No-Show Prediction + Safe Overbooking
Predict no-shows and overbook safely - recover lost slots without double-booking real patients.
TOMORROW · CHAIR 2 - RISK SCAN + OVERBOOK
Appointments · Wed
expected empty chairs: 1.1 → overbooked 1 · your cap: max 2/day · double-booking risk held under 3%
11:00 · M. Petrauskas · hygiene visit
no-show probability · calibrated - 6 in 10 like this one don't come. Booking metadata only, never medical records.
Why - top 3 drivers
🟡 patient messaging is clinic-approved - templates & frequency yours
How it works
Connect the scheduling system
Mediq, Clinica or custom - read-only. The model sees booking metadata only: lead time, history, reminders. Never diagnoses, never treatment data.
Calibrated risk per appointment
Overbooking math is only safe with true probabilities - calibration isn't a feature here, it IS the product. Validated on your history before go-live.
Overbook under YOUR cap
Expected-cost optimisation weighs an empty chair against a turned-away patient. The cap and the turn-away tolerance are your risk decisions, not the model's - and the cap is never exceeded. It bounds your exposure; it does not promise zero.
Reminders, clinic-approved
High-risk appointments can trigger an extra reminder - with templates and frequency your clinic signs off. Seasonal drift watched (holidays shift behaviour).
Methodology reference
The published method this build is based on - and what it did in production.
American Airlines pioneered no-show forecasting and overbooking optimization (DINAMO), valued at ~$1.4B over three years.
No-show → overbooking optimization · as reported by INFORMS ↗
Referenced for methodology only. AimRank is not affiliated with, endorsed by, or a customer of these companies; each links to its own public source.
The Four Guarantees™ - this build
Measured value
On the reference build: €13.64 recovered per no-show, and a held-out backtest that realised 1.18x its own forecast. Brier/ECE eval gate - the overbooking math demands true probabilities.
Defensible
Per-appointment risk reasons; no protected-class or clinical features; GDPR-grade (health-adjacent) audit log of every decision.
Self-correcting
Calibration tracked continuously; seasonal + behavioural drift alerts; corrections feed the model monthly.
Yours & everywhere
Scheduling-system write-back with the cap enforced; your cloud, full source. MCP tools for clinic-ops agents (🟡 messaging gated).
The number, sized honestly
Reference buyer: Lithuanian / Baltic private clinic group, ~500 appointments/week, ~12% no-show rate, ~€60 revenue per visit.
Three ways to own it
| Tier | What you get | Price | |
|---|---|---|---|
| Scaffolding ★ | The full repo - calibrated model + overbooking policy + reason layer, audit log, MCP server. Reference run on public clinic data. | €1,290 | |
| PoC ★★RECOMMENDED | A decision, not software. You export your appointment history once (CSV - no integration); we refit and calibrate on your patients, then replay the overbooking policy against weeks that already happened. You get the measured euro number, the eval-gate verdict, the Annex IV dossier and a written go/no-go - including 'don't buy the build'. | from €4,900 | |
| Implementation ★★★ | Production: live in your scheduling flow, caps enforced, monthly recovered-slots report. The agreed number attaches here - set from YOUR PoC backtest, not from our reference figures. Calibration monitoring, drift alerts and seasonal retraining run on the €390/mo plan, because a model that keeps earning its number needs watching, not just shipping. | from €12,000 |
★ = engagement depth. PoC is the recommended path: quality proven on your data before production money. The PoC carries no performance guarantee by design; the agreed number (conservative) attaches at Implementation, informed by the PoC report.
What we don't promise
Ready to see your own number?
Request the build: within 48h you get a personal reply with the value sized to your volume.
No commitment · reply within 48h · your data stays in the EU