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Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics
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. Th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516161/ https://www.ncbi.nlm.nih.gov/pubmed/37267045 http://dx.doi.org/10.1097/ACM.0000000000005287 |
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author | Mayrath, Michael Fontanez, Darah Abdelbaset, Ferrahs Lenihan, Bryan Lenihan, David V. |
author_facet | Mayrath, Michael Fontanez, Darah Abdelbaset, Ferrahs Lenihan, Bryan Lenihan, David V. |
author_sort | Mayrath, Michael |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10516161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-105161612023-09-23 Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics Mayrath, Michael Fontanez, Darah Abdelbaset, Ferrahs Lenihan, Bryan Lenihan, David V. Acad Med Innovation Reports 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. Lippincott Williams & Wilkins 2023-06-02 2023-10 /pmc/articles/PMC10516161/ /pubmed/37267045 http://dx.doi.org/10.1097/ACM.0000000000005287 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Association of American Medical Colleges. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Innovation Reports Mayrath, Michael Fontanez, Darah Abdelbaset, Ferrahs Lenihan, Bryan Lenihan, David V. Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics |
title | Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics |
title_full | Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics |
title_fullStr | Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics |
title_full_unstemmed | Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics |
title_short | Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics |
title_sort | increasing diversity in the physician workforce: pathway programs and predictive analytics |
topic | Innovation Reports |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516161/ https://www.ncbi.nlm.nih.gov/pubmed/37267045 http://dx.doi.org/10.1097/ACM.0000000000005287 |
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