A regional clinic network with eight locations was processing over 2,400 patient intakes per week through entirely manual workflows. Front-desk staff spent 15–20 minutes per patient on documentation, insurance verification, and triage routing — creating wait times that drove patient complaints and staff burnout.
We began with a two-week discovery sprint, mapping the intake workflow end-to-end and identifying the highest-leverage automation opportunities. We chose a RAG-based LLM approach grounded in the clinic's existing clinical protocols, with HIPAA-compliant infrastructure on AWS using HIPAA-eligible services and a signed BAA.
We deployed a conversational AI assistant that guides patients through intake questions, summarizes clinical information for staff review, routes cases by acuity, and pre-populates the EHR. Integration with the existing Epic EHR was built via HL7 FHIR APIs. The system includes confidence scoring so low-certainty inputs always escalate to human review.
Within six weeks of go-live, average intake time dropped from 17 minutes to under 7 minutes. Patient satisfaction scores rose from 3.9 to 4.8 stars. The clinic network projects $1.2M in annualized labor savings and has expanded the system to all eight locations.
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