Mechanistic interpretability of reinforcement learning in Medicaid care coordination

OBJECTIVE: To expose reasoning pathways of a reinforcement learning policy for Medicaid care coordination, develop an error taxonomy and implement fairness-aware guardrails. DESIGN: Retrospective interpretability audit using attention analysis, Shapley explanations, sparse autoencoder feature discovery and blinded clinician adjudication. SETTING: Medicaid care coordination programmes in Washington, Virginia and Ohio (July 2023-June 2025). PARTICIPANTS: 250 000 intervention decisions; 200 divergent cases reviewed by five clinicians. MAIN OUTCOME MEASURES: Calibrated harm prediction; algorithmic clearance and residual harm rates; error taxonomy frequencies; subgroup fairness metrics. RESULTS: The conformal model achieved area under the receiver operating characteristic curve of 0.80 (95% CI 0.78 to 0.82), clearing 89.5% (95% CI 88.9% to 90.1%) of decisions with 1.22% (95% CI 1.14% to 1.30%) residual harm versus 6.67% (95% CI 6.02% to 7.32%) for flagged decisions. Sparse autoencoders identified seven reasoning motifs linking social determinants to clinical cascades. The error taxonomy revealed premise errors (48%, 95% CI 41% to 55%), calibration failures (27%, 95% CI 21% to 33%) and contextual blind spots (25%, 95% CI 19% to 31%). Divergence was higher for telehealth visits (11.2%) and behavioural health patients (10.7% vs 6.9%, p