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Healthcare

An ML squad to triage patient intake for a Jeddah health network

Reduced triage backlog by 70% with a Ministry-of-Health-compliant ML pipeline.

Client

Shifa Health Network (Jeddah)

Duration

8 weeks

Squad shape

1 Staff ML Engineer · 1 Data Engineer

Shifa's intake team was drowning in unstructured patient referrals across 12 clinics in Jeddah. Their internal data team was 6 months booked.

The squad

A staff ML engineer and a data engineer, both with healthcare backgrounds and Arabic NLP experience. Live in 9 days from kickoff.

How we worked

  • Weeks 1-2: Audit existing data, define the labels, write a privacy-safe sample contract aligned with NPHIES + MoH guidelines.
  • Weeks 3-5: Train the first triage model on de-identified Arabic intake notes; ship the eval harness.
  • Weeks 6-7: Wire it into the Shifa intake app behind a clinician override.
  • Week 8: Production rollout with a 20% safety margin and a rollback plan.

Outcome

  • 70% reduction in unhandled intake at end-of-week.
  • Clinician override rate stabilized at 8% — well below the agreed threshold.
  • Full audit trail meeting Saudi Ministry of Health requirements from day one.

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