Strategic Deployment of Predictive Analytics for Chronic Disease and Population Health Management: A Comparative Review of U.S. and Developing Country Health Systems

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@article{1714671,
      author = {Erica Afrihyia, Prisca U Ojukwu, Salewa Gloria Akinse},
      title = {Strategic Deployment of Predictive Analytics for Chronic Disease and Population Health Management: A Comparative Review of U.S. and Developing Country Health Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {7},
      pages = {2770-2794},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714671.pdf},
      abstract = {The growing global burden of chronic disease necessitates a paradigm shift from reactive treatment models toward anticipatory, data-driven healthcare systems. This study critically examines the strategic frameworks, infrastructural prerequisites, governance mechanisms, and contextual dynamics shaping the deployment of predictive analytics in chronic disease management across high-income and developing health systems. The purpose was to conceptualize predictive analytics as a multidimensional transformation process and to evaluate the institutional conditions required for sustainable, equitable implementation. Adopting a structured analytical review methodology, the study synthesizes interdisciplinary scholarship spanning predictive modelling, digital health infrastructure, cybersecurity governance, financial sustainability, and public health systems design. The analysis develops a comparative lens to assess how digital maturity, regulatory alignment, financing structures, workforce readiness, and community engagement influence implementation trajectories. Findings indicate that predictive analytics enhances early disease detection, personalized intervention planning, financial forecasting, and system-wide resource optimization. However, successful deployment is contingent upon interoperable data ecosystems, explainable algorithmic architectures, secure cloud infrastructures, and robust compliance frameworks. High-income systems demonstrate advanced analytics engineering and security integration capacities, while developing contexts exhibit adaptive innovation strategies centered on modular deployment and access expansion. Across settings, ethical governance, stakeholder participation, and sustainability alignment emerge as decisive determinants of long-term impact. The study concludes that predictive healthcare transformation requires strategic coherence rather than isolated technological adoption. It recommends context-sensitive deployment frameworks, diversified financing models, strengthened cybersecurity safeguards, and continuous performance evaluation mechanisms to ensure equitable and durable implementation. By articulating an integrated roadmap for predictive health systems, the study contributes to advancing resilient, anticipatory, and socially responsive chronic disease management strategies.},
      keywords = {Predictive Analytics; Chronic Disease Management; Digital Health Governance; Health System Resilience; Sustainable Healthcare Financing; AI Implementation Strategy.},
      month = {January},
      doi = {https://doi.org/10.64388/IREV9I7-1714671}
  }