Predicting Attendance, Optimizing Care
No-shows cost the healthcare industry billions annually. We developed a Telehealth Appointment Optimizer that uses machine learning to predict the probability of a patient missing their appointment. Based on this score, the system automatically triggers personalized SMS reminders or suggests intelligent overbooking strategies to ensure provider time is never wasted.
The Challenge
The Solution
Key Features
No-Show Prediction
AI model that analyzes 50+ factors to predict attendance probability.
- Historical attendance
- Weather patterns
- Distance/Traffic data
Smart Reminders
Automated, conversational SMS reminders that allow instant confirmation.
- Two-way messaging
- Natural language processing
- Multi-language support
Dynamic Waitlist
Automatically fill last-minute cancellations from a high-priority waitlist.
- Instant notifications
- Priority scoring
- One-click booking
Technology Stack
Project Timeline & Results
Data Analysis & Modeling
Trained model with 85% accuracy in predicting no-shows.
Aggregated 2 years of historical appointment data. Engineered features such as 'days since booking', 'previous no-shows', and 'insurance type'. Trained and validated a Random Forest classifier.
Integration & Automation
Deployed automated SMS system reducing manual calls by 90%.
Built the API integration with the clinic's EMR. Set up Twilio workflows for reminder sequences (72h, 24h, 2h). Implemented logic to handle patient replies (e.g., 'Reschedule').
Pilot & Rollout
Reduced no-show rate to under 5% in the first month.
Rolled out the system to 5 providers initially. Monitored system performance and patient feedback. Fine-tuned the overbooking logic to prevent long wait times. Expanded to the entire network.
Related Services
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