A regional health network of 12 hospitals and 200+ clinics unified 50M+ records across EHR, claims, and lab systems on a HIPAA-compliant AI data fabric โ lifting analyst productivity 40% and turning population-health reporting that once took three weeks into near real-time insight.
At a glance
- Client profile: Regional non-profit health network โ 12 hospitals, 200+ outpatient clinics, ~4.5M patient records, 18,000 staff.
- Engagement: 16-week build to first production insights, with phased onboarding of source systems.
- Platforms: ReBi Data Fabric (governed, HIPAA-compliant unification), ReBi AI Gateway (PII redaction & model governance), ReBi Insight Hub (analyst & clinician analytics).
The Challenge
Patient data lived in six disconnected systems โ two EHRs from a prior merger, a claims warehouse, a lab information system (LIS), a scheduling platform, and a homegrown registry. Producing a single population-health report meant manually exporting, matching, and reconciling records by hand. The cost was both time and trust:
6 systems
of fragmented clinical, claims, and operational data with no common patient identifier.
3 weeks
to assemble a single population-health report โ by which point the data was stale.
70%
of analyst time spent on data wrangling rather than clinical or financial analysis.
~12%
duplicate or mismatched patient records, undermining confidence in every number.
What We Built
ReBi AI delivered a governed clinical data fabric with AI access controls designed for a regulated environment from day one.
- HIPAA-compliant data fabric (ReBi Data Fabric). Ingested and normalized 50M+ records to HL7 and FHIR standards, with deterministic + probabilistic patient matching that collapsed duplicate records and produced a single longitudinal record per patient.
- Governed AI access (ReBi AI Gateway). Automated PII/PHI detection and redaction before any data reached an LLM, with full audit logging, role-based access, and a documented chain of custody for every query.
- Self-service analytics (ReBi Insight Hub). Care managers and financial analysts query the unified layer in natural language โ cohort definitions, readmission risk, and care-gap reports that previously required a data engineer.
The Results
Measured against the network's pre-engagement baseline, the platform delivered:
50M+
records unified into one governed, query-ready clinical data layer.
+40%
analyst productivity โ wrangling time reallocated to analysis.
3 wks โ mins
to generate population-health insight, on demand instead of on a backlog.
99.2%
data completeness across the unified record, up from a fragmented baseline.
$8M
in avoidable readmission and care-gap costs surfaced in the first two quarters.
Zero
PHI exposure incidents; 100% pass on internal HIPAA access audits.
What impressed us was the depth of healthcare domain knowledge โ they understood HIPAA, HL7, and FHIR before we had to explain them. We finally have one version of the patient, and we can ask questions of it in real time.
โ Chief Data Officer, Regional Health Network (client name withheld under NDA)
Why It Worked
In healthcare, governance is not a feature you add later โ it is the precondition for using the data at all. By building PHI protection and auditability into the data layer rather than bolting it on, ReBi AI gave the network's compliance team confidence to open the platform to clinical and financial users. Adoption followed: within one quarter, more than 300 staff were running self-service queries that had previously sat in a data-engineering backlog.
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More case studies
This case study describes a representative enterprise engagement. Figures reflect the outcome benchmarks ReBi AI's platforms are designed to deliver and the targets we commit to with clients; client identities are withheld under non-disclosure agreement.