Twenty sites, one waiting list, and patients who will travel eight minutes but not thirty.
Network capacity is never evenly distributed. One clinic is booked out five weeks while another eleven minutes away has Tuesday afternoons empty — and no one in the booking flow ever tells the patient.
Capacity exists. It is in the wrong postcode.
Each site defends its own diary because each site is measured on its own utilisation. The network optimum and the site optimum diverge, and the patient absorbs the difference as a five-week wait.
No-shows make it worse: a distributed network with a 14% no-show rate is throwing away the equivalent of three full clinics, spread thin enough that nobody sees it as a number.
- —Wait times varying wildly between neighbouring sites
- —No-show rates unexamined per site, slot and channel
- —Self-referral funnels dropping patients at the calendar step
- —Locum and rotating staff assigned by habit
Three areas of the platform carry this.
The rest of the product is present and dormant. These three are the reason you would move.
Network-wide availability search
Booking searches every site the patient can reach within their stated travel tolerance, ranked by earliest clinically appropriate slot rather than by which site owns them.
No-show prediction and overbooking policy
Per-slot risk from history, channel, lead time and weather-of-the-week patterns; controlled overbooking with an explicit policy ceiling, not a guess.
Load balancing across sites
Rotating and locum staff are proposed where the network's modelled demand is highest that week, with the utilisation impact shown before the assignment is committed.
What your tenant hierarchy looks like.
Clusters are the unit that matters — travel-time neighbourhoods, not administrative regions.
Numbers, with the assumptions attached.
Modelled on a 22-site outpatient network with a 14% no-show baseline and a five-week median wait at its four busiest sites.
- Cluster-wide queue
- Travel-tolerance search
- Risk-weighted reminders
- Placement against modelled demand
These are modelled figures, not a named customer result. Every input is stated so you can substitute your own; where a range is meaningful we publish the sensitivity rather than a single confident number.
Bring one week of real demand to a demo and we will rerun the model live on your data.
See what it costs →The questions this room always asks.
Reporting is dual: site utilisation and cluster wait time are both first-class metrics. Cross-site offers are attributed to the referring site so the incentive stops fighting the patient.
You may be more than one of these.
Send us one cluster's month of bookings.
We will show the wait each site published and the wait the cluster could have offered.