# Development and Validation of a Prediction Model to Address Physician Burnout

> **NIH AHRQ K08** · STANFORD UNIVERSITY · 2022 · $152,582

## Abstract

PROJECT SUMMARY
Professional burnout is a growing epidemic with symptoms affecting over 500,000 US physicians at any given
time and carries significant adverse consequences for physician mental health, health care value, quality of care,
and patient safety. There is an urgent need to develop reliable methods to proactively identify work environments
at high risk for physician burnout, and tailor process improvements accordingly.
The objective of this proposal is to develop and validate a real-time prediction model that uses operational data
in primary care practices to identify high-risk clinics for physician burnout, enabling timely and tailored process
improvements. The central hypothesis is that routinely-collected electronic health record (EHR) usage metrics
coupled with practice-specific metrics can predict physicians’ risk for burnout and inform process improvement.
The rationale for the proposed research is that early identification of high-risk clinics will allow organizations to
tailor interventions to those clinics before burnout and its individual or health system consequences arise. This
would be an innovative approach that prevents burnout rather than reacting to it. This project capitalizes on
routinely-collect data from Stanford primary care clinics to create a database encompassing EHR usage metrics
and practice-specific metrics. The specific aims are:
Aim 1: Develop a prediction model to quantify risk for physician burnout. The working hypothesis is that real-time
metrics of practice efficiency tracked by the EHR and other practice-specific metrics can predict physician
burnout using a machine learning approach. Aim 2: Refine the prediction model using qualitative methods. The
working hypothesis is that qualitative assessment will inform refinements to the prediction model created in Aim
1, and will demonstrate the face validity of the model. Aim 3: Validate the use of the prediction model to identify
high-risk clinics. The working hypothesis is that quality of care metrics will demonstrate the concurrent validity,
that subsequent routinely administered burnout surveys will demonstrate the predictive validity of the prediction
model created in Aims 1 and 2, when aggregated at the clinic level.
This proposal is significant because physician burnout is a growing problem with important implications for
patient safety. It is also innovative in deploying machine learning and mixed methods to identify physicians at
increased risk for burnout which will enable testing interventions to reverse this trend. In combination with formal
training in quantitative and qualitative methods, expert mentorship, and participation in selected scholarly
activities at Stanford, the experience gained through this project will facilitate progress toward a long-term goal
to become an academic leader advancing evidence-based reform of the health care delivery system to optimize
human factors that improve quality and safety.

## Key facts

- **NIH application ID:** 10474340
- **Project number:** 5K08HS027837-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Daniel Tawfik
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2022
- **Award amount:** $152,582
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10474340

## Citation

> US National Institutes of Health, RePORTER application 10474340, Development and Validation of a Prediction Model to Address Physician Burnout (5K08HS027837-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10474340. Licensed CC0.

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