Using machine learning tools to investigate factors associated with trends in ‘no-shows’ in outpatient appointments

Eduard Incze, Penny Holborn, Gary Higgs, Andrew Ware

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    Missed appointments are estimated to cost the UK National Health Service (NHS) approximately £1 billion annually. Research that leads to a fuller understanding of the types of factors influencing spatial and temporal patterns of these so-called “Did-Not-Attends” (DNAs) is therefore timely. This research articulates the results of a study that uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialities. A predictive model was used to determine the risk-increasing and risk-mitigating factors associated with missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each speciality. Results show that the best predictors of DNAs include the patient's age, appointment history, and the deprivation rank of their area of residence. Findings have been analysed at both a geographical and medical speciality level, and the factors associated with DNAs have been shown to differ in terms of both importance and association. This research has demonstrated how machine learning techniques have real value in informing future intervention policies related to DNAs that can help reduce the burden on the NHS and improve patient care and well-being.

    Original languageEnglish
    Article number102496
    Number of pages11
    JournalHealth and Place
    Issue number102496
    Early online date13 Dec 2020
    Publication statusPublished - 1 Jan 2021


    • Compositional versus contextual
    • Machine learning
    • Medical specialities
    • Missed appointments (‘Did-not-attend'DNA)
    • Outpatients


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