AI Predicts Missed Pediatric Appointments, But Social Barriers Point to Real Fixes

Missed medical appointments are one of the most deceptively simple problems in modern healthcare. A child does not arrive, a clinician sits in an empty room, and a slot that another family could have used quietly evaporates. Multiplied across thousands of visits, these no-shows drain productivity, inflate costs, and, more importantly for pediatric medicine, interrupt the continuity of care that children with chronic conditions and developmental needs depend on. Prior research has linked missed primary care appointments to higher rates of hospitalization, chronic health problems, and even early mortality, making the humble no-show a surprisingly consequential event in a child’s health trajectory.

A new study published in Pediatric Research by a team at The University of Texas Health Science Center at Houston set out to answer two intertwined questions: can artificial intelligence predict which pediatric appointments will be missed, and does adding information about families’ social circumstances, known as non-medical drivers of health, make those predictions better? The answer to the first question is a qualified yes. The answer to the second is more nuanced, and arguably more interesting: the social data did not sharpen the algorithm’s accuracy, but it revealed exactly which barriers clinics could act on.

The research team analyzed 7,931 scheduled pediatric appointments across six general pediatrics clinics in the greater Houston metropolitan area between April and August 2024. The clinics included one large academic center within the Texas Medical Center and five community-based sites serving a racially and economically diverse population. The sample was striking: 2,761 appointments, or 34.8 percent, ended in no-shows, a rate far higher than many adult settings and a signal of how much unmet need hides inside pediatric scheduling data. Most appointments in the cohort involved Hispanic (43.2 percent) or non-Hispanic Black (39.2 percent) patients, and nearly 80 percent were covered by Medicaid.

At the heart of the study was a transformer-based machine learning model, an architecture familiar from modern language processing but adapted here for clinical sequences. The model processes each patient’s visit history with positional information, using encoder layers built around self-attention, feed-forward processing, and normalization steps to learn how past attendance patterns relate to future behavior. Its inputs included prior appointment attendance, phone reminders, confirmation status, appointment details, and even weather. A probability calibration layer then converts the learned visit history representation into an estimated likelihood that a given future appointment will be missed.

The team evaluated the model in four configurations, with and without demographic and insurance variables and with and without non-medical drivers of health data drawn from a 13-item screening questionnaire covering food insecurity, transportation needs, housing instability, financial resource strain, health literacy, and health-harming legal needs. The results were sobering for anyone hoping social data would supercharge prediction. Using baseline variables alone, the model achieved an area under the receiver operating characteristic curve of 0.708, and adding the social variables left that figure unchanged. Adding demographics and insurance nudged performance to an AUROC of 0.721, with the social variables again contributing essentially nothing on top. The area under the precision-recall curve hovered around 0.54 in all configurations, only modestly above the 34.8 percent no-show prevalence.

But the regression analyses told a different and more actionable story. Using generalized estimating equations to account for repeated appointments within the same patients, the researchers found that unconfirmed appointments had more than twice the odds of becoming no-shows compared with confirmed ones, an adjusted odds ratio of 2.24. Appointments tied to inactive or pending patient portal accounts carried 31 percent higher odds of no-show. Transportation needs raised the odds by 54 percent, and housing instability by 26 percent, even after adjusting for demographics, insurance, clinic site, and visit type. Each additional day between scheduling and the visit slightly increased the odds, translating to roughly 10 to 16 percent higher odds for appointments booked 30 days in advance.

Not every association pointed to something a clinic can fix. Non-Hispanic Black patients had 68 percent higher odds of no-show than non-Hispanic White patients, and Medicaid coverage carried 76 percent higher odds than private insurance. Most striking was the category of unknown insurance status, which showed the largest effect size of all, a more than fivefold increase in odds. The authors caution that this label reflects incomplete or unverified insurance information rather than a distinct insurance type, and may be entangled with the very pattern of repeated missed visits it predicts, since insurance is often verified at the visit itself. Still, the magnitude of the association flags these families as being at especially elevated risk.

The findings carry an important ethical warning about how such algorithms should be deployed. If a model flags a family as high-risk and the clinic responds by overbooking that slot or offering a less convenient time, the algorithm could inadvertently punish the very households already struggling with transportation gaps or unstable housing, deepening inequities in access to care. The authors argue that elevated predicted risk is better used as a trigger for supportive outreach and barrier reduction, such as transportation assistance, help with patient portal registration, and care coordination referrals, rather than as an input to scheduling decisions that could ration access. This aligns with broader recommendations for child-centered and ethically implemented medical artificial intelligence.

There are caveats worth keeping in view. The study window spanned only about five months of a single spring-summer period, so seasonal patterns in pediatric attendance, driven by school schedules, holidays, and illness waves, remain untested. Missing data were substantial for some social variables, with transportation needs unknown in 28.2 percent of appointments, which may have blunted both the model contribution and some measured associations. And the study took place within a single health system that already operates an established screening and referral workflow, so results may not generalize to settings without that infrastructure.

Even so, the study’s central message is refreshingly practical. Prediction alone will not fill empty exam rooms. The strongest predictors of a missed pediatric visit in this cohort, an unconfirmed appointment, an unactivated patient portal, a family that needs a ride or a stable home, are all signals that a clinic can detect and respond to before the visit happens. Artificial intelligence can help find those signals at scale, but the real intervention is human: a phone call that gets a confirmation, a portal sign-up session, a ride arranged, a referral made. In pediatric care, where every missed well-child visit can mean a delayed vaccination or a missed developmental screen, pairing algorithms with outreach may be the difference between predicting no-shows and preventing them.

Subject of Research: Machine learning prediction of pediatric appointment no-shows incorporating non-medical drivers of health

Article Title: Non-medical drivers of health and pediatric no-show prediction: identifying modifiable risk factors

Article References: Gibson, A., Nguyen, L., Yu, L., Kulesza, C., Le, Y.-C., Cavenaugh, S., McKay, S., & Jiang, X. (2026). Non-medical drivers of health and pediatric no-show prediction: identifying modifiable risk factors. Pediatric Research. https://doi.org/10.1038/s41390-026-05531-1

Image Credits: AI Generated

DOI: 10.1038/s41390-026-05531-1

Keywords: pediatrics, no-show prediction, machine learning, transformer model, non-medical drivers of health, social determinants of health, electronic health records, transportation barriers, housing instability, health equity, patient portal, appointment confirmation