Background: The Accreditation Council for Graduate Medical Education (ACGME) requires residency programs to monitor scheduling, work intensity, and work compression.
Objective: We aimed to create a model for assessing intern work intensity by examining patient and clinical factors in our electronic health systems using multiple linear regression.
Methods: We identified measurable factors that may contribute to resident work intensity within our electronic health systems. In the spring of 2021, we surveyed interns on pediatric hospital medicine rotations each weekday over 5 blocks to rank their daily work intensity on a scale from -100 (bored) to +100 (exasperated). We queried our electronic systems to identify patient care activities completed by study participants on days they were surveyed. We used multiple linear regression to identify factors that correlate with subjective scores of work intensity.
Results: Nineteen unique interns provided 102 survey responses (28.3% response rate) during the study period. The mean work intensity score was 9.82 (SD=44.27). We identified 19 candidate variables for the regression model. The most significantly associated variables from our univariate regression model were text messages (β=0.432, P<.0009, R2=0.105), orders entered (β=0.207, P<.0002, R2=0.128), and consults ordered (β=0.268, P=.022, R2=0.053). Stepwise regression produced a reduced model (R2=0.247) including text messages (β=0.379, P=.002), patient transfers (β=-1.405, P=.15), orders entered (β=0.186, P<.001), and national patients (β=-0.873, P=.035).
Conclusions: Our study demonstrates that data extracted from electronic systems can be used to estimate resident work intensity.