SCAFFOLD
Scalable Coaching and Appreciation Feedback For Optimal Learning and Decision-making
Scalable Coaching and Appreciation Feedback For Optimal Learning and Decision-making
In any context where human performance is critical, from professional sports to open-heart surgery, performance feedback is essential for learning and improvement. However, in health systems, feedback to providers and teams is both widely used and effective, but under-utilitized, resulting in low engagement with the reports and dashboards that it is delivered through.
SCAFFOLD is an open-source software application that enhances feedback reports, emails, and dashboards with coaching and appreciation messages and visualizations. It is based on the idea that there are two primary types of value from feedback: 1) coaching, in which people want to improve performance, and 2) appreciation, which motivates people to sustain performance through recognition of their impact.
Some examples of the messages that SCAFFOLD can generate are "You reached the top performer benchmark", “You are not a top performer”, “You may have an opportunity to improve”, “Your team reached the goal”, and “Congratulations on the exceptionally high quality of care your team has provided”.
SCAFFOLD is a tool for for anyone who has performance data and wants to provide high-value feedback to accelerate learning and improvement.
Scalable Coaching and Appreciation Feedback for Optimal Learning and Decision-Making (SCAFFOLD). Landis-Lewis Z, Boisvert P, Seifi F, Renji AD, Cao Y, Chung H, Janda A, Shah N, Flynn A. Stud Health Technol Inform. 2025 Aug 7;329:431-435. doi: 10.3233/SHTI250876. PMID: 40775894.
Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement. Landis-Lewis Z, Cao Y, Chung H, Boisvert P, Renji AD, Galante P, Jagadeesan A, Seifi F, Janda A, Shah N, Krumm A, Flynn A. AMIA Annu Symp Proc. 2025 May 22;2024:628-637. eCollection 2024. PMID: 40417586
Precision feedback: A conceptual model. Landis-Lewis Z, Janda AM, Chung H, Galante P, Cao Y, Krumm AE. Learn Health Syst. 2024 Apr 9;8(3):e10419. doi: 10.1002/lrh2.10419. eCollection 2024 Jul. PMID: 39036537
Exploring Anesthesia Provider Preferences for Precision Feedback: Preference Elicitation Study. Landis-Lewis Z, Andrews CA, Gross CA, Friedman CP, Shah NJ. JMIR Med Educ. 2024 Jun 11;10:e54071. doi: 10.2196/54071. PMID: 38889065
Performance Summary Display Ontology: Feedback intervention content, delivery, and interpreted information. Landis-Lewis Z, Stansbury C, Rincón J, Gross C. CEUR Workshop Proc. 2022 Sep;3805:L1-L10. PMID: 39949869
A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: Proposal for a Randomized Controlled Trial. Landis-Lewis Z, Flynn A, Janda A, Shah N. JMIR Res Protoc. 2022 May 10;11(5):e34990. doi: 10.2196/34990. PMID: 35536637