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Review Article

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

Authors

  • Chizube Obinna Chikezie School of Computing Engineering and Intelligent Systems, Ulster University, York Street, Belfast BT15 1ED, UK https://orcid.org/0009-0008-3996-3978

    kezie.c.o@gmail.com

  • Chibuzo Okechukwu Onah College of Business, Georgia State University, Atlanta, USA https://orcid.org/0000-0001-6116-3661
  • Chukwudi Anthony Okolue Owen Graduate School of Management, Vanderbilt University, Nashville, USA
  • Mary-Jane Ezinne Ugbor Faculty of Pharmaceutical Sciences, University of Port Harcourt, Port Harcourt, Nigeria
  • Emmanuel Fagbenle Department of Decision Science, University of New Hampshire, Durham, USA https://orcid.org/0000-0003-1724-4557
  • Olukunle Akanbi Graduate School of Business & Leadership, National Louis University, Tampa, USA https://orcid.org/0000-0002-6276-5901

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

Keywords:

Artificial Intelligence Healthcare Decision Support Systems Electronic Health Records Health Services Research Predictive Analytics in Healthcare

Article information

Journal

Journal of Medical Science, Biology, and Chemistry

Volume (Issue)

2(2), (2025)

Pages

252-261

Published

16-11-2025

How to Cite

Chikezie, C. O., Onah, C. O., Okolue, C. A., Ugbor, M.-J. E., Fagbenle, E., & Akanbi, O. (2025). From Models to Management: A Framework for Predictive Analytics in Health Systems. Journal of Medical Science, Biology, and Chemistry, 2(2), 252-261. https://doi.org/10.69739/jmsbc.v2i2.1149

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