# No-show appointment prediction

> No-show appointment prediction - Build a model that identifies patients most likely to miss
> appointments, with correlating reasons.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.585836+00:00` (UTC).

## Primary page

- [No-show appointment prediction](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/no-show.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Time series and specific use cases](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/healthcare_appointment_no_show_prediction/no_show.ipynb)

Many people are guilty of having canceled a doctor’s appointment. However, although canceling an appointment does not seem too disastrous from the patient’s point of view, no-shows cost outpatient health centers a staggering 14% of anticipated daily revenue (JAOA). Missed appointments trickle into lower utilization rates for not only doctors and nurses but also the overhead costs required to run outpatient centers. In addition, patients missing their appointments risk facing poorer health outcomes as they are unable to access timely care.

While outpatient centers employ solutions such as calling patients ahead of time, these high touch resources investments are often not prioritized for patients with the highest risk of no-shows. Low touch solutions such as automated texts are effective tools for mass reminders but do not offer necessary personalization for patients at the highest risk of no-shows. This accelerator shows how to identify clients who are likely to miss appointments ("no-shows") and take action to prevent that from happening.