Evidence map›Paper›PMID 35536637›Full record

ArticleJMIR research protocols2022

A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: Proposal for a Randomized Controlled Trial.

Zach Landis-Lewis, Allen Flynn, Allison Janda, Nirav Shah

Registry-linked trialAbstract read
In one paragraph

Article in JMIR research protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05923463 (A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback), which is not on this map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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.

NCT05923463 naactive not recruitingnot on this mapstarted 2024, after this paper: background citation

A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: A Cluster-randomized Controlled Trial

TypeinterventionalSponsorUniversity of MichiganRan2024 to 2025Enrolled3,500ConditionsAnesthesia Care QualityArmsprecision-enhanced feedback email messages, standard feedback email messages
3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Precision feedback: A conceptual model.Learning health systems · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. 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

4 authors.

Zach Landis-LewisDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-9117-9338
Allen FlynnDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0002-3471-3063
Allison JandaDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0001-5080-5630
Nirav ShahDepartment of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.ORCID https://orcid.org/0000-0001-7704-5141

Funding

University of Michigan Anesthesiology Post-Doctoral Research Training ProgramT32GM103730 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Chad M Brummett, SACHIN KHETERPAL · 2014 to 2026
$1.9M
Anesthetic Induction Overdose Among Elderly Surgical PatientsR01AG059607 · NIA · YALE UNIVERSITY · PI SCHONBERGER, ROBERT · 2019 to 2022
$1.8M
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
NIA NIH HHS R01 AG059607NIGMS NIH HHS T32 GM103730NLM NIH HHS K01 LM012528NLM NIH HHS R01 LM013894
6 · The paper itself

Abstract

backgroundHealth care delivery organizations lack evidence-based strategies for using quality measurement data to improve performance. Audit and feedback (A&F), the delivery of clinical performance summaries to providers, demonstrates the potential for large effects on clinical practice but is currently implemented as a blunt one size fits most intervention. Each provider in a care setting typically receives a performance summary of identical metrics in a common format despite the growing recognition that precisionizing interventions hold significant promise in improving their impact. A precision approach to A&F prioritizes the display of information in a single metric that, for each recipient, carries the highest value for performance improvement, such as when the metric's level drops below a peer benchmark or minimum standard for the first time, thereby revealing an actionable performance gap. Furthermore, precision A&F uses an optimal message format (including framing and visual displays) based on what is known about the recipient and the intended gist meaning being communicated to improve message interpretation while reducing the cognitive processing burden. Well-established psychological principles, frameworks, and theories form a feedback intervention knowledge base to achieve precision A&F. From an informatics perspective, precision A&F requires a knowledge-based system that enables mass customization by representing knowledge configurable at the group and individual levels.

objectiveThis study aims to implement and evaluate a demonstration system for precision A&F in anesthesia care and to assess the effect of precision feedback emails on care quality and outcomes in a national quality improvement consortium.

methodsWe propose to achieve our aims by conducting 3 studies: a requirements analysis and preferences elicitation study using human-centered design and conjoint analysis methods, a software service development and implementation study, and a cluster randomized controlled trial of a precision A&F service with a concurrent process evaluation. This study will be conducted with the Multicenter Perioperative Outcomes Group, a national anesthesia quality improvement consortium with >60 member hospitals in >20 US states. This study will extend the Multicenter Perioperative Outcomes Group quality improvement infrastructure by using existing data and performance measurement processes.

resultsThe proposal was funded in September 2021 with a 4-year timeline. Data collection for Aim 1 began in March 2022. We plan for a 24-month trial timeline, with the intervention period of the trial beginning in March 2024.

conclusionsThe proposed aims will collectively demonstrate a precision feedback service developed using an open-source technical infrastructure for computable knowledge management. By implementing and evaluating a demonstration system for precision feedback, we create the potential to observe the conditions under which feedback interventions are effective. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34990.

Indexed as

anesthesiologyaudit and feedbackhuman-centered designknowledge-based systemlearning health system

Identifiers

PMID35536637
PMCPMC9131150

What OpenQuestion holds

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Registered trials

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.