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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.
Purpose:
To inform graduate medical education (GME) outcomes at the individual resident level, this study sought a method for attributing care for individual patients to individual interns based on "footprints" in the electronic health record (EHR).
Method:
Primary interns caring for patients on an internal medicine inpatient service were recorded daily by five attending physicians of record at University of Cincinnati Medical Center in August 2017 and January 2018. These records were considered gold standard identification of primary interns. The following EHR variables were explored to determine representation of primary intern involvement in care: postgraduate year, progress note author, discharge summary author, physician order placement, and logging clicks in the patient record. These variables were turned into quantitative attributes (e.g., progress note author: yes/no), and informative attributes were selected and modeled using a decision tree algorithm.
Results:
A total of 1,511 access records were generated; 116 were marked as having a primary intern assigned. All variables except discharge summary author displayed at least some level of importance in the models. The best model achieved 78.95% sensitivity, 97.61% specificity, and an area under the receiver-operator curve of approximately 91%.
Conclusions:
This study successfully predicted primary interns caring for patients on inpatient teams using EHR data with excellent model performance. This provides a foundation for attributing patients to primary interns for the purposes of determining patient diagnoses and complexity the interns see as well as supporting continuous quality improvement efforts in GME.
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