Shorter waits by filling the slots that were already there
A weekly view of no shows by service and how long new patients wait, so the practice manager can act on missed appointments before hiring.
The problem
An allied health group saw waitlists growing and assumed it needed more clinicians. Its booking system held the answer, but no one had time to pull no show and wait time data together.
What the numbers say
Each missed appointment is a slot a waiting patient could have used. Reducing no shows is the fastest way to shorten the wait.
How FDX uses it
From the data to an approved draft
- 01Connect the data
We connect the practice management and booking systems into one model, refreshed daily.
- 02Draft, then approve
AI drafts reminders, waitlist offers and referral letters. Clinical content is always reviewed by the treating clinician.
- 03Keep it private
Patient health information is masked before any AI model sees it, with onshore hosting available and no data used for training.
Key numbers on the dashboard
- Appointments this week1,236Across 4 clinics
- No show rate8.1%100 missed appointments
- Wait for new patients13 daysTarget 7 days
- Clinician utilisation82%Booked hours as % of available
Next steps it points to
Reminders. Send two step reminders for psychology and dietetics. Templates are drafted with AI and approved by the practice manager.
Waitlist. Offer cancelled slots to the waitlist the same day.
Clinicians. AI can help draft referral letters and summaries, with the treating clinician reviewing every one.
ExampleExample case study with illustrative sample data. Not a real client or real results.
Next exampleSocial assistanceMaking sure participants get the support in their plans
The dashboard is one part of the work.
It gives the team one trusted view of the numbers. The bigger gain is AI working from that same data to draft the follow ups, with a person approving every step.
When AI drafts from your data, personal details are masked before the model sees them. Onshore hosting is available, and your data is not used for training.