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Diversity in the Pulmonary and Critical Care Medicine Pipeline. Trends in Gender, Race, and Ethnicity among Applicants and Fellows.

Lekshmi SanthoshJennifer M Babik
Published in: ATS scholar (2020)
Background: The diversity in pulmonary and critical care medicine (PCCM) training programs in the United States has not been systematically evaluated, despite emphasis on workforce diversity and its role in improving gender and racial healthcare disparities. Objectives: We analyzed the diversity of the PCCM pipeline by gender, race, and ethnicity over the last 10 years. Methods: The PCCM pipeline was defined as internal medicine residents, fellowship applicants, and fellows in pulmonary-only, critical care medicine-only, and combined PCCM programs. Data on gender, race, and ethnicity were obtained from 2009 to 2018 graduate medical education census data and the Association of American Medical Colleges Electronic Resident Application Service. We used the Association of American Medical Colleges definition of "underrepresented in medicine" (UIM), which comprises African American/black, Hispanic/Latino, American Indian/Alaska Native, or Native Hawaiian/Pacific Islander physicians. Results: Over the last decade, the percentage of female fellows was unchanged in pulmonary (range, 19.4-37.1%), critical care medicine (range, 17.6-31.9%), and PCCM programs (range, 29.5-35.2%). To capture the current snapshot of data across residents, applicants, and fellows, we analyzed 2018 data and found that there was a drop-off from the percentage of female internal medicine residents (41.9%) to the percentage of female applicants and fellows (⩽33% in all three programs). The percentage of UIM fellows decreased in PCCM programs over the last decade to 10.3%. In 2018, there was a drop-off from the percentage of UIM residents (13.7%) to the percentage of UIM fellows in all three programs (<12.9% in all three programs). Conclusions: Striking disparities remain in gender, race, and ethnicity in the pipeline of trainees in PCCM programs; these have not improved (for gender) or have even worsened (for race and ethnicity) over the last decade.
Keyphrases
  • public health
  • african american
  • healthcare
  • mental health
  • pulmonary hypertension
  • electronic health record
  • medical education
  • machine learning
  • deep learning
  • general practice
  • virtual reality
  • emergency medicine