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

Eduard Incze, Penny Holborn, Gary Higgs, Andrew Ware

    Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

    2 Wedi eu Llwytho i Lawr (Pure)

    Crynodeb

    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.

    Iaith wreiddiolSaesneg
    Rhif yr erthygl102496
    Nifer y tudalennau11
    CyfnodolynHealth and Place
    Cyfrol67
    Rhif cyhoeddi102496
    Dyddiad ar-lein cynnar13 Rhag 2020
    Dynodwyr Gwrthrych Digidol (DOIs)
    StatwsCyhoeddwyd - 1 Ion 2021

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