AI-Powered Patient Screening That Saved 40,000 Hours and Improved Care Quality
How we built an intelligent screening system that automates patient triage, symptom assessment, and appointment routing for a hospital network.
40,000
Hours Saved
92%
Patient Satis.
$2.8M
Annual Savings
Emergency Departments Overwhelmed by Routine Cases
A 12-hospital network faced critical challenges: 65% of ED visits were non-urgent, 4–6 hour average wait times, overworked nursing staff conducting initial screenings, and inconsistent triage across facilities.
The hospital needed an AI system to screen patients before arrival, assess symptoms accurately, route to appropriate care level, reduce ED burden, and maintain HIPAA compliance.
- •65% of ED visits were non-urgent cases
- •4–6 hour average wait times
- •$8M annual cost for initial screening staff
Intelligent Multi-Channel Patient Screening Platform
We built a platform with conversational AI (Claude 3.5) for symptom collection, a clinical decision support system with medical knowledge graph and RAG, a smart routing engine (Python + Redis), and EHR integration (HL7 FHIR). Input channels: phone, web portal, mobile app, and in-ED kiosks.
Conversational AI
Anthropic Claude 3.5 Sonnet
Natural language symptom collection, multi-language, voice recognition.
Clinical Decision Support
Custom ML + Medical Knowledge Graph + RAG
ESI scoring, care level recommendation, validated by physicians.
Smart Routing Engine
Python + Redis + Capacity API
ED wait times, specialist availability, load balancing.
EHR Integration
HL7 FHIR + Epic
Bi-directional patient data, HIPAA-compliant.
Measurable Transformation
Average Screening
Patient Satis.
Annual Savings
Operational Impact
- •Nursing hours saved: 40,000/year
- •ED volume reduction: 28%
- •Appropriate routing: 94% accuracy
- •Time to care: 4–6hrs → 15min–2hrs
Results as reported by Regional Hospital Network (12 facilities).
Technical Stack
AI/ML
Backend
Integration
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