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Development of a Nursing Diagnosis/Record Generative AI System Based on Virtual Patient Data
Dongkyun Lee1,2, Mihyeon Seong2, HongShin Ju2,3,4
1Ajou University College of Nursing.
Studies in Health Technology and Informatics
|July 25, 2024
Summary
Generative AI can reduce nurses' repetitive electronic nursing record tasks. This technology, trained on 50,000 records, shows promise in easing healthcare professionals' workload.
Area of Science:
- Nursing Informatics
- Artificial Intelligence in Healthcare
- Clinical Documentation
Background:
- Repetitive electronic nursing record tasks contribute to significant workload for nurses.
- Efficient clinical documentation is crucial for patient care and operational efficiency.
- Existing electronic nursing record systems may not fully address the burden of data entry.
Purpose of the Study:
- To investigate the application of generative AI for reducing repetitive electronic nursing record tasks.
- To evaluate the usefulness, usability, and availability of generative AI in nursing record creation.
- To assess the potential of generative AI to alleviate nurses' workload.
Main Methods:
- Generative AI was trained using 50,000 nursing record data points, including NANDA, FocusDAR, SOAPIE, and narrative formats, utilizing virtual patient data.
- The AI model's performance was enhanced through fine-tuning techniques.
- An API connected the generative AI to a practice electronic nursing record system for testing by 40 experienced nurses.
Main Results:
- The study demonstrated the potential of generative AI to automate and streamline the creation of electronic nursing records.
- Nurses' feedback indicated the system's potential usefulness and usability in practice.
- The integration of generative AI is expected to reduce the time spent on documentation.
Conclusions:
- Generative AI offers a viable solution for reducing the repetitive tasks associated with electronic nursing records.
- The technology has the potential to significantly ease the workload of nurses, allowing for more direct patient care.
- Further development and integration of generative AI in healthcare settings are warranted.
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