Leading national provider unifies 9 EHR…

01 · The challenge

Problem

Clinicians worked across 9 disconnected EHR/lab/imaging systems. Cross-site reporting took 7+ days. Clinical-trial recruitment was hand-curated. Audit fatigue was at an all-time high.

02 · How we delivered

Solution

Lakehouse with FHIR-native semantic layer, governed clinical AI workbench, and real-time dashboards. Migration completed without a single read-cutover incident, parallel-run validated.

03 · Outcome

Impact

Reporting latency cut from 7 days to 6 minutes. 38 clinician hours per month returned. Audit pass rate at 100%. Trial recruitment time halved.

How we delivered

Programme phases.

Five phases. One accountable team. Every phase had a named decision point and a measurable outcome.

Discovery & alignment

2–3 weeks

Workshops with the Leading National Healthcare Provider executive team, baseline metrics, target outcome tree, programme governance set up.

Design & architecture

4–6 weeks

Reference architecture, security blueprint, joint squad model agreed. Data model and integration contracts published.

Build & live-parallel

Q2 onwards

Vertical slice built and run live-parallel against the existing system. Continuous integration, daily deploys, weekly business demos.

Cutover & scale

Mid-programme

Phased cutover, audit-aligned reconciliation, scaling out of squads, capability transfer to Leading National Healthcare Provider teams.

Run & continuous improve

Steady state

Managed run with named SLOs, quarterly value reviews, and a 15% optimisation budget reserved for improvement work.

Engineering view

Architecture overview.

Foundations

Cloud landing zone, identity, network, security baseline. Data fabric with lineage-by-default. Audit-grade observability stack from day one.

Application & integration

Domain-aligned microservices behind a published API surface. Event-driven core with CDC into the data fabric. Live-parallel capability built in, not bolted on.

Trust & governance

RBAC, audit logs, lineage, policy-as-code. Model risk records for every production model. Compliance posture on the executive dashboard, not in a quarterly slide.

Built on

Technology stack.

Production-grade choices, defended by track record. The stack is one engineering decision among many — but a load-bearing one.

Databricks FHIR Iceberg dbt OpenSearch Tableau

Trust by design

Governance & assurance.

01

Programme assurance

Independent assurance reviews at each phase gate. Findings tracked in a single risk register with named owners and remediation deadlines.

02

Security & data

ISO 27001, SOC 2 Type II controls applied throughout. Data lineage captured by default; sensitive data tokenised at the edge.

03

Clinical safety

DCB 0129 / equivalent clinical safety case maintained for every AI-assisted journey. Continuous evaluation against ground-truth panels.

04

Patient privacy

Privacy-by-design for PHI. Consent capture and purpose-limited access enforced at the data-fabric layer.

Their joint-squad model meant my team came out stronger than they went in.

H Head of Architecture · National healthcare provider

What we learnt

Three things we would do again.

01

16 months from kickoff to first regulated outcome — squad density and decision velocity matter more than headcount.
02

Joint squads with Leading National Healthcare Provider engineers stayed in place after go-live. Ownership did not transfer in a hand-off — it grew in place.
03

Live-parallel for a meaningful window before cutover bought us trust. The cutover itself was a flag flip, not a war room.

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Leading national provider unifies 9 EHR systems into one data fabric