Algorithmic design choices as determinants of health equity: a causal simulation study of healthcare resource allocation

Background. Care management programs increasingly rely on prediction algorithms to decide which patients receive limited healthcare resources. Because these algorithms are typically trained to predict future healthcare utilization or cost, they may systematically underidentify patients whose care-seeking behavior is suppressed by structural barriers. Objective. To evaluate how three modifiable design choices (selection threshold, feature set, and training objective) affect allocation equity in care management targeting. Methods. We constructed an analytical cohort of 201,216 adults from linked

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