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

ABSTRACT:

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 an

SUBMITTER: Yang Y 

PROVIDER: S-EPMC10818230 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature