/case-studies / healthcare-patient-scheduling
DATA & PREDICTIVE AI

AI scheduling reduces no-shows for a hospital network

client
Hospital Network
industry
Healthcare
services
AI / ML · Data
duration
6 months
fig.20// skillikzEHRtriageclaimscarecare.metricsrollout68%-30%no-showsusage94coveragelive
// OVERVIEW

Hospital Network — a healthcare organisation — engaged Skillikz on ai patient scheduling: Predictive scheduling that fills clinics and cuts no-shows. This case study sets out the business challenge, the AI-led approach we took, the technologies involved and the measurable outcomes delivered over 6 months.

// TECHNOLOGIES
PythonPyTorch / scikit-learnMLflowSparkAWS SageMaker
-26%
no-shows
utilisation
fairer
access
predictive
reminders
01 // THE CHALLENGE

No-shows wasted clinical capacity and lengthened waiting lists, while manual scheduling couldn't adapt to risk.

02 // OUR APPROACH

We built models that predict no-show risk and optimise scheduling and reminders, improving utilisation fairly.

No-show risk prediction
Risk-aware scheduling and overbooking
Targeted, predictive reminders
Fairness checks and monitoring
03 // THE RESULTS

No-shows fell 26% and clinic utilisation rose, shortening waits while keeping access fair.

Fuller clinics, shorter waits, and we did it without disadvantaging anyone.

Director of Operations · Hospital Network
// HOW WE'D DELIVER THIS TODAY

The AI services behind this outcome

A project like this draws on a focused set of Skillikz services — from first assessment to a working pilot and a clear path to scale.

// MORE WORK
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