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 missed appointments is therefore timely. Current research is mainly focused on traditional statistical techniques that fail to capture the complex relationships in this type of data. This thesis uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialties. A predictive model based on LightGBM was used to determine the risk-increasing and risk-mitigating factors for missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each specialty. Results show that the best predictors of missed appointments include the patient’s age, appointment history, and the deprivation rank of their area of residence; however, the factors associated with missed appointments have also been shown to differ across medical specialties. This report shows that the use of machine learning techniques has real value in informing future targeted reminder mechanisms that can help reduce the burden on the NHS and improve patient care and well-being.
|Date of Award
|KESSII & Digital Health and Care Wales (DHCW)
|Penny Holborn (Supervisor), Andrew Ware (Supervisor) & Gary Higgs (Supervisor)