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 underutilized, even though it is widely available and effective. Low engagement with dashboards and reports is a symptom of a larger problem with feedback in health systems, which is that it is rarely prioritized based on its potential value to providers and teams.
SCAFFOLD is an open-source software application that analyzes performance data to identify and prioritize high-value information, expressed in concise messages, that can enhance dashboards, reports, and other feedback channels. It is based on the idea that there are two primary types of value from feedback: 1) coaching, in which people want to improve, and 2) appreciation, in which people want to sustain the impact of their work.
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 using performance data to accelerate learning and improvement through high-value feedback.
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