A framework for population health observatory from a global perspective: proposed preliminary maturity model and key performance indicators

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Background Population health observatories (PHOs) have emerged as fundamental pillars of modern public health governance, enabling the transformation of fragmented, multidimensional health data from multiple centres into evidence-informed policymaking and development, strategic planning and actionable intelligence for surveillance and precision public health. This study aimed to develop a globally comparative framework for assessing the maturity level of PHOs by using standardised artificial intelligence (AI) and a series of multidimensional key performance indicators (KPIs), with particular emphasis on the Saudi Arabian healthcare transformation context. Methodology We conducted a structured document review and qualitative comparative assessment of national PHOs or equivalent health intelligence systems in 21 jurisdictions. Evidence was obtained from peer-reviewed literature, governmental publications, international agency reports and publicly accessible national documentation. Each jurisdiction was assessed using 18 predefined variables covering data infrastructure, interoperability, surveillance, analytics, governance and population. The resulting scores were mapped to a proposed five-level maturity continuum, and candidate KPIs were organised across seven functional domains. Results This study demonstrated substantial international heterogeneity in PHO AI maturity, predominantly driven by the variables of interoperability of data, real-time surveillance capabilities, deployment of predictive analytics, integration with registry ecosystems and implementation of AI governance. The United Kingdom, the Netherlands, Finland, South Korea and Singapore exhibited maturity levels ranging from systemic to nearly autonomous. Concurrently, Saudi Arabia was positioned at operational maturity Level 3, reflecting substantial advancement in digital transformation, early AI implementation and registry integration. At the same time, gaps persist in terms of wearable and Internet of Things (IoT)-derived data integration, genomic integration, biobank interoperability and autonomous decision support infrastructure. Conclusion The preliminary framework identified variations in documented PHO capabilities and may support structured gap assessment. Formal external validation and prospective testing are required before this framework can be used for national benchmarking or evaluation of population health impacts. These steps are essential for strategic priority setting in Saudi Arabia’s PHO as the country advances towards Level 5 autonomous maturity through robust AI governance, interoperable health ecosystems, predictive and prescriptive analytics and precision public health integration. Such a transformation could position Saudi Arabia as a regional and global leader in precision public health aligned with Vision 2030.

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