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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Towards an on-demand peer feedback system for a clinical knowledge base: a case study with order sets
Nathan C Hulse1, Guilherme Del Fiol, Richard L Bradshaw
1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. nathan.hulse@gmail.com
Journal of Biomedical Informatics
|June 27, 2007
Summary
An automated system for physician order entry (POE) templates suggests modifications, with 62% accepted by authors. This clinical knowledge feedback system enhances order set usability and accuracy.
Area of Science:
- Clinical Informatics
- Health Information Technology
- Knowledge Management
Background:
- Enterprise-wide knowledge bases require continuous refinement for clinical accuracy.
- Physician order entry (POE) systems generate valuable data for improving clinical content.
Purpose of the Study:
- To develop and evaluate an automated peer feedback system for clinical knowledge content.
- To facilitate the creation and refinement of sound order set templates within an enterprise knowledge base.
Main Methods:
- Collected and de-identified 2951 instances of POE-based orders.
- Developed a program to analyze order set template usage patterns and suggest modifications.
- Clinical content authors reviewed and accepted/rejected 500 suggested modifications.
Main Results:
- The automated system generated 500 modification suggestions across 30 order set templates.
- Overall, 62% of all suggested modifications were accepted by clinical content authors.
- Acceptance rates varied by suggestion type: 32% for additions, 74% for pre-selections, 76% for de-selections, and 68% for combo box order changes.
Conclusions:
- The automated knowledge base peer feedback system is effective in generating acceptable suggestions for order set refinement.
- The system demonstrates high utility in improving clinical knowledge content and order set usability.
- Further enhancements to the software are expected to increase its value.
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