Booking
1) Conversational booking & rescheduling
- Natural-language prompts for visit type and preferred window
- Live provider/room slots; double-booking guards; time-zone safe
- One-tap reschedule/cancel links with automatic calendar updates
Case Study — Patient Access & Scheduling
Clinic Appointment-Setting
A.I. Agent (Chat/SMS)
Client: Regional outpatient clinic network (primary care + allied health) operating across three locations.
Client at a Glance
- Operations: Multi-site clinics with centralized scheduling policies
- Channels: Web chat, SMS, and optional WhatsApp
- Goal: Reduce no-shows, free front-desk capacity, and allow after-hours booking
Challenge
Manual phone scheduling and email follow-ups were bottlenecking access and front-desk capacity.
- High call volume: Peak-hour waits 6–12 minutes; abandoned calls led to lost bookings.
- Fragmented reschedules: No unified workflow → empty gaps in provider schedules.
- No-show rate: Inconsistent reminders; staff sent manual emails/SMS.
- Data entry load: Intake re-typed into EHR/Calendar → errors and rework.
- After-hours gap: Patients couldn’t self-serve; messages piled up for morning callbacks.
Needed: a conversational agent that books, verifies, reminds, and hands off to humans when needed.
Our Solution
A chat/web/SMS appointment-setting agent that verifies patients, surfaces real-time availability, books or reschedules, collects pre-visit info, and sends reminders—with seamless staff handoff for edge cases.
Intake
2) Patient verification & intake
- DOB + phone/email match or link-based magic code
- Reason for visit, meds/allergies, consents → written back to EHR
- Optional co-pay hold via secure pay-link; receipt stored with encounter
Utilization
3) Reminders, waitlist, & no-show recovery
- SMS/email reminders (T-48h, T-24h, T-2h) with confirm/modify
- Intelligent waitlist prioritizes urgency, proximity, history
- Missed-visit outreach proposes new times and updates schedules
Safety
4) Escalation & guardrails
- Clear staff handoff for clinical triage or insurance questions
- Policy guardrails: no diagnoses or medical advice
- PII redaction in logs; audit trail on all scheduler actions
Technology Stack & Architecture
Frontend
- Web chat widget + responsive UI
- SMS via Twilio; optional WhatsApp
- Accessibility-minded flows
Orchestration & LLM layer
- Node.js service with policy guardrails and retry queue
- RAG over clinic FAQs and policy prompts
- Tool-use for calendar/EHR actions
Integrations
- Calendar: Google / Microsoft 365
- EHR: FHIR/HL7 where available
- Payments: Stripe/Square; Email: SendGrid; SMS: Twilio
Data & security
- Postgres (session state); Redis (short-lived context)
- Event logs to ELK/CloudWatch
- TLS in transit; PHI minimized; role-based console + audits
Pilot Scope & Challenges
- Pilot across 3 clinics for 8 weeks (new & returning, in-person & telehealth)
- EHR variance: different APIs solved with thin adapter + fallback ICS feed
- Reminder preferences: agent asks SMS vs. email and stores preference
- Edge cases: insurance pre-auth & referrals auto-escalated with transcript
Outcome: predictable scheduling with fewer manual interventions and smoother reschedules.
Impact & Outcomes
First 60–90 days after launch:
- Booking conversion: +27% vs. phone-only baseline
- Abandoned calls: −41% during peak hours
- No-show rate: −22% with structured reminders and easy rescheduling
- Front-desk time on admin: −34%
- After-hours bookings: 31% of all appointments
Access improved, schedules filled more consistently, and staff focused on patients—not phone tag.
Future Roadmap
- Insurance eligibility checks before booking for specific visit types
- Multi-language conversations and accessibility enhancements (WCAG)
- Smart triage forms to route urgent vs. routine visits
- Deeper EHR write-backs (orders/referrals metadata)
Client: [Name Withheld — Regional outpatient clinic network]
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