Do Patients Want “Diversity” or Competence? | Dr. Stephen Kershnar (E188)

A philosophy professor/lawyer argues that med-school “holistic” + diversity-weighted admissions are less predictive than a numbers-based algorithm—and that the stakes show up downstream in physician quality, access, and patient outcomes.

Guest bio:

Dr. Steven Kirschner (as stated in your intro) is a distinguished teaching professor of Philosophy at SUNY Fredonia and also an attorney; he authored the 2024 paper “The Diversity Argument for Affirmative Action in Medical School: A Critique” (Journal of Controversial Ideas).

Topics discussed:

• Holistic admissions vs. algorithmic/metrics-based selection
• The “15% top GPA+MCAT rejected” claim (2019–2022)
• Medical error estimates and why measurement is messy
• Predictive validity: MCAT, GPA, boards, and what doesn’t predict
• Specialty selection, pass/fail exams, and ranking problems
• DEI/affirmative action post–Supreme Court and “relabeling” effects
• Workforce shortages, incentives, and productivity (incl. part-time work)
• Disability accommodations, testing integrity, and gaming incentives
• Diversity-of-thought vs demographic diversity; “underserved communities” argument
• The uncomfortable “should patients use demographics as signals?” question

Main points:

• Admissions should prioritize statistically validated predictors (MCAT + GPA, etc.), not interviews/essays/“compelling stories.”
• Holistic admissions is inconsistent and unvalidated, often functioning like an opaque quota-by-proxy system.
• Medical error and accountability make physician quality a high-stakes selection problem (even if exact death counts are disputed).
• If underserved-service is the goal, subsidize it directly (pay, loan forgiveness, tuition incentives) rather than indirectly via admissions preferences.
• Credential changes (e.g., pass/fail) can make it harder to sort candidates for competitive specialties.
• Workforce shortages strengthen the case for optimizing for long-run productivity and retention, not symbolic criteria.
• The taboo question: whether individuals should use group-level stats as a decision heuristic when individual-level info is limited.

Top 3 quotes:

• “The number one error is that we're waiting, giving diversity, um a large amount of weight.”
• “Medical school admissions are done through… a holistic means… and they weight things that have not been statistically validated.”
• “The awkward but correct approach is to say, yes, you should.” (re: whether people should use demographics as predictors)

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