How Arkin prevents 2,400 no-shows a year

Missed appointments can interrupt continuity of care and leave valuable time unused. This case study explains how Arkin used appointment data to focus follow-up and measure the difference it made.

More than 16,000 missed appointments a year across mental health services
A prediction model that identified around 80% of no-shows in the analysis
Targeted interventions for patients with an elevated no-show risk
Around 2,400 no-shows prevented each year
€240,000 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.

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