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Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study
Journal article   Open access   Peer reviewed

Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study

Yi Yang, Samaneh Madanian and David Parry
JMIR medical informatics, Vol.12, e48273
2024
DOI: https://doi.org/10.2196/48273
PMID: 38214974
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Published937.29 kBDownloadView
Published (Version of Record)CC BY V4.0,  Open Access

Abstract

data analytics health care operation predictive modeling decision support system prediction health equity patients no-show Did Not Attend Did Not Show machine learning appointment nonadherence
Background: The phenomenon of patients missing booked appointments without canceling them—known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)—has a detrimental effect on patients’ health and results in massive health care resource wastage. Objective: Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand. Methods: We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms’ prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics. Results: Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients’ DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction. Conclusions: This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#10 Reduced Inequalities

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.14 Nursing
1.14.1849 Appointment Scheduling
Web Of Science research areas
Medical Informatics
ESI research areas
Clinical Medicine
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