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Scalable Coaching and Appreciation Feedback for Optimal Learning and Decision-Making (SCAFFOLD)
Zach Landis-Lewis1, Peter Boisvert1, Farid Seifi1
1Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Abstract:
Clinical performance feedback is one of the most effective quality improvement strategies, but evidence about its effectiveness is highly variable. A key barrier to improving the effect of feedback is a lack of engagement by healthcare professionals. We developed Scalable Coaching and Appreciation Feedback For Optimal Learning and Decision-making (SCAFFOLD), a precision feedback system, that enhances performance feedback reports and dashboards to improve provider engagement and feedback effectiveness. We introduce SCAFFOLD and its key functions for generating messages with high motivational potential, and report on its function over a six-month period in a national-scale perioperative quality improvement consortium. The system was used to generate 21,776 messages for 4,550 anesthesia providers, producing 20,391 (94%) coaching messages and 1,385 (6%) appreciation messages. SCAFFOLD appears to demonstrate a feasible approach for large-scale generation of coaching and appreciation feedback messages that enhance feedback emails, reports, and dashboards.
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