# Asha: Autonomous Post-ER Referral Workflow

- **Event:** [The Future of Agentic AI in Healthcare - Abridge x Anthropic x Lightspeed](https://cerebralvalley.ai/e/abridge-hackathon)
- **When:** Sat, Jul 18 at 9:00 AM – 10:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Saket Toshniwal](https://cerebralvalley.ai/u/saket), [Madhur Garg](https://cerebralvalley.ai/u/RWEAI)
- **GitHub:** https://github.com/MG1100/asha-referral-agent
- **Demo video:** https://share.descript.com/view/xuk9U2VUxnd
- **Gallery:** https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery/41

Asha: Autonomous Post-ER Referral Workflow

When a patient is discharged from the Emergency Room, the burden of coordinating follow-up care falls entirely on their shoulders. Nearly 50% of patients fail to schedule required follow-ups, leading to a 19% increase in 30-day ER readmissions. Meanwhile, Primary Care Physicians (PCPs) are bogged down by overflowing inboxes of routine, administrative referral requests that eat into complex diagnostic time.

Asha is an autonomous, multi-agent voice workflow designed to eliminate this friction entirely.

When a patient is discharged, an Intelligent Triage Engine (powered by Claude 3 Haiku) ingests the ER's FHIR discharge summary and evaluates the complexity of the required follow-up. If it identifies a routine, administrative task (e.g., a simple orthopedic referral clearance), it routes the case to our "Fast-Track" queue.

From there, Asha proactively calls the patient to secure consent, calls the doctor's office to arrange the fast-track approval, and finally calls the patient back to confirm—zero manual phone calls required from the patient, and zero 30-minute diagnostic blocks wasted by the doctor.

Crucially, Asha is built for healthcare compliance: it is not a "black box." The triage engine is heavily constrained to output its clinical reasoning as structured JSON, ensuring every automated routing decision leaves a transparent, clinician-auditable trace.

Asha turns healthcare's most frustrating administrative bottleneck into a seamless, autonomous experience. Asha: Autonomous Post-ER Referral Workflow

When a patient is discharged from the Emergency Room, the burden of coordinating follow-up care falls entirely on their shoulders. Nearly 50% of patients fail to schedule required follow-ups, leading to a 19% increase in 30-day ER readmissions. Meanwhile, Primary Care Physicians (PCPs) are bogged down by overflowing inboxes of routine, administrative referral requests that eat into complex diagnostic time.

Asha is an autonomous, multi-agent voice workflow designed to eliminate this friction entirely.

When a patient is discharged, an Intelligent Triage Engine (powered by Claude 3 Haiku) ingests the ER's FHIR discharge summary and evaluates the complexity of the required follow-up. If it identifies a routine, administrative task (e.g., a simple orthopedic referral clearance), it routes the case to our "Fast-Track" queue.

From there, Asha proactively calls the patient to secure consent, calls the doctor's office to arrange the fast-track approval, and finally calls the patient back to confirm—zero manual phone calls required from the patient, and zero 30-minute diagnostic blocks wasted by the doctor.

Crucially, Asha is built for healthcare compliance: it is not a "black box." The triage engine is heavily constrained to output its clinical reasoning as structured JSON, ensuring every automated routing decision leaves a transparent, clinician-auditable trace.

Asha turns healthcare's most frustrating administrative bottleneck into a seamless, autonomous experience.

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Markdown version of https://cerebralvalley.ai/e/abridge-hackathon/hackathon/gallery/41. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt