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

Wen-Jan Tuan1, Yifang Yan2, Bilal Abou Al Ardat3

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Machine learning accurately predicts primary care no-shows and late cancellations. Schedule lead time is the key factor, enabling personalized strategies to improve appointment adherence.

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Primary Care Management

Background:

  • Missed appointments in primary care present significant challenges.
  • Predicting and intervening against patient no-shows and late cancellations is complex.
  • Optimizing appointment adherence is crucial for healthcare efficiency and patient outcomes.

Purpose of the Study:

  • To leverage machine learning and big data analytics for predicting missed primary care appointments.
  • To identify key contributing factors for no-shows and late cancellations.
  • To develop personalized strategies for improving patient adherence.

Main Methods:

  • Retrospective longitudinal study (January 2019-June 2023) using clinical, socioeconomic, and climate data.
  • Development of multiclass machine learning models (gradient boost, random forest, neural network, logistic regression).
  • Feature importance analysis and stratified analysis by sex and race/ethnicity for model fairness.

Main Results:

  • Analysis included 109,328 patients and 1,118,236 appointments; 6.9% no-shows, 6.8% late cancellations.
  • Gradient boost model demonstrated high performance (AUC 0.852 for no-shows, 0.921 for late cancellations).
  • Schedule lead time identified as the most significant predictor of missed appointments; no detected bias.

Conclusions:

  • A robust framework for predicting missed appointments was established.
  • Personalized strategies can be developed to enhance patient adherence to primary care.
  • Machine learning offers a practical approach to address the persistent challenge of missed appointments.