Predicting Medical School Enrollment Behavior: Comparing an Enrollment Management Model to Expert Human Judgment.

Purpose: Medical school admissions committees are tasked with fulfilling the values of their institutions through careful recruitment. Making accurate predictions regarding enrollment behavior of admitted students is critical to intentionally formulating class composition and impacts long-term physician representation. The predictive accuracy and potential advantages of employing an enrollment predictive model in medical school admissions compared with expert human judgment have not been tested.

Method: The enrollment management-based predictive model previously generated using historical data was employed to provide a predicted enrollment percentage for each admitted student in the 2016-2017 application pool (N = 352). Concurrently, the human expert created a predicted enrollment percentage for each applicant while blinded to the values generated by the model. An absolute error for each applicant for both approaches was calculated. Statistical significance between approaches (expert vs. enrollment model) was assessed using t tests.

Results: The enrollment management approach was noninferior to expert prediction in all cases (P < .05) with a superior correct classification rate (77.7% vs. 71.2%). When considering subgroup analyses for specific populations of potential importance in recruiting (underrepresented in medicine, female, and in-state applicants), the enrollment management predictions were statistically more accurate (P < .05).

Conclusions: Examining a single admitted class, the enrollment predictions using the enrollment management model were at least as accurate as the expert human estimates, and in specific populations of interest more accurate. This information can be readily exported for a real-time dashboard system to drive recruitment behaviors.