Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.

ABSTRACT:

Purpose

Factors influencing missed appointments are complex and difficult to anticipate and intervene against. To optimize appointment adherence, we aimed to use personalized machine learning and big data analytics to predict the risk of and contributing factors for no-shows and late cancellations in primary care practices.

Methods

We conducted a retrospective longitudinal study leveraging geolinked clinical, care utilization, socioeconomic, and climate data from 15 family medicine clinics at a regional academic health center in Pennsylvania from January 2019 to June 2023. We developed multiclass machine learning models using gradient boost, random forest, neural network, and logistic regression to predict appointment outcomes, followed by feature importance analysis to iden

SUBMITTER: Tuan WJ 

PROVIDER: S-EPMC12306982 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature