Background: Patient no-shows significantly disrupt pediatric healthcare delivery, highlighting the necessity for precise predictive models, especially during the dynamic shifts caused by the SARS-CoV-2 pandemic. In outpatient settings, these no-shows result in medical resource underutilization, increased healthcare costs, reduced access to care, and lower clinic efficiency and productivity. Methods: The objective is to develop a predictive model for patient no-shows using data-driven techniques. We analyzed five years of historical data retrieved from both a scheduling system and electronic health records from a general pediatrics clinic within the WVU Health systems. This dataset comprises a total of 209,408 visits from 2015 to 2018, 82,925 visits in 2019, and 58,820 visits in 2020, spanning both pre-pandemic and pandemic periods. The data includes variables such as patient demographics, appointment details, timing, hospital characteristics, appointment types, and environmental factors. Results: Our XGBoost model demonstrated robust predictive capabilities, notably outperforming traditional "no-show rate" metrics. Precision and recall metrics for all features were 0.82 and 0.88, respectively. Receiver Operator Characteristic (ROC) analysis yielded AUCs of 0.90 for all features and 0.88 for the top 5 predictors when evaluated on the 2019 cohort. Furthermore, model generalization across racial/ethnic groups was also observed. Evaluation on 2020 telehealth data reaffirmed model efficacy (AUC: 0.90), with consistent top predictive features. Conclusions: Our study presents a sophisticated predictive model for pediatric no-show rates, offering insights into nuanced factors influencing attendance behavior. The model's adaptability to evolving healthcare landscapes, including telehealth, underscores its potential for enhancing clinical practice and resource allocation.

The authors have declared no competing interest.

This study did not receive any funding.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Institutional Review Board (IRB) of West Virginia University gave ethical approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Our machine learning algorithm is covered by a provisional patent filed between Aspirations LLC and West Virginia University. This is distinctly unique from other filed patents, including US0150242819A1 [2015], which utilizes advanced statistical techniques without indicating the accuracy or performance of the models; US20110208674A1 [2010], which involves a similar concept within a ticket booking system; and WO2018058189A1 [2016], which describes a supervised learning module targeting overbooking strategies, rather than uniquely identifying patients at risk of no-showing their appointment. Due to confidentiality agreements and the proprietary nature of the technology, the underlying data supporting this work is not publicly available.