Datasets:
tags:
- object detection
- image classification
- nursing training
- medical quality assessment
- procedure workflow optimization
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
language:
- en
pretty_name: Nursing Procedure Workflow Dataset
size_categories:
- 1B<n<10B
Nursing Procedure Workflow Dataset
The current healthcare industry faces challenges with insufficient standardization of nursing procedures and suboptimal training effectiveness. Existing solutions often lack specificity, resulting in inconsistent nursing quality. This dataset aims to provide high-quality nursing procedure workflow data to support the training of object detection models, thereby improving the standardization level of nursing procedures and enhancing training effectiveness. Data collection is conducted using professional medical equipment in real hospital environments to ensure the data's authenticity and validity. We have implemented multiple rounds of annotation and consistency checks, and all annotations are reviewed by experts to ensure high quality and accuracy. The data storage format is JPG, categorized by patient ID for convenient retrieval and use.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| nurse_action | string | The specific nursing action being executed in the image. |
| equipment_used | string | The medical equipment being used during the nursing action. |
| action_phase | string | The phase of the nursing action currently depicted in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com