Local Services · Scenario 07

Dental Clinic

Empty chairs from last-minute no-shows quietly cost a clinic more than any campaign could earn. We predict who won’t show and fill the gap before it opens.

Method · Logistic-regression risk scoring

The situation

A no-show is pure loss: the room, the hygienist’s hour, the slot another patient wanted — gone with no notice. Most clinics treat every booking as equally certain and send everyone the same single reminder, so the chronically unreliable slip through and the calendar bleeds.

Front-desk staff sense which appointments are risky but have no system to act on it — no way to double-book intelligently, over-fill a risky morning, or reach the patients most likely to vanish.

14%

appointments no-show

$220

lost per empty chair

1 reminder

same for all patients

No signal

of who is at risk

Where we dig for the truth

We build a model that scores every upcoming appointment for no-show risk from the patterns already sitting in the practice software.

Appointment historyNo-show / cancel recordLead time to bookingPatient age & recall typeReminder responsesDay, time & weather

A history of past misses and a long lead time dominate the risk. The model combines them into one score per appointment.

Our approach — No-Show Prediction & Schedule Optimization

A logistic-regression model assigns each upcoming appointment a no-show probability. High-risk slots get an extra, personal confirmation and an easy option to move; genuinely high-risk mornings are gently over-booked so a miss still leaves the chair full.

A short waitlist of flexible patients, texted the moment a high-risk slot looks shaky, turns would-be empty hours into same-day fills.

Most flagged slots are saved before they ever become an empty chair — and the few that aren’t get backfilled from the waitlist.

What changes

Same patient list, a calendar that defends itself. Representative for a two-operatory practice.

Why this is not "social media management"

We didn't run a new-patient ad blitz to paper over the gaps. We closed them — predicting the no-shows costing the practice a chair a day and filling them before they happened. That's statistics protecting a P&L, not a campaign.

Frequently asked questions

How do you reduce dental no-shows?

We build a model that scores every upcoming appointment for no-show risk from your practice data. High-risk slots get an extra confirmation and an easy reschedule, genuinely risky mornings are gently over-booked, and a flexible-patient waitlist backfills shaky slots same-day.

What data predicts a no-show?

The strongest signals are a history of past misses and a long lead time between booking and appointment, plus factors like new-patient status and whether a reminder was acknowledged. A logistic-regression model combines them into one risk score.

How is this different from a marketing campaign?

Rather than advertising for new patients to paper over the gaps, we close the gaps — recovering the chair-hours no-shows were costing you. It is statistics protecting your schedule. Book a marketing audit.

Want this run on your numbers?

Share your appointment history and we’ll score where your calendar is leaking.