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

Journal: Annals of family medicine
Published Date: Jul 28, 2025

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.

Authors

  • Wen-Jan Tuan
    Department of Family and Community Medicine, The Pennsylvania State University, Hershey, PA, USA.
  • Yifang Yan
    The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA.
  • Bilal Abou Al Ardat
    Department of Family and Community Medicine, Penn State College of Medicine, Hershey, Pennsylvania.
  • Todd Felix
    Department of Family and Community Medicine, Penn State College of Medicine, Hershey, Pennsylvania.
  • QiuShi Chen
    Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China.

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