UAE health authority ships clinical AI…

01 · The challenge

Problem

Clinicians were sceptical of black-box AI. Adoption of decision-support across the system had stalled at 28%. Pathway variation was high.

02 · How we delivered

Solution

Clinical AI workbench co-designed with clinicians; explainable models with citations; pathway analytics; safety-first rollout with continuous evaluation.

03 · Outcome

Impact

Triage accuracy +21 percentage points. Length-of-stay down 1.4 days. 14 hospitals live. Clinician adoption at 92%. 100% explainability on every decision.

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 Middle East Healthcare Authority 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 Middle East Healthcare Authority 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.

Azure ML FHIR Cognitive Services Power BI Snowflake

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.

They earned the clinicians’ trust. That is rarer than the algorithm.

D Director of Quality · Leading Middle East healthcare authority

What we learnt

Three things we would do again.

01

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

Joint squads with Leading Middle East Healthcare Authority 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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UAE health authority ships clinical AI workbench — adoption at 92%