Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach.

Journal: PLoS medicine
Published Date:

Abstract

BACKGROUND: Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of unkept outpatient clinic appointments across hospital trusts in the England. In addition, we aimed to examine the predictors of unkept outpatient clinic appointments across specialties at Imperial College Healthcare NHS Trust (ICHT). Our final aim was to train machine learning models to determine the effectiveness of a potential intervention in reducing unkept appointments.

Authors

  • Sion Philpott-Morgan
    NHS Digital, London, United Kingdom.
  • Dixa B Thakrar
    Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
  • Joshua Symons
    Big Data and Analytical Unit, Imperial College of Science Technology and Medicine, London, UK.
  • Daniel Ray
    Farr Institute of Health Informatics Research, University College London, London, United Kingdom.
  • Hutan Ashrafian
    Institute of Global Health Innovation, Imperial College London, London, United Kingdom.
  • Ara Darzi
    Imperial College London London UK.