Using machine learning tools to investigate factors associated with trends in 'no-shows' in outpatient appointments. (January 2021)

• Record Type: Journal Article
• Authors: Incze, Eduard; Holborn, Penny; Higgs, Gary; Ware, Andrew
• Is Part Of: Health & place. Volume 67(2021)
• Publication Date: 2021-01
• Subjects: Missed appointments ('Did-not-attend'DNA) -- Compositional versus contextual -- Outpatients -- Medical specialities -- Machine learning; Health -- Social aspects -- Periodicals; Health services accessibility -- Periodicals; Public health -- Periodicals; Political planning -- Periodicals; Social medicine -- Periodicals; Epidemiology -- Periodicals; Health Policy -- Periodicals; Health Services Accessibility -- Periodicals; Public Health -- Periodicals; Public Policy -- Periodicals; Sociology, Medical -- Periodicals; Épidémiologie -- Périodiques; Politique sanitaire -- Périodiques; Santé, Services de -- Accessibilité -- Périodiques; Health services accessibility; Health -- Social aspects; Political planning; Public health; Social medicine; Periodicals; 613
• DOI: 10.1016/j.healthplace.2020.102496
• Languages: English
• ISSNs: 1353-8292

Abstract

Abstract: 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. Highlights: Missed appointments are estimated to cost the UK National Health Service (NHS) approximately £1 billion annually. More research is needed to understand factors associated with missed appointments. The types of factors impacting on no-shows vary by speciality. Machine learning techniques can be used to guide the types of interventions needed to help reduce the impact of no-shows.