From Models to Management: A Framework for Predictive Analytics in Health Systems

Abstract

Health systems increasingly deploy predictive analytics to improve patient outcomes and operational performance, yet many projects stall at the interface between model output and managerial action. This review looks at real-world deployments connecting clinical prediction with market and operational levers, staffing, bed flow, outreach, and scheduling, and distills an integration framework spanning data architecture, model selection, real-time pipelines, governance, and evaluation. Evidence emphasizes measurable effects on process and, in selected contexts, outcomes when models are embedded in event-driven workflows and governed with clear decision rights, calibration monitoring, and explainability support. Because much of the empirical literature originates in the United States, generalizability is assessed using compact international implementations (United Kingdom stroke AI, Singapore’s C3 command center, and India’s TB computer-aided CXR triage). The review argues that impact depends less on algorithmic novelty than on socio-technical integration: reliable data plumbing, execution discipline, and incentives aligned to net clinical and operational utility

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