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Machine learning predicts appointment no-shows, late cancels

Machine learning can help predict appointment no-shows and late cancellations, helping clinics tailor their patient engagement and outreach strategies.

Sara Heath
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Published: 28 Jul 2025

New research from Pennsylvania State University shows that AI and machine learning can accurately predict no-shows and late cancellations in primary care, a trend study authors said could help practices better tailor their patient engagement strategies.

Overall, machine learning models scored a 0.85 on a 0-1 scale for predicting appointment no-shows and a 0.92 for predicting late cancellations, the study authors wrote in Annals of Family Medicine.

These findings come as healthcare experts and practice managers strive for better operational efficiency. Late cancellations and appointment no-shows can be costly because they represent missed opportunities for healthcare organizations to capture revenue, not to mention their impacts on health outcomes and continuity of care.

"Failure to attend primary care visits could disrupt essential care for patients and also interrupts clinical workflows and strains health care resources," the researchers explained. "Delayed or missed appointments for essential care can lead to serious health consequences and increased disparities among populations already experiencing health inequities."

Healthcare organizations have deployed a number of technologies, such as appointment reminders and self-scheduling options, to help reduce the number of late cancellations and no-shows. But while those tools have helped drive better patient engagement, the researchers acknowledged that they have not entirely closed the missed-appointment gap.

According to the researchers, AI, and particularly machine learning, could be effective.

Examining machine learning's impact on no-shows, late cancels

Using geolinked clinical, care utilization, socioeconomic and climate data from more than a million appointments at 15 family medicine clinics in Pennsylvania, the researchers were able to test different learning models to predict late cancellations and appointment no-shows.

The tested models included gradient boost, random forest, neural network, and logistic regression to predict appointment outcomes. They also conducted feature importance analysis to identify factors that could lead to no-shows or late cancellations at population and patient levels.

According to the researchers, the gradient boost model performed best in predicting no-show and late cancellations, with about 85% prediction quality for no-shows and 92% prediction quality for late cancellations. Importantly, the model did not predict results biased against sex or race/ethnicity.

In terms of what contributed to late cancellations or missed appointments, the researchers noted that lead time was critical. When there was too much lead time before an appointment -- meaning the patient had to wait a long time between booking the appointment and actually attending it -- they were more likely to cancel at the last minute or not show up at all.

This insight could help clinics retool their booking strategies.

"Given the strong effect of lead time, clinics could prioritize shorter wait times for high-risk patients," the researchers recommended.

However, shortening the amount of time leading up to an appointment is only one key strategy. Even when lead times were reduced down to less than 30 days, certain sociodemographic characteristics influenced appointment no-shows or late cancellations.

That is where tailored patient engagement technology can come in. The researchers said machine learning might be able to offer deeper insights into how to anticipate specific patient needs, letting clinics deploy personalized outreach or assistance.

For example, factors like female sex, younger age, limited English proficiency, number of chronic conditions and distance to the clinic contributed to no-shows and late cancellations. Providing certain systems or assistance for patients fitting these profiles could help reduce poor appointment outcomes.

And to streamline this more personalized approach, clinics could deploy machine learning. The AI might help identify patients who are high risk for cancelling at the last minute or missing their appointment altogether.

"Integrating personalized predictive models with system-wide initiatives, such as automated reminders, patient navigations and late cancellation fees, offers opportunities to help care teams create more targeted, effective and personalized interventions for better appointment adherence," the researchers concluded.

Sara Heath has reported news related to patient engagement and health equity since 2015.

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