How Erasmus MC prevents 6,000 no-shows a year

Reminders are useful, but they do not reach every patient who may miss an appointment. This case study explains how Erasmus MC used appointment data to focus follow-up and measure the difference it made.

More than 42,000 missed appointments a year across outpatient clinics
A prediction model that identified around 80% of no-shows in the analysis
Targeted interventions for patients with an elevated no-show risk
Around 6,000 no-shows prevented each year across outpatient clinics
€270,000 in lost care capacity recovered
Results measured against a comparison group

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What you’ll learn from this case study

AI prediction of no-shows

Identify in advance which appointments are at risk

Targeted interventions

Prioritise timely follow-up for higher-risk appointments

Measurable results

See how the impact was measured in practice

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A personal note from Joris

I’m Joris Tukker. From my work in healthcare, I’ve seen how missed appointments affect patients as well as the teams trying to provide care. That experience led me to build NoShowsVoorspellen.nl, a practical way to use appointment data to guide follow-up.

At Erasmus MC and Arkin, I worked with teams to make the impact visible, test targeted follow-up and measure the results. I’m sharing this case study so you can see what the approach involved and judge whether it may be relevant to your organisation.

Download the case study

Have a similar challenge? Talk it through with me. We can first see whether a closer look makes sense.