Predictive Patient Tracking
Appointment cancellations were eroding revenue and staff capacity without any predictive mechanism to intervene before the slot was lost.
- Timeline
- 8 week build and integration, 4 week calibration period, ongoing model refresh cadence.
- Scope
- Predictive cancellation risk modelling, tiered intervention design, slot recovery workflow, and utilisation reporting across a 6 location aesthetics group.
- Model
- Embedded delivery with the group's operations manager as primary owner. Training provided to all front of house leads on intervention tiers and escalation logic. Governance checkpoint at 30 and 90 days post launch.
The outcome
Cancellation rate reduced by 31% within 90 days; treatment room utilisation improved from 67% to 79%.
Findings
What we built it around.
Cancellation risk scoring model
trained on 18 months of historical appointment data, incorporating lead time, patient tenure, treatment type, booking channel, and previous cancellation history
Tiered intervention logic
three tier communication sequence mapped to risk score, standard reminder for low risk, personalised check in for medium risk, direct outreach from front of house for high risk
Slot recovery workflow
automated identification of waitlisted patients matched to vacated slots by treatment type and location, with templated outreach reducing fill time from hours to minutes
Utilisation dashboard
real time and rolling 30 day view of room utilisation by location, practitioner, and treatment category to identify structural capacity issues versus behavioural ones
Feedback loop
weekly model recalibration incorporating new cancellation and attendance outcomes to maintain scoring accuracy over time
Results
What changed.
Cancellation rate reduced from 22% to 15
2% within 90 days, a 31% relative reduction, with same day cancellations falling disproportionately from 11% to 6.4%
Treatment room utilisation improved from 67% to 79% across the group, recovering approximately £14,000 in monthly revenue that had previously been lost to vacant slots
Slot recovery workflow reduced average time to refill from 4
2 hours to under 45 minutes on high demand days
Front of house staff reported a meaningful reduction in reactive rescheduling workload, with structured intervention protocols replacing ad hoc decision making
Takeaway
Cancellation rate reduced by 31% within 90 days; treatment room utilisation improved from 67% to 79%.
Medical Aesthetics
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context. We will identify the highest leverage intervention areas before any scope is agreed.