Accountable Allocation and Triage in Health Systems: A Governance Template for AI-Supported Decision Rights

Abstract: This manuscript develops a governance template for accountable allocation and triage in health systems where artificial intelligence is used to support, but not replace, clinical and administrative decision rights. The central claim is that triage quality depends as much on institutional design as on predictive accuracy: when uncertainty, scarcity, and moral pluralism are unavoidable, decision systems must make their assumptions auditable, their trade-offs contestable, and their authority boundaries explicit. The book integrates research design logic, causal reasoning, and trustworthy machine learning with operational engineering for scalable analytics, offering a coherent framework that links model development to decision governance, documentation, and oversight. Across diverse contexts—South Asia’s high-volume public hospitals, European social insurance systems, African resource-constrained referral networks, and the Americas’ mixed payer-provider environments—the template shows how to operationalize fairness, safety, and effectiveness without relying on unverifiable claims. The outcome is a set of reusable artifacts: triage decision-rights matrices, evaluation protocols, uncertainty disclosures, monitoring dashboards, escalation pathways, and policy-aligned accountability mechanisms suitable for researchers, practitioners, regulators, and health-system leaders. Keywords health system governance, triage, resource allocation, accountable AI, decision rights, clinical workflow, uncertainty quantification, causal inference, statistical modeling, algorithmic fairness, model monitoring, MLOps, reproducibility, data stewardship, auditability, policy impact