Current Research and Scholarly Interests

I work on the development of machine learning methods to identify structures and systems that promote high quality health care using large databases of electronic health record (EHR) metadata. I am particularly interested on leveraging EHR audit log data to understand and mitigate medical errors. Projects include:

- prediction model for physician burnout from EHR use measures
- computer vision model to define EHR use measures
- identification of inpatient medication errors using EHR audit log data
- unsupervised learning model to identify pediatric patient-centric team members from EHR audit log data
- identification of team and organizational context factors associated with medication errors using EHR audit log data

Lab Affiliations

All Publications

  • Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures. Mayo Clinic proceedings Tawfik, D., Bayati, M., Liu, J., Nguyen, L., Sinha, A., Kannampallil, T., Shanafelt, T., Profit, J. 2024


    To evaluate the ability of routinely collected electronic health record (EHR) use measures to predict clinical work units at increased risk of burnout and potentially most in need of targeted interventions.In this observational study of primary care physicians, we compiled clinical workload and EHR efficiency measures, then linked these measures to 2 years of well-being surveys (using the Stanford Professional Fulfillment Index) conducted from April 1, 2019, through October 16, 2020. Physicians were grouped into training and confirmation data sets to develop predictive models for burnout. We used gradient boosting classifier and other prediction modeling algorithms to quantify the predictive performance by the area under the receiver operating characteristics curve (AUC).Of 278 invited physicians from across 60 clinics, 233 (84%) completed 396 surveys. Physicians were 67% women with a median age category of 45 to 49 years. Aggregate burnout score was in the high range (≥3.325/10) on 111 of 396 (28%) surveys. Gradient boosting classifier of EHR use measures to predict burnout achieved an AUC of 0.59 (95% CI, 0.48 to 0.77) and an area under the precision-recall curve of 0.29 (95% CI, 0.20 to 0.66). Other models' confirmation set AUCs ranged from 0.56 (random forest) to 0.66 (penalized linear regression followed by dichotomization). Among the most predictive features were physician age, team member contributions to notes, and orders placed with user-defined preferences. Clinic-level aggregate measures identified the top quartile of clinics with 56% sensitivity and 85% specificity.In a sample of primary care physicians, routinely collected EHR use measures demonstrated limited ability to predict individual burnout and moderate ability to identify high-risk clinics.

    View details for DOI 10.1016/j.mayocp.2024.01.005

    View details for PubMedID 38573301