More Rural, Minoritised Patients Face Higher Amputation Rates—AI Sheds Light on Why
A recent study led by the University of Maryland, published in *Epidemiology*, uses AI to analyze over 1.5 million hospitalizations (2017–19) of adults over 40 with peripheral artery disease or chronic limb‑threatening ischemia. It reveals that unconscious provider bias contributes significantly to higher amputation rates among rural and minoritized communities.
Key Findings:
- Disparities Confirmed: After adjusting for clinical and systemic factors, Black, Hispanic, and Native American patients in rural areas—and Black and Native American patients in urban settings—still experienced significantly higher amputation rates compared to others :contentReference[oaicite:1]{index=1}.
- AI Reveals Hidden Bias: The model evaluated 70 + variables—including health status, hospital access, regional care capacity, and socioeconomic context—and flagged potential unconscious bias in clinical decision-making :contentReference[oaicite:2]{index=2}.
- Clinical Implications: Vascular surgeons often navigate complex treatment decisions. Without clear guidelines to choose between limb-sparing revascularization and amputation, provider instinct—potentially influenced by bias—may steer outcomes :contentReference[oaicite:3]{index=3}.
- AI as a Solution: AI models that incorporate intersectional variables (race, income, rurality) can help identify disparities and guide development of more equitable, evidence-based guidelines :contentReference[oaicite:4]{index=4}.
This work underscores the urgent need to address implicit biases within vascular care and empowers clinicians with data-driven tools to promote equitable limb preservation.
Keywords:
Paula Strassle,
Katharine McGinigle,
peripheral artery disease,
chronic limb-threatening ischemia,
unconscious bias,
amputation disparities,
AI in vascular decision-making