This hybrid model will be revised and optimized to capture the skills most reliably assessed by each format. Future analysis will examine validity by determining whether short-form and long-form interview scores accurately measure the skills intended to be assessed. Additionally, data collected from both formats will be used to establish baselines for entering students' competencies.
An enrollment-management-based approach would allow medical schools to better manage the number of students they admit and target recruitment efforts to improve their likelihood of success. It also performs a key institutional research function for understanding failed recruitment of highly desirable candidates.
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.
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