Use of Race in Clinical Algorithms—Reply - Scorecard - MDSpire
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Incorporating Race in Clinical Algorithms—A Response

  • By

  • James A. Diao

  • Emma Pierson

  • October 1, 2026

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Clinical Scorecard: Incorporating Race in Clinical Algorithms—A Response

At a Glance

CategoryDetail
ConditionClinical Algorithm Design
Key MechanismsIncorporation of patient preferences, accuracy, equity, and outcomes in algorithm design.
Target PopulationPatients affected by health disparities.
Care SettingClinical research and algorithm development.

Key Highlights

  • Patient preferences are a key consideration in algorithm design.
  • Concerns exist regarding the incorporation of race into clinical algorithms.
  • Race-neutral approaches are favored when performance is comparable.
  • Comparative assessment of race-aware versus race-neutral algorithms is necessary.
  • Race often serves as a proxy for unmeasured factors in algorithms.

Guideline-Based Recommendations

Diagnosis

  • Evaluate the methodological rigor in developing clinical algorithms.

Management

  • Consider systemic measures beyond computational modifications for race removal.

Monitoring & Follow-up

  • Assess the consequences of algorithm modifications on health disparities.

Risks

  • Different race-removal approaches may improve or worsen health disparities.

Patient & Prescribing Data

Patients with varying racial backgrounds affected by health disparities.

Incorporating novel measurements may improve algorithm accuracy without relying on race.

Clinical Best Practices

  • Focus on patient attitudes regarding the use of race in clinical algorithms.
  • Conduct rigorous comparisons of race-aware and race-neutral algorithms.
  • Implement systemic efforts to improve measurement and reduce disparities.

Related Resources & Content

Original Source(s)

Use of Race in Clinical Algorithms—Reply

  • by James A. Diao, Emma Pierson

  • October 1, 2026

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