Artificial Intelligence Conformalised Counterfactual Policy Learning for Community Clinic Referral Escalation Under Unobserved Acuity Drift: A Healthcare Regret-Bound Evaluation

Community clinic referral escalation policies must decide which patients to send onward when patient acuity is only partially observed and drifts over time. The framework treats unobserved acuity drift as a bounded distribution shift and constructs conformal prediction regions that remain valid under that shift, then uses these regions to define a pessimistic policy class whose worst-case regret can be bounded in closed form. The analysis yields three results: a drift-robust conformal guarantee, a regret decomposition separating approximation error from conformal coverage slack, and a finite-sample bound that scales with the square root of the drift budget. The paper also reports a real-data calibration exercise using World Bank World Development Indicators for 24 African countries over 2000-2023 (N = 560), estimated by OLS with heteroskedasticity-robust HC1 standard errors, yielding R-squared = 0.591, adjusted R-squared = 0.5895, F = 184.764 (p = 2.82e-62), with coefficients for access to electricity (1291.55405, SE 194.35159), urban population (113.13952, SE 7.77493) and a linear year term (-104.41834, SE 11.31314). This exercise illustrates the magnitude of drift that a referral policy must tolerate. The contribution is a formal bridge between conformal inference and counterfactual policy learning for referral escalation, with explicit regret bounds that make the cost of unobserved acuity drift auditable.