Evaluate no-show prediction for scheduling
No-shows leave holes in my clinic day, but a fixed number of overbooks makes some days a mess.
Prediction-model study Needs a HIPAA agreement (BAA) Organization involvement
Source check: 2026-11-11 · Physician review: not yet recorded
The workflow
- Ask your scheduling vendor or data team whether an approved no-show model is available.
- Evaluate it on your clinic’s historical data, including calibration and errors across patient groups.
- Have operations staff assess reminders and scheduling interventions before changing bookings.
- Track access, patient waits, overtime and unexpected double-bookings during any approved pilot.
Tools and cost
USD unless stated otherwise. Billing periods and eligibility matter. A tool listed here was not necessarily tested in the cited study.
What the evidence supports
A model developed from 1,260,083 appointments had test-set AUC 0.83. At the reported cutoff it detected 71% of no-shows, but only 18% of flagged appointments were actual no-shows.
Primary study · Read the source ↗
Where it goes wrong
AUC measures discrimination, not percentage accuracy. Most flagged patients still attended. The paper did not establish improved waiting times or revenue from overbooking; its cutoff should not be copied into a different clinic.