Data
Analyse clinic utilisation
Where the schedule leaks, and what it costs.
Fill it in
A year if you have it.
No-shows cluster by time, type and lead time.
And say where it stops being safe.
Your prompt
Analyse the schedule. Report the no-show rate, the late-cancellation rate and the unfilled-slot rate separately. They have different causes and different fixes, and a single "utilisation" number hides which one you have. Find the PATTERNS. No-shows cluster by appointment type, by time of day, by day of week and above all by lead time: an appointment booked eleven weeks out behaves nothing like one booked on Tuesday for Thursday. Model overbooking against the observed rate and say where it stops being safe. The cost of a patient waiting an hour is real, and a model that only counts revenue recommends something a clinic should not do. Quantify the lost capacity in slots and in money.
Use Analyse clinic utilisationOpens with everything above already filled in.
Why this works
No-shows cluster by appointment type, time of day and above all lead time: one booked eleven weeks out behaves nothing like one booked on Tuesday for Thursday. This finds the patterns and says where overbooking stops being safe.