Patient scheduling, reminders, and intake — handled end-to-end without staff involvement.
Healthcare front-desk operations handle high volumes of repetitive, time-consuming interactions: appointment requests, insurance verification questions, prescription refill requests, referral status inquiries, and post-visit follow-up. A busy primary care practice receives 80–120 phone calls per day; most front-desk staff spend 60–70% of their time on interactions that follow predictable patterns and require no clinical judgment.
The consequence is a bottleneck at the access point. Patients wait on hold. After-hours callers leave messages and wait for callbacks. Appointment requests submitted online wait for staff to process them during business hours. This access friction drives patient dissatisfaction and, for practices with capacity constraints, lost revenue from appointments that are never scheduled.
Hyro's 2024 healthcare AI data shows that 60–70% of inbound patient calls involve tasks that can be handled by a conversational AI with EHR integration: scheduling, rescheduling, directions, insurance questions, and lab result status. Ushur's patient outreach data shows 40–60% improvement in pre-visit intake completion rates when intake is delivered via conversational automated outreach versus paper forms.
The Patient Engagement & Navigation Agent handles the full range of routine patient interactions — scheduling, intake, follow-up, and navigation — across web chat, SMS, and phone (IVR), integrated with the practice's EHR and scheduling system.
Patients interact with the agent naturally: they can request an appointment for a specific concern, check availability, reschedule, ask about parking and directions, complete pre-visit intake forms, and receive post-visit follow-up for care plan adherence. The agent integrates with the EHR's scheduling module to access real-time availability and book appointments without staff involvement.
For interactions requiring clinical judgment or that fall outside the agent's configured scope, the agent routes to staff with a complete context handoff: what the patient asked, what was discussed, and what information was already collected. Staff receive warm handoffs rather than cold calls.
Conversation handling runs in Go over WebSocket for web chat and Twilio for SMS and voice, with Redis-backed session state that persists context across channels. Intent classification and structured data extraction are handled by a Python NLU layer feeding into LangGraph, which manages multi-step clinical workflows with strict scope boundaries configured per practice. EHR reads and writes use SMART on FHIR R4 or certified API access — scheduling, patient records, and intake data stay in sync with the source system. Claude handles natural language generation with a healthcare-appropriate tone; a rules engine determines what the agent resolves independently versus what routes to staff, with full context passed on handoff. All patient data is processed under BAA-covered infrastructure.
A multi-specialty medical practice with 15 providers and 8 front-desk staff handles approximately 250 patient contacts per day across phone, web, and in-person. Staff spend an estimated 65% of their time on interactions the agent could handle. After-hours contact volume (30% of daily total) goes entirely to voicemail with next-day callback.
After deploying the patient engagement agent, 24/7 coverage handles scheduling, intake, and routine inquiries automatically. Staff handle complex inquiries, billing issues, and clinical escalations during business hours.
These projections are informed by Hyro's published healthcare AI outcomes data, Ushur's patient engagement benchmarks, and MGMA data on medical practice operations.
| Metric | Before | After |
|---|---|---|
| After-hours appointment access | Voicemail only; callback next business day | 24/7 self-service scheduling via chat, SMS, or phone IVR |
| Pre-visit intake completion | Paper forms at check-in; 40–50% completion before arrival | Conversational intake 24–48 hours before appointment; 75–85% completion target |
| Staff time on routine scheduling calls | 60–70% of daily staff time | Automation handles routine volume; staff focus on complex and clinical escalations |
Handles patient interactions across web chat, SMS/MMS, and phone IVR from a single platform backed by a shared Redis session store. A patient who starts on web and switches to phone continues the same conversation — no repeated information, no dropped context. Channel state is maintained across the full interaction lifecycle.
Reads real-time provider availability directly from the EHR scheduling module via SMART on FHIR, then books, reschedules, or cancels without staff involvement. Appointment type rules — new patient vs. established, visit type constraints, provider-specific preferences — are enforced at the scheduling layer. No double-booking, no workarounds.
Runs structured intake 24–48 hours before the appointment through natural conversation: chief complaint, symptom history, medication updates, and insurance verification. Collected data is written back to the EHR patient record so clinical staff arrive prepared. Completion rates are tracked per appointment type to identify where patients drop off.
Sends configurable post-visit check-ins — medication adherence reminders, symptom monitoring for specific diagnoses, care plan progress — at intervals set per protocol. Responses that indicate concern trigger automatic escalation to the clinical team with the full conversation log attached. Non-urgent follow-ups resolve without touching staff queues.
When an interaction exceeds the agent's configured scope or a patient requests a human, the handoff includes everything collected: what was discussed, what data was gathered, and what the patient still needs. Staff receive a structured summary rather than a cold transfer. The agent doesn't drop the patient — it hands them off properly.
Anonymized work with hard metrics — NDA-bound, no client names.
41%
Faster Critical Response
91%
Alert Precision
31%
False Alarm Reduction
“The false alarm rate was a genuine patient safety issue — staff were silencing monitors as a coping mechanism. The AI layer changed the signal-to-noise ratio enough that nurses are paying attention to alerts again.”
— ICU Clinical Director, Regional Medical Centre
We custom-build each agent to fit your data, your rules, and your existing systems.
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