Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement

• Published in: AMIA ... Annual Symposium proceedings Vol. 2024; p. 628
• Main Authors: Landis-Lewis, Zach, Cao, Yidan, Chung, Hana, Boisvert, Peter, Renji, Anjana Deep, Galante, Patrick, Jagadeesan, Ayshwarya, Seifi, Farid, Janda, Allison, Shah, Nirav, Krumm, Andrew, Flynn, Allen
• Format: Journal Article
• Language: English
• Published: United States 2024
• Subjects: Feedback; Formative Feedback; Health Personnel - education; Humans; Knowledge Bases; Learning; Learning Health System; Quality Improvement
• ISSN: 1942-597X; 1559-4076

Healthcare providers learn continuously, but better support for provider learning is needed as new biomedical knowledge is produced at an increasing rate alongside widespread use of EHR data for clinical performance measurement. Precision feedback is an approach to improve support for provider learning by prioritizing coaching and appreciation messages based on each message's motivational potential for a specific recipient. We developed a Precision Feedback Knowledge Base as an open resource to support precision feedback systems, containing knowledge models that hold potential as key infrastructure for learning health systems. We describe the design and development of the Precision Feedback Knowledge Base, as well as its key components, including quality measures, feedback message templates, causal pathway models, signal detectors, and prioritization algorithms. Presently, the knowledge base is implemented in a national-scale quality improvement consortium for anesthesia care, to enhance provider feedback email messages.