Saudi Vision 2030 and Healthcare Data Strategy: What Cluster Leadership Needs

The MOH Transformation Programme: From Payer-Provider to Cluster Model

The Saudi Ministry of Health (MOH) transformation programme, launched formally in 2016 and accelerated under the Vision 2030 framework, restructured the delivery of public healthcare away from a centralised payer-provider model toward nine semi-autonomous health clusters. Each cluster is responsible for a defined geographic population, operates a network of hospitals, primary care centres (PHCs), and specialist facilities, and is accountable to the MOH through a performance framework that includes quality metrics, financial efficiency targets, and patient experience indicators.

The nine health clusters are:

Cluster leadership — typically a CEO/Director General and a senior clinical and operations team — is now responsible for population health outcomes, hospital operational performance, primary care penetration rates, and financial sustainability. The data infrastructure to manage across that accountability scope does not come standard with the cluster governance model. It has to be built.

NPHIES: The National Platform for Health Insurance Exchange

NPHIES — the National Platform for Health Information Exchange and Services — is the Saudi Health Council's unified health data exchange infrastructure, deployed from 2021 onward. NPHIES serves as the centralised hub for electronic claims submission, pre-authorisation, eligibility verification, and, increasingly, clinical data exchange between public MOH facilities, military healthcare (SANG, SANG hospital network, Armed Forces Medical Services), and private sector payers and providers.

The NPHIES architecture has three components relevant to cluster analytics:

• NPHIES Claims Exchange: All electronic health insurance claims are submitted through NPHIES. For clusters that operate under the governmentally-funded patient population (non-insured Saudi nationals), claims data flows through MOH internal systems rather than commercial NPHIES channels — but the data standards are converging.
• NPHIES Clinical Exchange: Based on HL7 FHIR R4, this component supports structured clinical data exchange including lab results, medication lists, diagnostic reports, and referral documentation. Implementation has been phased, with private hospitals leading adoption.
• NPHIES Verification Portal: The eligibility and beneficiary verification layer, used by all providers at the point of registration to confirm insurance coverage and entitlement status.

CBAHI Accreditation and Data Requirements

The Central Board for Accreditation of Healthcare Institutions (CBAHI) is Saudi Arabia's national healthcare accreditation body, established by Royal Decree. CBAHI accreditation is mandatory for all hospitals operating in the Kingdom — including both public (MOH cluster) and private sector facilities — under the licensing framework administered by the MOH.

The CBAHI standards most relevant to data strategy are contained in the Performance Measurement and Improvement (QI) chapter and the Information Management (IM) chapter. Key requirements include:

JCI Accreditation Growth: From 15 to 47+ Facilities

Saudi Arabia had approximately 15 Joint Commission International (JCI)-accredited hospitals in 2015. By 2025, that number had grown to over 47 facilities — driven by private hospital groups seeking to attract medical tourism patients, international staff, and to demonstrate quality credentials to corporate health insurance clients. The King Faisal Specialist Hospital network, Saudi German Hospitals Group, and Mouwasat Medical Services have led the private sector JCI push.

JCI accreditation has significant data infrastructure implications because of the Quality Improvement and Patient Safety (QPS) chapter requirements. JCI requires:

• Ongoing monitoring of clinical quality indicators, with monthly reporting to quality committees and quarterly reporting to the governing board
• Use of statistical process control (run charts, control charts) to distinguish common-cause from special-cause variation — a methodology that requires analyst capacity many Saudi hospitals do not have in-house
• International Library of Measures compliance for 12 core IPSG (International Patient Safety Goals) indicators, with external benchmarking against JCI-accredited peers globally
• Evidence of quality improvement projects using a structured methodology (PDSA, Lean, or equivalent) with documented data collection, analysis, and outcome measurement

Value-Based Healthcare and the Digital Health Strategy

Saudi Arabia's National Digital Health Strategy (NDHS), overseen by the Saudi Health Council and aligned with Vision 2030, sets out a framework for digital transformation across five domains: electronic health records, telemedicine, health data platforms, artificial intelligence, and genomics. The NDHS targets include:

The shift to value-based healthcare — paying for outcomes rather than activity — is being piloted through bundled payment models for elective procedures and capitation models for primary care in the Eastern Province cluster. These models require claims analytics and clinical outcome tracking at a granularity that most cluster hospital information systems (HIS) — predominantly Oracle Cerner and Intersystems HealthShare deployments — do not provide through standard reporting.

What Cluster Leadership Needs from Data Infrastructure

The accountability structure of a health cluster creates specific analytical demands that differ from those of an individual hospital. A cluster CEO or CMO is accountable for:

• Population health outcomes: Chronic disease prevalence and management rates across the cluster catchment population — diabetes (currently affecting approximately 18% of the Saudi adult population), hypertension (~33%), obesity, and cardiovascular disease. Primary care penetration — the proportion of the population registered with a PHC and attending regularly — is a leading indicator of downstream hospital utilisation.
• Cross-facility referral management: Appropriate referral from PHC to secondary care, and from district hospital to tertiary facility, avoids both undertreatment and unnecessary tertiary admissions. Cluster analytics needs to show referral patterns across all levels of the care pyramid simultaneously.
• Elective capacity and waiting time management: Outpatient waiting times at tertiary cluster facilities are a significant source of patient dissatisfaction and a driver of private sector utilisation. Cluster leadership needs real-time visibility of specialty waiting times, not monthly reports.
• MOH performance framework compliance: The cluster performance framework includes specific KPIs reported quarterly to the MOH. Data for these KPIs is currently assembled manually from HIS extracts in most clusters — a process that is slow, error-prone, and leaves no time for analytical insight beyond the numbers themselves.
• Financial efficiency benchmarking: Cost per case, bed occupancy, theatre utilisation, and length of stay benchmarks against national and international comparators are required for the cluster business model as private sector participation increases and activity-based funding models are phased in.

Building the Data Capability: Practical Priorities

For cluster leadership navigating the analytics infrastructure build, the practical priorities in sequence are:

First, unified data extraction. Most clusters have 8–30 hospitals on disparate HIS versions, with data in separate schemas that cannot be queried together. Before any analytics is possible, a data warehouse or lakehouse approach — extracting from each HIS into a common data model — is required. HL7 FHIR-based extraction using the NPHIES infrastructure is the emerging standard; proprietary database extracts from Cerner or Intersystems via agreed data sharing agreements are the near-term fallback.

Second, indicator governance. Clusters that deploy dashboards before agreeing on indicator definitions produce dashboards that different stakeholders trust differently. Bed occupancy calculated as midnight census beds occupied divided by licensed beds produces a different number than the same calculation using staffed beds. Neither is wrong; both are useful; but the cluster needs one definition per indicator, agreed by clinical and operations leadership, documented, and enforced in the data pipeline.

Third, analytical questions before analytical tools. The most common failure mode in cluster analytics investment is procuring a business intelligence platform before specifying what questions it needs to answer. Vizier's engagement model starts with the cluster's strategic priorities — MOH KPIs, accreditation requirements, value-based contract targets — and works backward to data requirements and tooling, rather than forward from a software catalogue.