Evidence map›Paper›PMID 40775894›Full record

ArticleStudies in health technology and informatics2025

Scalable Coaching and Appreciation Feedback for Optimal Learning and Decision-Making (SCAFFOLD).

Zach Landis-Lewis, Peter Boisvert, Farid Seifi, Anjana Deep Renji, Yidan Cao, Hana Chung, Allison Janda, Nirav Shah, Allen Flynn

Abstract read
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In one paragraph

Article in Studies in health technology and informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zach Landis-LewisDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Peter BoisvertDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Farid SeifiDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Anjana Deep RenjiDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Yidan CaoDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Hana ChungDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Allison JandaDepartment of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA.
Nirav ShahDepartment of Anesthesiology, University of Michigan, Ann Arbor, Michigan, USA.
Allen FlynnDepartment of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.

Funding

A scalable service to improve health care quality through precision audit and feedbackR01LM013894 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANDIS-LEWIS, ZACHARY · 2021 to 2024
$1.3M
PHEnylephrine versus NOrepinephrine in Major NONcardiac surgery (PHENOMeNON): Foundational Studies for a Pragmatic Randomized Clinical Trial.K23HL166685 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Allison Mary Janda · 2023 to 2026
$689k
NHLBI NIH HHS K23 HL166685NLM NIH HHS R01 LM013894
6 · The paper itself

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.

Indexed as

Formative FeedbackMentoringFeedbackHumansQuality ImprovementAudit and feedbackclinical performance improvementmotivation

Identifiers

PMID40775894
PMCPMC12723818

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.