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A selection of (unanswered) questions, generalized from the literature: |
* How can we tell if AI-PROMs are designed and implemented successfully? |
* How can AI systems be designed to incorporate and analyze patient-reported outcome measures effectively? |
* What types of PROMs would best suit the target population for a specific study or intervention, considering factors such as disease severity, age range, cultural background, etc.? |
* Are there existing validated PROMs that can be used in the context of AI-based interventions, and how do we ensure their applicability to the given scenario? |
* How can patients be engaged as partners in designing, testing, and implementing AI-based healthcare solutions that incorporate their experiences and perspectives? |
* How has value-based healthcare influenced the development of AI in healthcare? |
* What proxies are available—for experience? For expertise? |
* What are the impacts of AI-PROMs on patient outcomes, satisfaction, and overall healthcare costs? |
* How feasible and effective it is to integrate AI-PROMs into existing clinical workflows? To adjust treatment dynamically, based on patterns in patient-reported feedback? |
Conclusion: |
The current landscape for both AI and PROMs is characterized by increased adoption and efforts to increase standardization and interoperability. While challenges remain, the benefits of using AI and PROMs to improve patient outcomes and healthcare quality make them each an essential component of modern healthcare. |
The integration of AI with PROMs has shown promising results in enhancing healthcare services, improving patient outcomes, and optimizing treatment decisions. |
By applying AI tools to the analysis of PROMs, researchers gain a more holistic understanding of the complex relationships between diverse inputs (such as demographics, medical history, and treatment plans) and outputs (patient experiences and quality of life). This information may enable healthcare providers to make b... |
Conversely, the use of AI algorithms that incorporate PROM data as an input ensures the inclusion of patients' perspectives in AI-based predictions. This persistent validation of patient experience may help to bridge gaps between patients and healthcare providers, facilitating more transparent communication about patie... |
Suggestions for future work: |
Further review of the literature should contain an expanded bibliometric analysis: To what extent does the literature build on a shared research tradition, with shared assumptions? Are there divergent assumptions motivating the research? How collaborative is the research, between and within disciplines? How ‘common’ ar... |
Ethics: Continue evaluation of the ethical implications of using AI and PROMs together, with particular attention to the underlying ethical assumptions that ground issues around patient autonomy, privacy, data ownership, algorithmic transparency, and the potential exacerbation of health disparities due to biased algori... |
Safety: Develop guidelines and best practices for protecting patient privacy when using PROMs data as training data for AI models. |
Real-world implementation: Conduct pilot studies or small-scale implementations of AI-PROM systems in real-world clinical settings to evaluate feasibility, effectiveness, and scalability. A literature review that uses a realist synthesis methodology would provide guidance for implementing and evaluating the technologie... |
Multidisciplinary collaboration: Foster collaborations between clinicians, researchers, data scientists, industry experts, and regulatory bodies to develop solutions for integrating PROMs with AI, and to build consensus around standardized protocols and guidelines. |
Standardization: Establish common standards for collecting, processing, and integrating PGHD with AI. Make use of existing secure standards for data interoperability such as FHIR. Harmonize standards for access and ethical shared use of AI-PROMs across studies, countries, and fields of study. Support for innovative de... |
Apply AI-PROMs to new domains: AI-PROM systems may be particularly well-suited to address unmet needs in areas such as mental health, palliative care, and rare diseases. AI-PROMs may be used to assess the effectiveness of interventions or treatments that do not have established PROMs measures, as well as to expand the ... |
Addendum |
Figures: |
Fig 1: Features of a hypothetical AI-PROM as a flow diagram. Patients, providers, and AI are all in a shared loop, with personalized, adaptable, efficient, accessible, and effective care as the intended goals. |
Bibliographic Notes |
(link to bibliography of included references) |
Total references included: |
n=537 |
Unique sources (Journals, Books, Conferences, etc): |
n=318 |
Individual authors: |
n=3863 |
Publications by year: |
2020 |
67 |
2021 |
112 |
2022 |
129 |
2023 |
158 |
2024 (1/1—4/10) |
71 |
Publications by type: |
peer-reviewed articles |
410 |
reviews |
69 |
conference papers |
17 |
book chapters |
10 |
protocols |
21 |
preprints |
6 |
editorials |
4 |
Research Areas |
Research Area |
Count |
Machine Learning |
261 |
Health Informatics |
162 |
Oncology |
112 |
Orthopedics |
58 |
Medical Informatics |
51 |
Health Care Sciences & Services |
49 |
Surgery |
40 |
Neurosciences & Neurology |
25 |
General & Internal Medicine |
23 |
Sport Sciences |
23 |
Computer Science |
21 |
Engineering |
19 |
Gastroenterology & Hepatology |
12 |
Cardiovascular System & Cardiology |
11 |
Science & Technology - Other Topics |
11 |
Public, Environmental & Occupational Health |
10 |
Rheumatology |
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