Integrating NPHIES and Health Insurance Data for Population-Level Disease Surveillance, Risk Stratification and Preventive Care in Saudi Arabia

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@article{1723992,
      author = {Salah Ali Ibrahim Ramadan},
      title = {Integrating NPHIES and Health Insurance Data for Population-Level Disease Surveillance, Risk Stratification and Preventive Care in Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {2094-2107},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723992.pdf},
      abstract = {Saudi Arabia’s digital health transformation has created an opportunity to move from episodic, provider-centred information exchange towards population-level intelligence that supports prevention. The National Platform for Health and Insurance Exchange Services (NPHIES) can contribute to this shift because claims, eligibility, authorisation and reimbursement data describe longitudinal patterns of healthcare use across insured populations. Yet administrative data alone cannot establish clinical need with sufficient precision. This review examines how NPHIES-linked health insurance data could be integrated with electronic health records, laboratory results, pharmacy information and patient-generated data to support three connected functions: disease surveillance, risk stratification and preventive care. A structured integrative review of peer-reviewed literature published from 2020 to 2025 was undertaken, with emphasis on Saudi digital health, claims-EHR linkage, population health analytics, chronic disease prediction, interoperability and algorithmic assurance. Thirty DOI-resolvable sources were synthesised thematically. The evidence indicates that claims data are valuable for population coverage, longitudinal utilisation histories and service-cost patterns, whereas clinical data contribute physiological detail, disease severity and treatment context. Their integration can improve case finding, reveal screening and care gaps, and enable risk models that identify people likely to develop chronic disease or experience avoidable acute-care use. However, value depends on identity resolution, coding quality, semantic interoperability, calibration, subgroup fairness, privacy protection and a closed-loop process that converts risk signals into documented preventive action. A Saudi implementation framework is proposed in which NPHIES becomes a governed population-health data layer rather than only a transaction exchange.},
      keywords = {NPHIES; health insurance data; population health; disease surveillance; risk stratification; preventive care; interoperability; Saudi Arabia; Vision 2030},
      month = {October},
  }