Healthcare & Life Sciences
Patient Engagement & Retention
3 autonomous agents monitor risk, engage patients, and fill schedules. 45% reduction in no-shows.
Agentic AI Workflow
3 agents guide patients through inquiry, appointment scheduling, and post-discharge follow-up
The Challenge
No-shows were costing millions and creating access bottlenecks for patients who needed care
A multi-specialty practice with 200 providers was experiencing a 22% no-show rate, translating to $8.4M in annual lost revenue. Generic appointment reminders sent 24 hours in advance had minimal impact. When patients cancelled, slots went unfilled 65% of the time.
Chronic disease patients with the highest care needs had the worst attendance rates. The scheduling team of 15 staff spent most of their time making outbound calls that went to voicemail. Wait times for new appointments stretched to 3-4 weeks despite significant schedule gaps from no-shows.
The practice needed a system that could predict which patients would miss appointments, intervene proactively, and automatically fill cancelled slots.
The Solution
Agents that predict, engage, and reschedule to maximize patient access and revenue
Vijan.AI deployed 3 agents in a continuous engagement loop. The Risk Monitor analyzes 25+ signals including past no-show history, appointment type, weather, day of week, and distance from clinic to score each appointment's no-show probability. The Outreach agent sends personalized, multi-channel reminders (SMS, patient portal, phone) timed and phrased based on each patient's engagement patterns. The Reschedule agent detects cancellations, identifies patients on waitlists, and offers open slots in real-time, filling 90% of cancelled appointments within 2 hours.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Triage
Patient Inquiry Agent
Intelligent Patient Inquiry Handling
Autonomously handles patient questions, performs symptom triage, and routes urgent cases to clinical staff.
Input
Patient inquiries (chat, email, phone), symptom descriptions
Output
Triaged inquiries with urgency levels and next actions
- Calls FAQ matching tool to answer common questions about services, hours, insurance
- Calls symptom triage tool to assess urgency and recommend care setting (ER, urgent care, appointment)
- Autonomous decision: escalate emergencies to clinical staff, route routine questions to self-service
- Routes appointment requests to Scheduling Agent, urgent cases to clinical triage
Step 2 · Scheduling
Appointment Scheduling Agent
Automated Appointment Scheduling
Books appointments based on patient preferences, provider availability, and clinical urgency autonomously.
Input
Appointment requests, provider schedules, patient history
Output
Confirmed appointments with reminders scheduled
- Calls availability check tool to find optimal appointment slots matching patient and provider constraints
- Calls reminder send tool to automate SMS/email confirmations and pre-visit instructions
- Autonomous decision: prioritize urgent cases, offer telehealth options, handle cancellations
- Routes appointment confirmations to patients and scheduling data to Follow-Up Agent
Step 3 · Follow-Up
Post-Discharge Follow-Up Agent
Proactive Post-Discharge Engagement
Conducts automated follow-up calls, monitors recovery progress, and predicts readmission risk.
Input
Discharge summaries, care plans, patient contact info
Output
Follow-up status with readmission risk scores
- Calls outreach tool to conduct automated check-in calls and surveys
- Calls readmission prediction tool to identify high-risk patients needing intervention
- Autonomous decision: escalate complications to care team, schedule follow-up visits
- Routes follow-up data to EHR and care management for proactive intervention
Results
Measurable impact within 90 days of deployment
Fewer No-Shows
No-show rate dropped from 22% to 12%. Chronic disease patients saw the largest improvement at 55% reduction.
Revenue Recovered
Combination of reduced no-shows and automated slot filling recovered $5.2M in annual revenue.
Slot Fill Rate
Cancelled appointments filled within 2 hours, up from 35%. Wait times for new appointments reduced from 4 weeks to 9 days.
Staff Reallocated
Scheduling staff redirected from outbound calls to patient experience and care coordination roles.
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
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