The next frontier in AI development may not be a bigger model or a faster chip — it could be a better dataset. And right now, some of the world’s most powerful technology companies are turning to a surprisingly concentrated source: the longitudinal medical records of millions of Israeli patients, built up over decades inside the country’s four major health maintenance organizations. As Calcalist Tech reports, that data infrastructure has quietly become a global technology asset, attracting partnerships and licensing deals with companies ranging from Pfizer to OpenAI.
The appeal is structural. Israel’s HMOs — Clalit, Maccabi, Meuhedet, and Leumit — together cover virtually the entire population, and they have maintained digitized records far longer than most healthcare systems worldwide. Clalit alone holds data on roughly 4.7 million members, spanning primary care visits, lab results, imaging, prescriptions, and hospitalizations, in some cases going back 30 or more years. That kind of longitudinal depth, combined with near-universal coverage and relatively homogenous data formatting, is exceptionally rare. For AI developers trying to train models that can reason about disease progression, drug interactions, or treatment outcomes, it is close to irreplaceable.
The Deals Taking Shape
Maccabi Healthcare Services has been among the most aggressive in monetizing this asset. The HMO’s data-sharing arm has inked research collaborations with major pharmaceutical companies and is in discussions with AI firms about access to its anonymized dataset, which covers approximately 2.7 million members. Clalit’s research institute has similarly structured deals that allow external parties to query specific cohorts — patients with Type 2 diabetes, for instance, or those who underwent a particular surgical procedure — without transferring raw records outside the system.
OpenAI’s reported interest fits a pattern visible across the industry. Large language models trained primarily on text increasingly need domain-specific structured data to become clinically useful. Medical records — with their combination of free-text physician notes, coded diagnoses, and numerical lab values — offer exactly that mixture. Pfizer and other pharmaceutical firms, meanwhile, have long used real-world evidence from claims and EHR data to support drug development and regulatory submissions, and Israeli datasets offer a uniquely clean version of that evidence base.
Privacy Architecture and the Regulatory Question
None of this comes without friction. The legal framework governing how Israeli HMO data can be shared internationally is still evolving, and patient consent models vary across the four organizations. Anonymization protocols — typically involving the removal or hashing of direct identifiers — are standard practice, but researchers and regulators alike have raised questions about re-identification risk as AI models grow more powerful. The same capabilities that make these models valuable for medical inference also make them theoretically capable of reconstructing individual identities from patterns in aggregated data.
That tension is not unique to this geography. Health systems across Europe and the United States are grappling with the same tradeoff between data utility and patient privacy as demand for medical AI training sets accelerates. What distinguishes the Israeli case is the scale of institutional readiness: the HMOs have built dedicated data governance teams, legal structures for external licensing, and — in some cases — revenue models that funnel proceeds back into research infrastructure. Whether that model becomes a template for others, or whether tighter international data-transfer rules eventually constrain it, will shape how much of the global AI health market gets built on this particular foundation.
For now, the competitive advantage is real. High-quality, longitudinal, population-scale medical data remains one of the scarcest inputs in healthcare AI — and the organizations sitting on it are increasingly aware of exactly what they hold.
