Leading pan-European data science teams for a global technology company's healthcare division — deploying predictive models for chronic disease risk in partnership with major health insurers and hospital groups.
Our founding team led the pan-European data science function for a major global technology company's healthcare division — working directly with Fortune 500 healthcare organisations across European markets. The engagement spanned health insurers, hospital groups, and healthcare providers, with a mandate to deliver AI solutions that produced measurable clinical and economic outcomes, not just technical proofs-of-concept.
Healthcare organisations were sitting on rich patient data but lacked the ML capability to translate it into preventive action. Chronic conditions like hypertension and type II diabetes were being identified reactively — often only after significant deterioration — rather than flagged early when intervention is both cheaper and more effective.
For insurers, late-stage chronic disease management represented a significant and growing liability. For hospital groups, the absence of early-warning tools meant preventable admissions were consuming capacity. Both had incentives to invest in prediction — but lacked the data science expertise and production ML infrastructure to make it work in a regulated clinical environment.
We developed and deployed a predictive model for hypertension risk, built in direct partnership with the health insurers that would use it in production.
Deployed in partnership with major European health insurers. The model identifies patients at elevated risk of hypertension onset before clinical presentation — enabling targeted early intervention programmes that reduce downstream treatment costs and long-term liability for the insurer.
The model was built to the explainability and auditability standards required in clinical settings — with outputs designed to support clinical decision-making rather than replace it, and full GDPR-compliant data handling throughout.