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A Feasibility Study to Attribute Patients to Primary Interns on Inpatient Ward Teams Using Electronic Health Record
Daniel J Schumacher1, Danny T Y Wu, Karthikeyan Meganathan
1D.J. Schumacher is associate professor of pediatrics, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio. D.T.Y. Wu is assistant professor of biomedical informatics and pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio. K. Meganathan is senior clinical data analyst, Center for Health Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio. L. Li is research associate, Center for Health Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio. B. Kinnear is assistant professor of pediatrics and internal medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio. D.R. Sall is assistant professor of internal medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio. E. Holmboe is senior vice president for milestones development and evaluation, Accreditation Council for Graduate Medical Education, Chicago, Illinois. C. Carraccio is vice president of competency-based assessment, American Board of Pediatrics, Chapel Hill, North Carolina. C. van der Vleuten is professor of education, Department of Educational Development and Research, Faculty of Health, Medicine, and Life Sciences, and scientific director, School of Health Professions Education (SHE), Maastricht University, Maastricht, The Netherlands. J. Busari is consultant pediatrician and associate professor of medical education, Maastricht University, Maastricht, The Netherlands. M. Kelleher is assistant professor of pediatrics and internal medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio. D. Schauer is associate professor of internal medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio. E. Warm is professor of medicine and internal medicine program director, University of Cincinnati College of Medicine, Cincinnati, Ohio.
This study developed an accurate electronic health record (EHR) method to identify primary interns caring for patients. This supports graduate medical education (GME) quality improvement and outcome assessment.
Area of Science:
- Medical Informatics
- Graduate Medical Education
- Healthcare Analytics
Background:
- Assessing individual resident performance in graduate medical education (GME) requires accurate patient attribution.
- Electronic Health Records (EHRs) offer a potential data source for tracking physician-patient interactions.
Purpose of the Study:
- To develop and validate a method for attributing patient care to individual interns using EHR data.
- To establish a foundation for evaluating GME outcomes and quality improvement initiatives.
Main Methods:
- Utilized daily attending physician records as the gold standard for primary intern identification.
- Analyzed EHR variables including postgraduate year, progress note author, order placement, and system interactions.
- Employed a decision tree algorithm to model intern involvement based on quantitative EHR attributes.
Main Results:
- The developed model achieved high performance metrics: 78.95% sensitivity, 97.61% specificity, and an AUC of approximately 91%.
- Key EHR variables, excluding discharge summary authorship, were significant predictors of primary intern involvement.
- Successfully predicted primary intern patient attribution using EHR footprints.
Conclusions:
- EHR data can reliably predict primary intern patient attribution with excellent accuracy.
- This method supports GME by enabling assessment of patient diagnoses and complexity managed by interns.
- Facilitates continuous quality improvement efforts within GME programs.
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