Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics

Abstract

Problem: Lack of diversity in the physician workforce has well-documented negative impacts on health outcomes. Evidence supports the use of pathway or pipeline programs to recruit underrepresented in medicine students. However, data on how a pathway program should deliver instruction are lacking. This report describes a multiyear project to build such a system with the goal of increasing diversity within medical school cohorts and ultimately the physician workforce.

Approach: In the 2015-2016 academic year, the Ponce Health Sciences University started a 3-phase project to create a data-driven medical school feeder system by coupling a pathway program with predictive analytics. Phase 1 launched the pathway program. Phase 2 developed and validated a predictive model that estimates United States Medical Licensing Examination (USMLE) Step 1 performance. Phase 3 is underway and focuses on adoption, implementation, and support.

Outcomes: Data analysis compared 2 groups of students (pathway vs direct) across specific factors, including Medical College Admission Test (MCAT) score, undergraduate grade point average (GPA), first-generation status, and Step 1 exam performance. Statistically significant differences were found between the 2 groups on the MCAT exam and undergraduate GPA; however, no significant differences were found between groups for first-generation status and performance on the Step 1 exam. This finding supports the authors' hypothesis that although pathway students have significantly lower mean MCAT exam scores compared with direct students, pathway students perform just as well on the USMLE Step 1 exam.

Next steps: Next steps include expanding the project to another campus, adding more socioeconomic status and first-generation data, and identifying best curricular predictors. The authors recommend that medical school programs use pathway programs and predictive analytics to create a more data-centered approach to accepting students with the goal of increasing physician workforce diversity.

Figure 1

Relationship between Ponce Health Sciences University medical students’ actual (not predicted) Medical College Admission Test (MCAT) scores and United States Medical Licensing Examination (USMLE) Step 1 exam scores. The horizontal line at the Step 1 score of 194 indicates the passing score during the data collection period. The vertical line at the MCAT score of 500 indicates that most medical school applicants with a score below 500 will likely not be accepted. Abbreviation: MSMS, Master of Science in Medical Sciences.

Figure 2

Correlation between students’ actual United States Medical Licensing Examination (USMLE) Step 1 exam scores and a proprietary calculation created by the Ponce Health Sciences University technology team called the Tiber Performance Value (TPV). The TPV measures student performance and predicts the likelihood of passing the USMLE Step 1 exam. The horizontal line at the Step 1 score of 194 indicates the passing score during the data collection period. This figure shows a strong positive linear correlation, whereas Figure 1 shows a weak correlation. Abbreviation: MSMS, Master of Science in Medical Sciences.

Figure 3

Reliability of the original predictive model. The markers on the graph represent individual students and show the intersection between their actual and predicted scores. The quadrants divide the graph into 4 regions based on the accuracy of the predictions. The top-left quadrant shows students that the model predicted would pass but failed. The top-right quadrant shows students that the model predicted would pass and passed. This quadrant has significantly more students in it compared with the other 3 quadrants. The bottom-left quadrant shows students that the model predicted would fail and failed. The bottom-right quadrant shows students that the models predicted would fail but passed. The 3 lines represent the midpoint and a range of acceptable error.