contexto proporcionado, junto con resúmenes de nivel superior que pueden ayudar a formularlas:\n\n### Resumen de nivel superior:\nEl documento aborda la importancia de un marco de calidad de datos en la regulación de medicamentos en la UE, destacando estándares como ISO 25012 e ISO 13485, así como el marco ALCOA+ para la integridad de los datos. Se discuten los principios de ALCOA+ y su alineación con las dimensiones de calidad de datos, así como la implementación de controles de calidad de datos.\n\n### Preguntas:\n\n1. **¿Cómo se relacionan los principios de ALCOA+ con las dimensiones de calidad de datos en el marco de calidad de datos (DQF) presentado?**\n - Esta pregunta busca explorar la conexión entre los principios de ALCOA+ y cómo se integran en el marco de calidad de datos, lo que no se detalla explícitamente en otros documentos.\n\n2. **¿Qué ejemplos de implementación de la norma ISO 25012 se han documentado y cómo se alinean con las regulaciones europeas?**\n - Esta pregunta se centra en ejemplos concretos de cómo se ha aplicado la norma ISO 25012, específicamente en el contexto de la regulación de medicamentos en la UE, lo que puede no estar ampliamente disponible en otras fuentes.\n\n3. **¿Cuáles son los desafíos asociados con la validación de datos en el contexto de la calidad de datos y cómo se pueden abordar?**\n - Esta pregunta busca profundizar en los problemas prácticos que surgen al validar datos en el marco de calidad de datos, así como las posibles soluciones, un aspecto que puede no ser ampliamente discutido en otros documentos sobre calidad de datos.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Marco de Calidad de Datos (DQF)**: El documento se centra en la implementación de un marco de calidad de datos en la regulación de medicamentos de la UE, enfatizando la importancia de la calificación y validación de sistemas informáticos.\n\n2. **Guía EMA/226170/2021**: Proporciona directrices sobre el uso de sistemas informatizados y datos electrónicos en ensayos clínicos, estableciendo un marco que puede ser adoptado más ampliamente para asegurar la calidad y conformidad en la gestión de datos.\n\n3. **Sistemas de Gestión de Calidad (QMS)**: Se discuten los elementos clave para implementar un QMS en el contexto del DQF, incluyendo la calificación y validación de sistemas, controles de proceso, documentación, gestión de acceso, gestión de proveedores y auditorías.\n\n4. **Normas ISO**:\n - **ISO 9000**: Establece estándares para sistemas de gestión de calidad en todos los niveles de una organización, aplicable si no existe un QMS específico de la industria.\n - **ISO 8000**: Define estándares para la calidad de los datos maestros y su intercambio entre sistemas, abordando la gobernanza de datos, gestión de calidad de datos y evaluación de calidad.\n\n5. **Calidad de Datos Maestros**: La norma ISO 8000 se centra en cómo los datos maestros deben cumplir con especificaciones formales y utilizar identificadores específicos para asegurar la calidad de los datos.\n\n6. **Decisión sobre QMS**: Se menciona un árbol de decisiones que guía sobre cómo considerar la adopción de QMS en la implementación del DQF.\n\n### Entidades Clave:\n- **EMA (Agencia Europea de Medicamentos)**: Organismo regulador que proporciona directrices sobre ensayos clínicos y sistemas informatizados.\n- **ISO (Organización Internacional de Normalización)**: Cuerpo que desarrolla estándares internacionales, incluyendo ISO 9000 y ISO 8000.\n- **Sistemas Informáticos**: Herramientas tecnológicas que requieren calificación y validación para su uso en el contexto regulatorio. \n\nEste resumen destaca la importancia de la calidad de los datos y la regulación en el ámbito de los medicamentos, así como el papel de las normas ISO en la gestión de calidad.", + "excerpt_keywords": "Keywords: data quality, ALCOA+, ISO standards, regulatory compliance, pharmaceutical industry", + "_node_type": "TextNode", + "document_id": "1ef01661-7bc9-49a5-be76-8995c6ee59e9", + "doc_id": "1ef01661-7bc9-49a5-be76-8995c6ee59e9", + "ref_doc_id": "1ef01661-7bc9-49a5-be76-8995c6ee59e9" + }, + "90245005-8dc3-42b8-a5f4-4e5d80984976": { + "page_label": "38", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [ + "https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page38-1.jpg" + ], + "parsed_text_markdown": "facts are not available in machine-readable structured form. Alternatively, data can be tested via intrinsic plausibility metrics, and specifically by assessing the dataset respect to (See figure 7):\n\n**Other data in the same dataset:** The test would detect logical or factual contradictions (e.g., embedding background knowledge on relations between entities and events). For example, the timing of a causal effect must occur after its causing intervention, or a female patients cannot have observations only occurring in males.\n\n**External reference ranges** (or Gold Standards): Some measured quantity cannot exceed a certain magnitude, such as a blood pressure of 1000/500 mmHg.\n\n**Plausible trends:** certain data can be valid when observed individually, but the collective trend of all data of a kind should follow expected distributions or trends. For example, the incidence of a disease is unlikely to grow drastically from 2% to 80% in a population from one year to another, or the exposed cell line in an experiment cannot show less effect than the unexposed comparator. In this case data are assessed with respect to background knowledge on typical characteristics of data.\n\n| | | |\n| ---------------------- | --- | -------------------------------------------------------------------------------------------- |\n| True facts accessible? | Yes | Implement system of external validation |\n| | No | Implement testing of data against other data, external reference ranges and plausible trends |\n\n*Figure 7 – Overview of external reference ranges*\n![page38-1.jpg](https://huggingface.co/spaces/pharma-IA/PharmaWise_5.0_DataIntegrity/resolve/main/vec_37_di/storage_images/page38-1.jpg)", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas que el contexto puede responder, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento aborda la calidad de los datos en el contexto de la regulación de medicamentos en la UE. Se discuten métricas de plausibilidad intrínseca para evaluar la calidad de los datos, incluyendo la detección de contradicciones lógicas, el uso de rangos de referencia externos (o estándares de oro) y la identificación de tendencias plausibles en los datos. Se presenta una tabla que sugiere acciones a tomar dependiendo de si los hechos verdaderos son accesibles o no.\n\n### Preguntas específicas:\n1. **¿Qué tipo de contradicciones lógicas se pueden detectar al evaluar un conjunto de datos en el contexto de la regulación de medicamentos?**\n - El contexto menciona ejemplos como la necesidad de que el efecto causal ocurra después de la intervención que lo causa, o que una paciente femenina no puede tener observaciones que solo ocurren en pacientes masculinos.\n\n2. **¿Cuáles son algunos ejemplos de rangos de referencia externos que se pueden utilizar para validar datos en estudios clínicos?**\n - Se menciona que ciertos valores medidos, como la presión arterial, no pueden exceder magnitudes específicas, como un valor de 1000/500 mmHg, lo que sirve como un estándar de oro para la validación.\n\n3. **¿Qué tendencias se consideran plausibles al evaluar la calidad de los datos en estudios de salud?**\n - El documento indica que, por ejemplo, la incidencia de una enfermedad no debería aumentar drásticamente de un 2% a un 80% en un solo año, y que los efectos en líneas celulares expuestas no deberían ser menores que los de los comparadores no expuestos.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\n1. **Marco de Calidad de Datos (DQF)**:\n - Se destaca la importancia de un marco estructurado para asegurar la calidad de los datos en la regulación de medicamentos en la UE.\n\n2. **Normas ISO**:\n - **ISO 25012**: Define un modelo general de calidad de datos para datos estructurados, aplicable a la toma de decisiones regulatorias. Proporciona un marco para establecer requisitos y medidas de calidad de datos, así como un plan para evaluaciones de calidad.\n - **ISO 13485**: Especifica los requisitos para un sistema de gestión de calidad (QMS) en la fabricación de dispositivos médicos, asegurando que cumplan con los requisitos del cliente y regulaciones.\n\n3. **ALCOA+**:\n - Un marco para la integridad de los datos en la industria farmacéutica, que establece principios que los datos deben cumplir: Atribuible, Legible, Contemporáneo, Original, Preciso (ALCOA) y atributos adicionales: Consistente, Duradero, Disponible, Trazable.\n - Se relaciona con las dimensiones de calidad de datos, especialmente en términos de fiabilidad, extensividad, coherencia y puntualidad.\n\n4. **Implementación de Controles de Calidad de Datos**:\n - Se discuten diferentes enfoques para implementar controles de calidad de datos, incluyendo la validación de datos contra registros fuente, aunque se reconoce que esto puede ser costoso y requerir tiempo.\n\n### Entidades Clave:\n- **Estándares ISO**: ISO 25012, ISO 13485.\n- **Principios ALCOA+**: Atribuible, Legible, Contemporáneo, Original, Preciso, Consistente, Duradero, Disponible, Trazable.\n- **Organización**: Statistics Finland (ejemplo de implementación de ISO 25012).\n- **Dimensiones de Calidad de Datos**: Fiabilidad, Extensividad, Coherencia, Puntualidad. \n\nEste resumen encapsula los elementos esenciales del contenido, proporcionando una visión clara de los estándares y marcos discutidos en el contexto de la calidad de datos en la regulación de medicamentos.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, plausibility metrics, external reference ranges, logical contradictions", + "_node_type": "TextNode", + "document_id": "f977496a-33ed-4f15-bb80-a40ad349dbcc", + "doc_id": "f977496a-33ed-4f15-bb80-a40ad349dbcc", + "ref_doc_id": "f977496a-33ed-4f15-bb80-a40ad349dbcc" + }, + "bd26bc9b-c6a1-46d8-80e4-e7c37498b008": { + "page_label": "39", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Glossary\n\nThis glossary addresses the main terms and definitions that have been used in this Data Quality Framework for regulatory decision making.\n\n| Definitions | Explanation |\n| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| Data accessibility | The ability of data to be accessible for public use in terms of discoverability, exportability, and usability. |\n| Data conciseness | The characteristic of data to be expressed in a compact representation. Sometimes also defined as the characteristic of data to include only essential, and not spurious, information. |\n| Data immutability | Data immutability is the concept that data is never deleted or altered. Once some data is “stated” (e.g.: entered in a database), it can only be augmented (eventually with additional information meant to invalidate or supersede previous data) but never remoted. In other words, data that has been entered in a system (and on which some other data or actions may depend) cannot be changed without explicitly mentioning of a new state of the information and maintaining the knowledge of the previous state. |\n| Data integrity | Data integrity refers to the maintenance and assurance of data reliability and consistency over time, encompassing the whole data life cycle. It is a broader concept than Data Quality, whose precise definition varies across contexts, extending from physical to logical aspects of data processing and storage. |\n| Data quality metrics | DQ metrics can be defined as indicators that can be applied to a data source to derive assessments of one or more quality dimensions. |\n| Data quality | Data quality is defined as fitness for purpose for users’ needs in relation to health research, policy making, and regulation and that the data reflect the reality, which they aim to represent. Data quality is relative to the research question and does not address the question on what level is the quality measured e.g., variable, data source or institutional level. These aspects are addressed in the data quality determinants and dimensions of data quality. |\n| Data quality determinants | What contributes to data quality or its characterisation. In this Framework determinants are classified into three categories:- **Foundational determinants:** what affects the quality of a dataset, being external to the dataset itself (e.g., systems and processes that generate data).\n- **Intrinsic determinants:** what can be derived in terms of quality for a dataset itself, without information on how the data came to or its intended usage.\n- **Question specific determinants:** what affects the assessment of a dataset quality, that strictly depends on the dataset intended or actual usage. |", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden formular a partir del contexto proporcionado, junto con sus respuestas específicas:\n\n1. **¿Cuáles son las tres categorías de determinantes de la calidad de los datos según el marco de calidad de datos para la regulación de medicamentos en la UE?**\n - **Respuesta:** Las tres categorías de determinantes de la calidad de los datos son:\n - **Determinantes fundamentales:** Factores externos que afectan la calidad de un conjunto de datos, como los sistemas y procesos que generan los datos.\n - **Determinantes intrínsecos:** Aspectos de calidad que se pueden derivar del propio conjunto de datos, sin información sobre cómo se obtuvieron o su uso previsto.\n - **Determinantes específicos de la pregunta:** Factores que afectan la evaluación de la calidad de un conjunto de datos, que dependen estrictamente del uso previsto o real del conjunto de datos.\n\n2. **¿Cómo se define la calidad de los datos en el contexto de la investigación en salud y la regulación?**\n - **Respuesta:** La calidad de los datos se define como la idoneidad para el propósito en relación con las necesidades de los usuarios en investigación en salud, formulación de políticas y regulación, y que los datos reflejan la realidad que pretenden representar. La calidad de los datos es relativa a la pregunta de investigación y no aborda en qué nivel se mide la calidad, como a nivel de variable, fuente de datos o institucional.\n\n3. **¿Qué implica el concepto de inmutabilidad de los datos en el marco de calidad de datos?**\n - **Respuesta:** La inmutabilidad de los datos implica que los datos nunca se eliminan ni se alteran una vez que se han ingresado en un sistema. Solo se pueden agregar datos adicionales que invaliden o sustituyan la información anterior, pero no se puede eliminar la información original. Esto significa que cualquier cambio en los datos debe ser documentado explícitamente, manteniendo un registro del estado anterior de la información.\n\n### Resumen de nivel superior:\nEl marco de calidad de datos para la regulación de medicamentos en la UE establece definiciones clave y conceptos relacionados con la calidad de los datos, que son esenciales para la toma de decisiones regulatorias. Se enfoca en la accesibilidad, concisión, inmutabilidad, integridad y métricas de calidad de los datos, así como en los determinantes que afectan la calidad de los datos. Este marco es fundamental para asegurar que los datos utilizados en la investigación y la regulación sean confiables y representativos de la realidad.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Calidad de los datos**: El documento se centra en la evaluación de la calidad de los datos en el contexto de la regulación de medicamentos en la UE, destacando la importancia de la plausibilidad intrínseca.\n\n2. **Métricas de plausibilidad**: Se mencionan diferentes métodos para evaluar la calidad de los datos, incluyendo:\n - **Contradicciones lógicas**: Se busca detectar inconsistencias en los datos, como la relación temporal entre causas y efectos, y la coherencia en las observaciones de pacientes de diferentes géneros.\n - **Rangos de referencia externos (estándares de oro)**: Se utilizan valores máximos aceptables para validar datos, como la presión arterial que no debe exceder 1000/500 mmHg.\n - **Tendencias plausibles**: Se evalúa si las tendencias observadas en los datos son razonables, como la incidencia de enfermedades que no debería cambiar drásticamente en un corto período.\n\n3. **Acciones recomendadas**: Se presenta una tabla que sugiere acciones a tomar dependiendo de si los hechos verdaderos son accesibles o no:\n - Si los hechos son accesibles, se recomienda implementar un sistema de validación externa.\n - Si no son accesibles, se sugiere realizar pruebas de datos contra otros datos, rangos de referencia externos y tendencias plausibles.\n\n4. **Entidades**:\n - **Datos**: Información relacionada con la salud y la regulación de medicamentos.\n - **Pacientes**: Referencias a pacientes masculinos y femeninos en el contexto de observaciones clínicas.\n - **Estándares de oro**: Valores de referencia utilizados para validar la precisión de los datos.\n\nEste resumen destaca la importancia de la evaluación rigurosa de los datos en la investigación y regulación de medicamentos, asegurando que sean lógicos, consistentes y plausibles.", + "excerpt_keywords": "Keywords: data quality, regulatory decision making, data integrity, data accessibility, data quality determinants", + "_node_type": "TextNode", + "document_id": "8af71da0-232b-476e-beb0-7333e2f20ee3", + "doc_id": "8af71da0-232b-476e-beb0-7333e2f20ee3", + "ref_doc_id": "8af71da0-232b-476e-beb0-7333e2f20ee3" + }, + "f18cfc05-ca61-4e71-8044-452c121b4765": { + "page_label": "40", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# Definitions and Explanations\n\n| Definitions | Explanation |\n| ------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Data quality dimensions | Data quality aspects are partitioned into different group that answers different questions about data. Such partitions are called “dimensions”. This Framework distinguishes five dimensions that can be divided further into sub-dimensions: 1) extensiveness, 2) coherence, 3) timeliness, 4) relevance, and 5) reliability. |\n| Data quality framework | A Data Quality Framework provides a set of definitions, guidelines, and recommendation to assess and govern data quality. The framework here presented addresses a wide range of data sources for the purpose of characterising, assessing, and assuring data quality for regulatory decision making. |\n| Entity | An entity is a collection of similar values that belong to a specific variable (e.g., weight). This is also referred to as row level. |\n| Healthcare data | Medical data gathered from different settings containing various clinical measurements of specific populations. In most cases this is electronically stored data known as electronic health data. |\n| Fit for purpose | Possessing all required data quality characteristic needed to address a specific goal. The emphasis of data quality is ensuring that the data are fit for purpose for reliable assessments of whether the data are fit for the purpose of decision making to supporting health research and population health. |\n| Foundational determinant | A data quality determinant that covers aspects related to the generation of data. It affects the quality of data, but it’s not part of the data themselves e.g., software systems, training, audit processes. It can be seen as data generation specific. |\n| Intrinsic determinant | A data quality determinant that covers aspects that are inherent to a given dataset e.g., the formatting of the data. This can be seen as a dataset specific determinant. |\n| Maturity model | A maturity model is a framework for assessing processes, technology and structure of an organisation or function. It provides a structured approach to evaluating how well an organization or a function manages its data quality processes, policies, and practices. The model defines key characteristics at each level to guide measure continuous improvement in data quality over time. |\n| MDM | Master data management, a system that helps synthesise data from different systems and secure and clean it (eliminates duplications etc) to deal with the right information. |\n| Metadata | Metadata are defined as “data about data” providing context about their purpose and generation. It’s a set of data that describes and gives information on other data providing context about their purpose, location, key-variables, generation, format, and ownership of a dataset. Metadata are often published in data catalogues, which have |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas que se pueden responder específicamente con la información proporcionada en el contexto, junto con un resumen de nivel superior:\n\n### Resumen de Nivel Superior\nEl documento presenta un marco de calidad de datos en el contexto de la regulación de medicamentos en la UE. Define varias dimensiones de calidad de datos, como la extensividad, coherencia, puntualidad, relevancia y fiabilidad. También se discuten determinantes de calidad de datos, tanto intrínsecos como fundamentales, y se menciona la importancia de un modelo de madurez para evaluar y mejorar continuamente los procesos de calidad de datos en organizaciones relacionadas con la salud. Además, se aborda el concepto de gestión de datos maestros (MDM) y la relevancia de los metadatos.\n\n### Preguntas Específicas\n1. **¿Cuáles son las cinco dimensiones de calidad de datos identificadas en el marco y cómo se pueden subdividir?**\n - Esta pregunta se centra en las dimensiones específicas de calidad de datos mencionadas en el documento, proporcionando detalles sobre cada una y sus subdimensiones.\n\n2. **¿Qué papel juegan los determinantes fundamentales e intrínsecos en la calidad de los datos y cómo se diferencian entre sí?**\n - Esta pregunta busca una comprensión más profunda de los tipos de determinantes de calidad de datos, lo que permite distinguir entre los aspectos relacionados con la generación de datos y aquellos inherentes a los conjuntos de datos.\n\n3. **¿Cómo puede un modelo de madurez ayudar a una organización a mejorar sus procesos de calidad de datos a lo largo del tiempo?**\n - Esta pregunta explora la aplicación práctica del modelo de madurez en la gestión de la calidad de datos, destacando su importancia para la evaluación y mejora continua dentro de una organización.", + "prev_section_summary": "### Resumen de la Sección: Glosario del Marco de Calidad de Datos para la Regulación de Medicamentos en la UE\n\nLa sección proporciona definiciones clave y conceptos relacionados con la calidad de los datos, esenciales para la toma de decisiones regulatorias en el ámbito de la salud. A continuación se presentan los temas clave y entidades mencionadas:\n\n1. **Accesibilidad de los Datos**: Se refiere a la capacidad de los datos para ser accesibles al público, considerando su descubribilidad, exportabilidad y usabilidad.\n\n2. **Concisión de los Datos**: Describe la característica de los datos de ser expresados de manera compacta, incluyendo solo la información esencial y evitando datos espurios.\n\n3. **Inmutabilidad de los Datos**: Concepto que establece que los datos no pueden ser eliminados ni alterados una vez ingresados en un sistema. Solo se pueden agregar datos adicionales que invaliden o sustituyan la información anterior, manteniendo un registro del estado previo.\n\n4. **Integridad de los Datos**: Se refiere al mantenimiento y aseguramiento de la fiabilidad y consistencia de los datos a lo largo del tiempo, abarcando todo el ciclo de vida de los datos.\n\n5. **Métricas de Calidad de Datos**: Indicadores que se pueden aplicar a una fuente de datos para evaluar una o más dimensiones de calidad.\n\n6. **Calidad de los Datos**: Definida como la idoneidad de los datos para satisfacer las necesidades de los usuarios en investigación en salud, formulación de políticas y regulación, reflejando la realidad que pretenden representar.\n\n7. **Determinantes de la Calidad de los Datos**: Factores que contribuyen a la calidad de los datos, clasificados en tres categorías:\n - **Determinantes Fundamentales**: Factores externos que afectan la calidad de un conjunto de datos.\n - **Determinantes Intrínsecos**: Aspectos de calidad que se derivan del propio conjunto de datos.\n - **Determinantes Específicos de la Pregunta**: Factores que afectan la evaluación de la calidad de un conjunto de datos, dependiendo de su uso previsto o real.\n\n### Conclusión\nEl glosario establece un marco conceptual que es fundamental para asegurar que los datos utilizados en la investigación y regulación de medicamentos sean confiables y representativos de la realidad, facilitando así la toma de decisiones informadas en el ámbito de la salud pública.", + "excerpt_keywords": "Keywords: data quality, healthcare data, maturity model, metadata, regulatory decision making", + "_node_type": "TextNode", + "document_id": "e18010da-3f80-46f1-ab45-0ef74539b7e0", + "doc_id": "e18010da-3f80-46f1-ab45-0ef74539b7e0", + "ref_doc_id": "e18010da-3f80-46f1-ab45-0ef74539b7e0" + }, + "06b326fb-fbac-4566-8f2a-a5cbdd7c6bb7": { + "page_label": "41", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "| Definitions | Explanation |\n| --------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| the purpose of allowing data to be discoverable and checked for fitness for purpose, without revealing the data themselves. | |\n| Primary use of data | Primary use of (electronic) health data is the processing of personal health data for the provision of health services to assess, maintain or restore the state of health of the person it belongs to, including the prescription, dispensation and provision of medicinal products and medical devices, as well as for relevant social security, administrative or reimbursement services. |\n| Secondary use of data | Secondary use of (electronic) health data is the processing of health data for other purposes rather than primary use such as national statistics, education/teaching, scientific research etc. The data used may include personal health data initially collected in the context of primary use, but also electronic health data collected for the purpose of secondary use. |\n", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "Aquí tienes tres preguntas específicas basadas en el contexto proporcionado, junto con un resumen de nivel superior:\n\n### Resumen de nivel superior:\nEl documento \"Data quality framework EU medicines regulation\" aborda la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se definen dos tipos de uso de datos: el uso primario, que se refiere al procesamiento de datos de salud personales para la provisión de servicios de salud, y el uso secundario, que implica el procesamiento de datos de salud para otros fines, como estadísticas nacionales, educación y investigación científica. Además, se menciona la importancia de permitir que los datos sean descubribles y verificables en cuanto a su idoneidad para el propósito, sin revelar los datos en sí.\n\n### Preguntas específicas:\n1. **¿Cuál es la diferencia clave entre el uso primario y el uso secundario de los datos de salud según el marco de calidad de datos de la regulación de medicamentos de la UE?**\n - Esta pregunta busca una comparación directa entre los dos tipos de uso de datos, lo que no se detalla en otras fuentes.\n\n2. **¿Qué implicaciones tiene la capacidad de hacer que los datos sean descubribles y verificables para la calidad de los datos en el contexto de la regulación de medicamentos?**\n - Esta pregunta se centra en la importancia de la transparencia y la verificación de datos en el ámbito regulatorio, un aspecto que puede no estar ampliamente discutido en otros documentos.\n\n3. **¿Qué tipos de datos pueden ser utilizados en el contexto del uso secundario, y cómo se relacionan con los datos recolectados para el uso primario?**\n - Esta pregunta explora la relación entre los datos recolectados para diferentes propósitos y cómo se pueden reutilizar, un tema que puede no estar claramente definido en otras fuentes.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nEl documento \"Data quality framework EU medicines regulation\" presenta un marco integral para la calidad de datos en el contexto de la regulación de medicamentos en la Unión Europea. A continuación se resumen los temas clave y las entidades mencionadas en la sección:\n\n#### Temas Clave\n\n1. **Dimensiones de Calidad de Datos**: Se identifican cinco dimensiones principales que son:\n - **Extensividad**: Grado en que se recopilan datos completos.\n - **Coherencia**: Consistencia de los datos a través de diferentes fuentes.\n - **Puntualidad**: Disponibilidad de datos en el momento adecuado.\n - **Relevancia**: Pertinencia de los datos para el propósito específico.\n - **Fiabilidad**: Confianza en la precisión y veracidad de los datos.\n\n2. **Marco de Calidad de Datos**: Proporciona definiciones, directrices y recomendaciones para evaluar y gobernar la calidad de los datos, abarcando diversas fuentes de datos para la toma de decisiones regulatorias.\n\n3. **Determinantes de Calidad de Datos**:\n - **Determinante Fundamental**: Aspectos relacionados con la generación de datos, como sistemas de software y procesos de auditoría.\n - **Determinante Intrínseco**: Aspectos inherentes a un conjunto de datos, como su formato.\n\n4. **Modelo de Madurez**: Un marco para evaluar y mejorar continuamente los procesos y políticas de calidad de datos dentro de una organización, definiendo características clave en cada nivel.\n\n5. **Gestión de Datos Maestros (MDM)**: Sistema que ayuda a sintetizar y limpiar datos de diferentes sistemas para asegurar la calidad de la información.\n\n6. **Metadatos**: Datos que proporcionan contexto sobre otros datos, incluyendo su propósito, ubicación y formato, y que son cruciales para la gestión y comprensión de los conjuntos de datos.\n\n#### Entidades\n\n- **Datos de Salud**: Datos médicos recopilados de diversas configuraciones, generalmente almacenados electrónicamente.\n- **Entidad**: Colección de valores similares que pertenecen a una variable específica, representando el nivel de fila en un conjunto de datos.\n\nEste marco es esencial para garantizar que los datos utilizados en la regulación de medicamentos sean de alta calidad, lo que a su vez apoya la investigación en salud y la toma de decisiones informadas.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, primary use, secondary use, health data", + "_node_type": "TextNode", + "document_id": "24871504-3ce8-49bc-824c-3834a986dbc9", + "doc_id": "24871504-3ce8-49bc-824c-3834a986dbc9", + "ref_doc_id": "24871504-3ce8-49bc-824c-3834a986dbc9" + }, + "bf80c6f4-fba9-41b1-92fc-943b01188f2a": { + "page_label": "42", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# References\n\n1. European Medicines Regulatory Network Data Standardisation Strategy. December 16th, 2021. Available from: https://www.ema.europa.eu/en/documents/other/european-medicines-regulatory-network-data-standardisation-strategy_en.pdf.\n\n2. European Health Data Space Data Quality Framework. Deliverable 6.1 of TEHDAS EU 3rd Health Program (GA: 101035467). May 18th, 2022, accessed at https://tehdas.eu/results/tehdas-develops-data-quality-recommendations.\n\n3. ISO 9001:2015 Quality Management System, accessed November 18th, 2022. https://www.iso.org/standard/62085.html.\n\n4. Kahn, M.G., et al., A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC), 2016. 4(1): p. 1244.\n\n5. Healthcare Data Quality: A 4-Level Actionable Framework 2020 [September 5th, 2022]. Available from: https://www.healthcatalyst.com/insights/healthcare-data-quality-4-level-actionable-framework.\n\n6. Schmidt, C.O., et al., Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R. BMC Med Res Method, 2021. 21(1): p. 63.\n\n7. Sentinel QA Program. Quality Assurance - Sentinel Version Control System. [February 14th, 2022]. Available from: https://dev.sentinelsystem.org/projects/QA/repos/qa_package/browse.\n\n8. NESTcc. Data Quality Framework. A report of the Data Quality Subcommittee of the NEST Coordinating Center - An initiative of MDIC. 2020 [February 14th, 2022]. Available from: https://nestcc.org/nestcc-data-quality-framework.\n\n9. The National Patient-Centered Clinical Research Network. PCORnet - Data Quality Framework. [February 18th, 2022]. Available from: https://pcornet.org/data.\n\n10. Duke-Margolis Center for Health Policy. Characterising RWD Quality and Relevance for Regulatory Purposes, 2018. Available from: https://healthpolicy.duke.edu/sites/default/files/2020-03/characterising_rwd.pdf.\n\n11. Duke-Margolis Center for Health Policy. Determining Real-World Data’s Fitness for Use and the Role of Reliability, 2019. Available from: https://healthpolicy.duke.edu/sites/default/files/2019-11/rwd_reliability.pdf.\n\n12. Data Utility Framework. Available from: https://www.hdruk.ac.uk/wp-content/uploads/2020/11/201105-Updates-to-the-Data-Utility-Framework-v2.pdf.\n\n13. Statistics Finland, Data Quality Framework, National data quality criteria and indicators. Available from: https://stat.fi/org/tiedon-laatukehikko/tiedon-laatukriteerit_en.html.\n\n14. Cave, A., X. Kurz, and P. Arlett, Real-World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe. Clin Pharmacol Ther, 2019. 106(1): p. 36-39.\n\n15. Big Data Steering Group, Big Data Workplan 2022-2025. Available from: https://www.ema.europa.eu/en/news/big-data-use-public-health-publication-big-data-steering-group-workplan-2022-25.\n\n16. FAIR. Available from: https://www.go-fair.org/fair-principles/.\n\n17. Good Practice Guide for the use of the Metadata Catalogue of Real-World Data Sources, EMA/786747/2022. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/good-practice-guide-use-metadata-catalogue-real-world-data-sources_en.pdf.\n\n18. Wang, S.V. and S. Schneeweiss, A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data. Clin Epidemiol, 2022. 14: p. 601-608.\n\n19. European Commission. Can we use data for another purpose? [Internet, cited 27 Sept 2022] https://ec.europa.eu/info/law/law-topic/data-protection/reform/rules-business-and-organisations/principles-gdpr/purpose-data-processing/can-we-use-data-another-purpose_en.\n\n20. American Society for Quality. What is a Quality Management System (QMS)? | ASQ. Available from: https://asq.org/quality-resources/quality-management-system.\n\n21. Guideline on computerised systems and electronic data in clinical trials. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/draft-guideline-computerised-systems-electronic-data-clinical-trials_en.pdf.\n\n22. Data Integrity and Compliance with cGMP, guidance for Industry. Available from: https://www.fda.gov/files/drugs/published/Data-Integrity-and-Compliance-With-Current-Good-Manufacturing-Practice-Guidance-for-Industry.pdf.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resumen del Contexto\n\nEl documento titulado \"Data quality framework EU medicines regulation\" presenta un marco de calidad de datos en el contexto de la regulación de medicamentos en la Unión Europea. Incluye referencias a diversas estrategias, guías y marcos relacionados con la calidad de los datos en el ámbito de la salud, así como la importancia de la estandarización y la evaluación de la calidad de los datos para la toma de decisiones regulatorias. Se mencionan iniciativas de diferentes organizaciones y programas que buscan mejorar la calidad de los datos utilizados en la investigación y la regulación de medicamentos.\n\n### Preguntas Específicas\n\n1. **¿Cuáles son los principales objetivos de la \"European Medicines Regulatory Network Data Standardisation Strategy\" mencionada en el documento?**\n - Esta pregunta busca obtener detalles específicos sobre los objetivos y metas de la estrategia de estandarización de datos en el contexto de la regulación de medicamentos en la UE.\n\n2. **¿Qué criterios y métricas se utilizan en el \"Data Quality Framework\" de Statistics Finland para evaluar la calidad de los datos?**\n - Esta pregunta se enfoca en los criterios específicos que se aplican en el marco de calidad de datos de Statistics Finland, que podría no estar ampliamente disponible en otras fuentes.\n\n3. **¿Cómo se relacionan los principios FAIR con la calidad de los datos en el contexto de la regulación de medicamentos según el documento?**\n - Esta pregunta busca explorar la conexión entre los principios FAIR (Findable, Accessible, Interoperable, Reusable) y la calidad de los datos en el ámbito regulatorio, un aspecto que puede no ser evidente en otras discusiones sobre FAIR. \n\nEstas preguntas están diseñadas para profundizar en aspectos específicos del marco de calidad de datos y su aplicación en la regulación de medicamentos, proporcionando información que podría no estar fácilmente disponible en otras fuentes.", + "prev_section_summary": "### Resumen de temas clave y entidades:\n\n1. **Marco de Calidad de Datos**: El documento se centra en la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea.\n\n2. **Uso Primario de Datos**: Se refiere al procesamiento de datos de salud personales para la provisión de servicios de salud. Esto incluye actividades como la prescripción, dispensación y provisión de productos medicinales y dispositivos médicos, así como servicios relacionados con la seguridad social y la administración.\n\n3. **Uso Secundario de Datos**: Implica el procesamiento de datos de salud para fines distintos al uso primario, como la elaboración de estadísticas nacionales, la educación, la enseñanza y la investigación científica. Este uso puede incluir datos de salud personal recolectados inicialmente para el uso primario, así como datos recolectados específicamente para el uso secundario.\n\n4. **Descubribilidad y Verificación de Datos**: Se destaca la importancia de permitir que los datos sean descubribles y verificables en cuanto a su idoneidad para un propósito específico, sin necesidad de revelar los datos en sí. Esto es crucial para garantizar la calidad y la confianza en los datos utilizados en el ámbito regulatorio.\n\n### Entidades Clave:\n- **Datos de Salud Electrónicos**: Incluyen tanto datos personales como datos recolectados para usos secundarios.\n- **Servicios de Salud**: Comprenden la atención médica, la prescripción y la administración de productos y servicios relacionados con la salud.\n- **Investigación Científica y Estadísticas**: Ejemplos de usos secundarios de los datos de salud.\n\nEste resumen encapsula los conceptos fundamentales y las entidades relevantes discutidas en la sección del documento.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, health data, standardization, regulatory framework", + "_node_type": "TextNode", + "document_id": "e1969a35-dc91-4c1b-a3dc-f6e39e41adf1", + "doc_id": "e1969a35-dc91-4c1b-a3dc-f6e39e41adf1", + "ref_doc_id": "e1969a35-dc91-4c1b-a3dc-f6e39e41adf1" + }, + "4c31a1dc-04cb-4556-8f7e-4b6f48b1939a": { + "page_label": "42", + "file_name": "[37] Data quality framework EU medicines regulation.pdf", + "file_path": "raw_data/[37] Data quality framework EU medicines regulation.pdf", + "image_path": [], + "parsed_text_markdown": "# References\n\n1. European Medicines Regulatory Network Data Standardisation Strategy. December 16th, 2021. Available from: https://www.ema.europa.eu/en/documents/other/european-medicines-regulatory-network-data-standardisation-strategy_en.pdf.\n\n2. European Health Data Space Data Quality Framework. Deliverable 6.1 of TEHDAS EU 3rd Health Program (GA: 101035467). May 18th, 2022, accessed at https://tehdas.eu/results/tehdas-develops-data-quality-recommendations.\n\n3. ISO 9001:2015 Quality Management System, accessed November 18th, 2022. https://www.iso.org/standard/62085.html.\n\n4. Kahn, M.G., et al., A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC), 2016. 4(1): p. 1244.\n\n5. Healthcare Data Quality: A 4-Level Actionable Framework 2020 [September 5th, 2022]. Available from: https://www.healthcatalyst.com/insights/healthcare-data-quality-4-level-actionable-framework.\n\n6. Schmidt, C.O., et al., Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R. BMC Med Res Method, 2021. 21(1): p. 63.\n\n7. Sentinel QA Program. Quality Assurance - Sentinel Version Control System. [February 14th, 2022]. Available from: https://dev.sentinelsystem.org/projects/QA/repos/qa_package/browse.\n\n8. NESTcc. Data Quality Framework. A report of the Data Quality Subcommittee of the NEST Coordinating Center - An initiative of MDIC. 2020 [February 14th, 2022]. Available from: https://nestcc.org/nestcc-data-quality-framework.\n\n9. The National Patient-Centered Clinical Research Network. PCORnet - Data Quality Framework. [February 18th, 2022]. Available from: https://pcornet.org/data.\n\n10. Duke-Margolis Center for Health Policy. Characterising RWD Quality and Relevance for Regulatory Purposes, 2018. Available from: https://healthpolicy.duke.edu/sites/default/files/2020-03/characterising_rwd.pdf.\n\n11. Duke-Margolis Center for Health Policy. Determining Real-World Data’s Fitness for Use and the Role of Reliability, 2019. Available from: https://healthpolicy.duke.edu/sites/default/files/2019-11/rwd_reliability.pdf.\n\n12. Data Utility Framework. Available from: https://www.hdruk.ac.uk/wp-content/uploads/2020/11/201105-Updates-to-the-Data-Utility-Framework-v2.pdf.\n\n13. Statistics Finland, Data Quality Framework, National data quality criteria and indicators. Available from: https://stat.fi/org/tiedon-laatukehikko/tiedon-laatukriteerit_en.html.\n\n14. Cave, A., X. Kurz, and P. Arlett, Real-World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe. Clin Pharmacol Ther, 2019. 106(1): p. 36-39.\n\n15. Big Data Steering Group, Big Data Workplan 2022-2025. Available from: https://www.ema.europa.eu/en/news/big-data-use-public-health-publication-big-data-steering-group-workplan-2022-25.\n\n16. FAIR. Available from: https://www.go-fair.org/fair-principles/.\n\n17. Good Practice Guide for the use of the Metadata Catalogue of Real-World Data Sources, EMA/786747/2022. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/good-practice-guide-use-metadata-catalogue-real-world-data-sources_en.pdf.\n\n18. Wang, S.V. and S. Schneeweiss, A Framework for Visualizing Study Designs and Data Observability in Electronic Health Record Data. Clin Epidemiol, 2022. 14: p. 601-608.\n\n19. European Commission. Can we use data for another purpose? [Internet, cited 27 Sept 2022] https://ec.europa.eu/info/law/law-topic/data-protection/reform/rules-business-and-organisations/principles-gdpr/purpose-data-processing/can-we-use-data-another-purpose_en.\n\n20. American Society for Quality. What is a Quality Management System (QMS)? | ASQ. Available from: https://asq.org/quality-resources/quality-management-system.\n\n21. Guideline on computerised systems and electronic data in clinical trials. Available from: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/draft-guideline-computerised-systems-electronic-data-clinical-trials_en.pdf.\n\n22. Data Integrity and Compliance with cGMP, guidance for Industry. Available from: https://www.fda.gov/files/drugs/published/Data-Integrity-and-Compliance-With-Current-Good-Manufacturing-Practice-Guidance-for-Industry.pdf.", + "parsed_text": "", + "document_title": "Data quality framework EU medicines regulation", + "questions_this_excerpt_can_answer": "### Resumen del Contexto\n\nEl documento titulado \"Data quality framework EU medicines regulation\" aborda la importancia de la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. Se presentan diversas referencias que incluyen estrategias de estandarización de datos, marcos de calidad de datos, guías de buenas prácticas y principios de FAIR (Findable, Accessible, Interoperable, Reusable). Además, se discuten los desafíos y soluciones en el uso de datos del mundo real para la toma de decisiones regulatorias. El marco de calidad de datos es esencial para garantizar que los datos utilizados en la investigación y la regulación sean precisos, confiables y útiles.\n\n### Preguntas Específicas\n\n1. **¿Cuáles son los criterios nacionales de calidad de datos establecidos por Statistics Finland y cómo se aplican en el contexto de la regulación de medicamentos en la UE?**\n - Esta pregunta busca información específica sobre los criterios de calidad de datos de Statistics Finland, que no se detalla en otras referencias.\n\n2. **¿Qué desafíos específicos se identifican en el artículo de Cave, Kurz y Arlett sobre el uso de datos del mundo real para la toma de decisiones regulatorias en Europa?**\n - Esta pregunta se centra en los desafíos mencionados en el artículo, proporcionando una visión más profunda de los problemas que enfrenta la regulación de medicamentos.\n\n3. **¿Cómo se define y aplica el concepto de \"Data Integrity\" en la guía de la FDA sobre la conformidad con las Buenas Prácticas de Manufactura (cGMP) en el contexto de los datos utilizados en ensayos clínicos?**\n - Esta pregunta busca una comprensión detallada de cómo se relaciona la integridad de los datos con las regulaciones de calidad en la industria farmacéutica, un aspecto que puede no estar ampliamente discutido en otros documentos.\n\n### Resumen de Nivel Superior\n\nEl marco de calidad de datos en la regulación de medicamentos de la UE es fundamental para asegurar que los datos utilizados en la investigación y la toma de decisiones sean de alta calidad. Se hace hincapié en la estandarización de datos y en la implementación de buenas prácticas para garantizar la integridad y la utilidad de los datos. Las referencias citadas ofrecen una variedad de enfoques y guías que abordan la calidad de los datos desde diferentes perspectivas, incluyendo la investigación observacional y el uso de datos del mundo real.", + "prev_section_summary": "### Resumen de Temas Clave y Entidades\n\nLa sección de referencias del documento \"Data quality framework EU medicines regulation\" aborda diversos aspectos relacionados con la calidad de los datos en el contexto de la regulación de medicamentos en la Unión Europea. A continuación se presentan los temas clave y las entidades mencionadas:\n\n#### Temas Clave:\n1. **Estandarización de Datos**: Se menciona la \"European Medicines Regulatory Network Data Standardisation Strategy\", que busca establecer un marco común para la estandarización de datos en la regulación de medicamentos.\n \n2. **Calidad de Datos en Salud**: Se hace referencia al \"European Health Data Space Data Quality Framework\" y otros marcos que proporcionan recomendaciones y criterios para evaluar la calidad de los datos en la investigación y la regulación de medicamentos.\n\n3. **Normas de Calidad**: Se citan estándares como ISO 9001:2015, que establece requisitos para un sistema de gestión de calidad, y guías sobre la integridad de los datos y el cumplimiento de buenas prácticas de fabricación.\n\n4. **Evaluación de Calidad de Datos**: Se discuten metodologías y marcos para la evaluación de la calidad de los datos, incluyendo terminologías armonizadas y enfoques prácticos para la investigación en salud.\n\n5. **Principios FAIR**: Se menciona la importancia de los principios FAIR (Findable, Accessible, Interoperable, Reusable) en la mejora de la calidad de los datos y su aplicabilidad en el contexto regulatorio.\n\n6. **Iniciativas y Programas**: Se citan diversas iniciativas y programas, como el NEST Coordinating Center y el PCORnet, que trabajan en el desarrollo de marcos de calidad de datos y en la mejora de la calidad de los datos utilizados en la investigación clínica.\n\n#### Entidades Mencionadas:\n- **European Medicines Agency (EMA)**: Organismo regulador que proporciona directrices y marcos para la calidad de los datos en la regulación de medicamentos.\n- **Statistics Finland**: Entidad que establece criterios y métricas para la calidad de los datos en su marco de calidad.\n- **Duke-Margolis Center for Health Policy**: Centro que investiga la calidad y relevancia de los datos del mundo real para fines regulatorios.\n- **NESTcc**: Iniciativa que se centra en la calidad de los datos en el contexto de la investigación clínica.\n- **PCORnet**: Red que promueve la calidad de los datos en la investigación centrada en el paciente.\n\nEste resumen destaca la importancia de la calidad de los datos en la regulación de medicamentos y las diversas iniciativas y marcos que buscan mejorar esta calidad en el ámbito de la salud.", + "excerpt_keywords": "Keywords: data quality, EU medicines regulation, standardization, real-world data, compliance", + "_node_type": "TextNode", + "document_id": "e1969a35-dc91-4c1b-a3dc-f6e39e41adf1", + "doc_id": "e1969a35-dc91-4c1b-a3dc-f6e39e41adf1", + "ref_doc_id": "e1969a35-dc91-4c1b-a3dc-f6e39e41adf1" + } + } +} \ No newline at end of file