Industrial AI practice  ·  Gulf  ·  Arabic and English Reply within one working day
Core data science / forecasting & decision support

A five-site outpatient hospital network

Healthcare

← All documented proof

The shape of this problem

The same judgment, made differently every time

The problem

Clinics ran a flat 8% no-show assumption and overbooked uniformly, while real no-show rates swung from 4% to 22% by specialty. The result was patients waiting up to 90 minutes in some clinics while specialists sat idle in others.

What we built

Before building any prediction, we audited the scheduling workflow and found the "no-show" field itself was unreliable - late cancellations, walk-ins, and true no-shows were logged inconsistently across sites. We fixed the data definitions first, then built a per-appointment risk score translated into simple scheduler decision rules, and piloted at one high-variance site for six weeks before expanding.

What moved

Measure Before After
Network no-show rate 14.6% 9.1%
Patient wait time in piloted specialties down 31%
Specialist chair utilisation +11 points, recovering ~240 slots per month per site

† Measured against the prior level of the same measure. The source publishes the size of the movement, not the figure it moved from.

Network no-show rate

14.6% 9.1% 0% 100%

Patient wait time in piloted specialties

Prior level 0 100 200 down 31%

Measured against the client's own prior process, indexed to 100. The source publishes the size of the movement, not the absolute figure it moved from.

Figures are drawn from the practice's own delivery records for the engagement named, measured against the process that preceded it. They have not been through third-party audit, and none is presented as an average across clients.

The win wasn't a fancier algorithm - it was that we stopped treating every specialty the same, and our schedulers trusted the tool because they could see the reasoning.

Director of Ambulatory Operations

Service line
Forecasting & Planning AI
The finding matters more than the number here. Both cases repaired data definitions before modelling, which is the practice's whole thesis in miniature.

Start here

Which of the four is yours?

Tell us the documents and the monthly volume and we will send the two closest records, with the proof behind each and the honest note where the match is partial.

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