A five-hospital academic health system. Research-intensive. They wanted population health analytics — disease prevalence patterns, readmission risk prediction, resource utilization optimization — across their entire patient population. The problem: their data governance committee wouldn't approve any architecture that moved identifiable patient data outside the hospital's on-premise data centers.
Two analytics vendors had proposed cloud-based solutions. Both were rejected by the data governance committee. A third vendor proposed an on-premise solution that would take 14 months to implement and cost $3M in hardware alone. The CMIO was stuck — needed the analytics, couldn't move the data, couldn't afford to wait.
We proposed a federated analytics architecture — computation goes to the data, not data to the computation. No PHI leaves the hospital's network. Analytics results (aggregated, de-identified) surface to dashboards.
Federated analytics engine running inside each hospital's on-premise infrastructure. Local data processors executed queries against clinical data warehouses without extracting PHI. Results were aggregated and de-identified using HIPAA Safe Harbor methodology before surfacing to the centralized analytics dashboard. Real-time population health metrics: disease prevalence, readmission risk scores, utilization patterns, outcome disparities — all without a single identified patient record leaving the hospital network.
Population health analytics across 1.2M patient records — operational within 14 weeks. Zero PHI exposure. The data governance committee approved the architecture unanimously. Readmission risk prediction model identified high-risk patients with 89% accuracy, enabling targeted intervention programs that reduced 30-day readmission rates by 18%.
The first call is with a senior engineer.
Tell us the industry, the regulatory environment, and what needs to be built. We'll tell you if we've done it before, what it should cost, and how long it takes.