Run on sample data from an invented clinic. Every number from the analysis is sample data.
We ran an AI analysis on 12 months of sample data from an invented two-branch clinic. Here is what came out, and what a medical director would do with each finding.
The example: Example Medical Centre (invented): two branches, 9 doctors, 12 months of appointments, invoices, packages and referral sources. All of it is sample data.
Clinics collect a lot of data: appointments, invoices, packages, referrals. Most of it is only opened when something has already gone wrong.
The reports in the system show totals. They rarely say where money leaks, which hours sit empty or which patients stop coming.
We take exports or read-only access to your booking, billing and records data. Nothing in your systems changes.
We match appointments, invoices, packages and referral sources, and remove personal details the analysis does not need.
AI scans the data for leakage, utilisation, no-shows, package use and referral sources.
Each finding is checked against the raw data before it reaches you. If it cannot be checked, it is not in the report.
We go through the findings with the owner or medical director and agree the 2 or 3 actions worth taking first.
No-show rate by how far ahead the visit was booked
| Same or next day | 6% |
|---|---|
| 2 to 7 days | 11% |
| 8 to 14 days | 17% |
| More than 14 days | 24% |
Finding: 6% of completed treatments had no invoice line, and 9% of invoices had a discount above the clinic’s own policy, most of them in one branch.
What a medical director would do: Match every completed treatment to an invoice each evening; discounts above policy need a manager’s approval.
Finding: Evening hours (5 pm to 9 pm) were 86% booked; weekday mornings (9 am to 12 pm) only 44%.
What a medical director would do: Move part of one doctor’s schedule to the evening, and offer morning slots first to recall and package patients.
Finding: Visits booked more than 14 days ahead were missed four times as often as visits booked for the same or next day (24% vs 6%).
What a medical director would do: Ask far-ahead bookings to confirm 3 days before and again the day before; release unconfirmed slots to the waiting list.
Finding: Of 1,240 package sessions sold, 310 (25%) were still unused 6 months after purchase.
What a medical director would do: Remind package holders of their remaining sessions, with a booking link in the same message.
Finding: Patients referred by other patients were 18% of new patients, but 31% of the patients still visiting after 12 months.
What a medical director would do: Thank referring patients and make referring easier, before spending more on new channels.
On your own data, the analysis would track:
Tell us on WhatsApp how your clinic works today. We will tell you plainly whether this fits, and what the first step would be.
Ask us on WhatsAppNot happy after the first month? We refund everything you paid us.
An AI assistant on the clinic's WhatsApp answers enquiries day and night in Arabic and English, books into your system, sends reminders and recalls, and hands anything clinical to your team.
Patients book, get reminders, "your results are ready" notices and visit updates on WhatsApp or a simple web link, while a connector keeps everything in sync with the system your clinic already uses.
An invented four-branch clinic group gets a part-time CTO. The first 90 days: audit, roadmap, vendor and build decisions, compliance checks, AI opportunities and the team.