# A scalable service to improve health care quality through precision audit and feedback

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $331,500

## Abstract

All health care delivery organizations measure care quality and outcomes, increasingly via electronic clinical
quality measures 1 and dashboards 2,3. However, these organizations lack evidence-based strategies for putting
their quality and outcome data to work to improve performance 4,5. The most common approach is audit and
feedback (A&F), the delivery of clinical performance summaries to providers, which demonstrates potential for
large effects on clinical practice 6–8. But A&F too often produces negligible effects 5,9, creating little more than
distraction for providers who are fatigued by information chaos 9–11. As currently implemented, A&F is a blunt,
“one size fits most” intervention. Each provider in a care setting typically receives identical metrics in a
common format, despite a growing recognition that “precisionizing” interventions holds significant promise to
improve their impact 12–15. A precision approach to A&F would prioritize display of information in the single
metric that, for each recipient, carries the highest value for improving performance, such as when the metric's
level drops below a peer benchmark or minimum standard for the first time, revealing an actionable
performance gap 16–19. Furthermore, precision A&F would employ an optimal message format (including framing
and visual displays 20–24), based on what is known about the recipient and the intended gist meaning being
communicated, to improve message interpretation while reducing cognitive processing burden 25–28. Well-
established psychological principles, frameworks, and theoretical mechanisms provide a knowledge base to
achieve precision A&F 16–19,29–33. From an informatics perspective, precision A&F requires a knowledge-based
system that uses psychological theory at its core, but which enables mass customization by giving precedence
to configurable knowledge about recipients at the group and individual levels. A precision A&F service employs
this knowledge as requirements (necessary characteristics for message acceptability) and preferences (the
relative importance of message characteristics) to generate messages that are more likely than a “one size fits
most” report to positively influence clinical decision-making and practice. An equally important informatics
challenge is to enable widespread improvement through a service for precision A&F at scale. A scalable
precision A&F service must function as infrastructure compatible with a wide range of computing environments
and supporting a wide range of clinical domains. In his previous NLM K-award, the principal investigator
developed and tested a prototype knowledge-based system for precision A&F in email messages in anesthesia
care. Preliminary data show that provider preferences are not uniform, suggesting that a platform for
computable knowledge is necessary to support scalable precision A&F. The Knowledge Grid platform,
developed at the University of Michigan, has been shown to support “precisionizing” for cli...

## Key facts

- **NIH application ID:** 10491922
- **Project number:** 5R01LM013894-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Zachary Landis-Lewis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $331,500
- **Award type:** 5
- **Project period:** 2021-09-21 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10491922

## Citation

> US National Institutes of Health, RePORTER application 10491922, A scalable service to improve health care quality through precision audit and feedback (5R01LM013894-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10491922. Licensed CC0.

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