AI Patient Triage Cutting Emergency Wait Times in Half
A regional health network with 12 hospital sites was facing a critical patient experience problem: emergency department wait times averaging 4+ hours, driven by manual triage processes that couldn't scale to demand. We built a conversational AI triage agent integrated with their EHR that structured patient intake, scored acuity, and routed patients intelligently — reducing average wait time from 4.2 hours to 1.5 hours.
The health network operates 12 acute care facilities across the region, handling over 400,000 emergency department visits annually. Nursing shortages and increasing patient volumes had pushed manual triage processes beyond their capacity. The clinical leadership team had explored traditional workflow optimisation but concluded that a technology-led solution was necessary.
The triage bottleneck had multiple root causes: manual data entry from patient registration to EHR took 12–18 minutes per patient; acuity scoring was inconsistent across sites and nursing staff; high-acuity patients were not being surfaced fast enough; and nurses were spending 30% of their time on administrative tasks rather than clinical assessment.
12–18 minutes of manual data entry per patient before clinical assessment begins
Inconsistent acuity scoring across sites leading to suboptimal patient routing
High-acuity patients not identified quickly enough — 8% of critical patients waited >60 minutes
Nurses spending 30% of time on administrative intake rather than clinical care
Patient satisfaction scores in the bottom quartile of the health network's peer group
We built a conversational AI triage agent deployed on tablet kiosks in waiting areas, integrated with the network's Epic EHR system via HL7 FHIR APIs. The agent conducts structured symptom intake, applies the Manchester Triage System protocol with ML-enhanced acuity scoring, and generates a pre-populated clinical note ready for the attending nurse — reducing their administrative burden to validation and sign-off.
Clinical workflow mapping across all 12 sites — identifying variation in triage protocols and data requirements
Conversational AI agent built using OpenAI Assistants API with custom guardrails for medical terminology and escalation pathways
ML-enhanced acuity scoring model trained on 3 years of triage outcomes from the client's own EHR data
Epic EHR integration via certified HL7 FHIR APIs — pre-populating clinical notes, not replacing them
Tablet kiosk deployment with accessibility accommodations and multilingual support (7 languages)
Clinical governance process: every AI acuity score reviewed by a registered nurse before any routing decision
Six months post full deployment, average ED wait time across all 12 sites fell from 4.2 hours to 1.5 hours. Nurse administrative burden fell from 30% to 12% of shift time. Patient satisfaction scores moved from the bottom quartile to above the network's peer group median. Zero safety incidents attributable to AI-assisted triage.
Average ED wait time: 4.2 hours → 1.5 hours (64% reduction)
Nurse administrative time: 30% → 12% of shift
Critical patient identification speed: 8% waiting >60 min → 1.2%
Patient satisfaction: bottom quartile → above peer median
Zero AI-related safety incidents in 6 months of operation
“We've seen measurable improvements in both patient satisfaction and staff workload from week one. The AI doesn't replace clinical judgment — it gives our nurses the time and information to exercise it better.”
Dr. Amara Osei
Chief Medical Officer, Regional Health Network
Clinical governance must be designed before technology — the nurse review requirement wasn't a constraint, it was the feature that unlocked clinical trust
Training on the client's own outcome data consistently outperformed generic medical models on acuity scoring accuracy
Multilingual support from day one (not as a retrofit) was decisive for adoption at two sites with high non-English-speaking patient populations
Key Results
Services Engaged
Technology Stack
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