Evidence map›Paper›PMID 40417586›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Modeling Precision Feedback Knowledge for Healthcare Professional Learning and Quality Improvement.

Zach Landis-Lewis, Yidan Cao, Hana Chung, Peter Boisvert, Anjana Deep Renji, Patrick Galante, Ayshwarya Jagadeesan, Farid Seifi, Allison Janda, Nirav Shah and 2 more

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

12 authors.

Zach Landis-LewisDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Yidan CaoDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Hana ChungDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Peter BoisvertDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Anjana Deep RenjiDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Patrick GalanteDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Ayshwarya JagadeesanDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Farid SeifiDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Allison JandaDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI.
Nirav ShahDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI.
Andrew KrummDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.
Allen FlynnDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI.

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
A knowledge-based message tailoring systemK01LM012528 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANDIS-LEWIS, ZACHARY · 2017 to 2019
$523k
NLM NIH HHS K01 LM012528NLM NIH HHS R01 LM013894
6 · The paper itself

Abstract

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.

Indexed as

FeedbackHealth PersonnelKnowledge BasesQuality ImprovementFormative FeedbackHumansLearningLearning Health System

Identifiers

PMID40417586
PMCPMC12099443

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.