Why Global Health Systems Should Pay Attention to Saudi Arabia’s Analytics Playbook

For a healthcare system under constant pressure to do more with less, the promise of advanced analytics has always been alluring. But few organizations have built a truly mature, fully integrated analytics infrastructure that can influence care delivery, operational performance, and long-term strategic planning. This month, a global first came quietly out of Riyadh that should force health systems around the world to rethink their own analytics ambitions.

King Abdulaziz Medical City (KAMC) became the first facility worldwide to achieve Stage 7 on the modernized HIMSS Analytics Maturity Model (AMAM), introduced in 2024. In doing so, KAMC became one of only a handful of organizations globally to earn triple Stage 7 status across AMAM, EMRAM (Electronic Medical Record Adoption Model), and INFRAM (Infrastructure Adoption Model). And while accolades alone do not guarantee impact, the components of KAMC’s approach make clear this recognition is more than symbolic.

The updated AMAM framework is not just a benchmarking tool. It is a maturity model that tests real-world organizational capacity to govern data, execute advanced analytics, and embed AI transparently into decision support. According to HIMSS’ documentation, Stage 7 now reflects the ability to incorporate predictive and prescriptive analytics, integrate patient-reported outcomes, and drive human-centered design into data strategy. In short, it measures not just tools, but governance, performance, and culture.

KAMC achieved this not through outsourcing or platform shopping, but by cultivating deep institutional commitment to analytics fluency. Its focus areas included:

• Integration of medical devices into EMR workflows

• Upgrades to patient portals that support self-management

• Systematic measurement of digital tool usage and impact

• Extensive patient satisfaction surveying

• Transparent, clinician-facing AI systems for prescriptive insight

This integrated approach goes beyond what most U.S. hospitals today are prepared to implement. In fact, while many U.S. systems have implemented predictive analytics in siloed use cases, very few have achieved system-wide governance of data use and AI accountability. As recently as 2023, a report by McKinsey estimated that up to 40% of data analytics deployments in hospitals underperformed due to lack of enterprise strategy, fragmented tooling, or clinician mistrust in the outputs.

Compare that with the HIMSS assessment of KAMC, which specifically cited analytics governance, planning rigor, and data stewardship as differentiating features. That reflects a maturity level where leadership isn’t simply experimenting with data science. They’re managing it like infrastructure.

This raises an uncomfortable question for health systems in the U.S. and Europe: why are many still unable to scale analytics effectively?

One reason may be misaligned incentives. In most markets, there is limited reimbursement directly tied to enterprise analytics performance. Hospitals invest in what generates margin or meets compliance. Until advanced analytics directly intersects with reimbursement policy or regulatory scrutiny, many systems view it as a “nice to have.”

But this view is changing. The Centers for Medicare & Medicaid Services (CMS) has increasingly referenced the need for real-time analytics capabilities in its value-based models, and ONC’s HTI-1 Final Rule puts stronger expectations on system interoperability that directly impacts data liquidity for analytics use cases. Analytics infrastructure is no longer tangential to compliance, and it is fast becoming a prerequisite.

Second, many systems have underestimated the cultural component. As highlighted in a 2024 Harvard Business Review analysis of hospital digital transformation, analytics transformation requires more than IT strategy. It demands new roles, new KPIs, and an organization-wide operating model that ties data to decision-making, not just dashboards.

KAMC’s model, anchored in real-time feedback loops, visible AI systems, and user adoption metrics, demonstrates what it looks like when analytics is treated as a health system asset, not a bolt-on product. The organization’s global-first AMAM achievement signals more than internal alignment. It signals international leadership.

For health systems watching from afar, this moment should be a call to action. It’s time to stop treating data maturity as a technology problem. It is a strategy problem, and one that will soon determine competitive relevance in care delivery, payment optimization, and patient trust.