AI & Automation · Healthcare
An AI triage assistant cut patient intake time by 42% across nine hospitals
A nine-hospital network was losing outpatient capacity to manual intake. We built a clinician-supervised AI triage assistant that structures symptoms, routes to the right department and pre-fills the EMR — cutting intake time by 42%.
- Client
- Confidential hospital network
- Region
- India (multi-city)
- Timeline
- 4 months to production
- Team
- 6 specialists
42%
Faster average patient intake
9
Hospitals live on the assistant
96%
Clinician acceptance of routing suggestions
100%
Suggestions with an audit trail
Context
The situation we found
Nine-hospital private network with 2,400 daily outpatient visits
Engagement model: Discovery sprint + build + managed AI operations
Front-desk staff captured symptoms free-text, then a nurse re-interviewed the patient before routing. The duplication added an average of eleven minutes per patient and produced inconsistent department routing.
Challenge
What was blocking the business
The constraints that had to be resolved before any technology decision mattered.
Approach
How we solved it
The decisions that produced the outcome, not a feature list.
Human-in-the-loop by design
The assistant proposes; a clinician confirms. Every suggestion is reviewable, overridable and logged against the reviewing user.
Structured symptom capture
An LLM converts multilingual free-text and voice input into a structured intake record mapped to the network's own triage taxonomy.
Retrieval grounded in network protocols
Routing recommendations are grounded in the network's approved clinical protocols, not general model knowledge, with citations shown to the clinician.
Privacy and audit controls
Consent capture, PII redaction before model calls, regional data residency and a complete, immutable audit log.
Delivery roadmap
How the programme ran
- 01
Discovery & risk assessment · 3 weeks
Process shadowing, data protection review and success metric definition.
- 02
Pilot build · 6 weeks
Intake assistant, protocol retrieval and clinician review console for one hospital.
- 03
Clinical validation · 4 weeks
Supervised pilot, accuracy benchmarking and protocol tuning.
- 04
Network rollout · 5 weeks
Staged rollout across nine sites with training and managed AI operations.
Results
What changed after go-live
Measured in the client's own systems against the baseline captured in discovery.
Client view
In their words
“The assistant never makes a clinical call on its own — it removes the paperwork around one. That distinction is why our clinicians trust it.”
Chief Medical Information Officer
CMIO, Confidential hospital network
Stack & integrations
What it was built on
Technology stack
Integrations
Capabilities used
Services and platforms behind this work
FAQ
Questions about this engagement
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