Congratulations to PhD student Eduard Incze who has had his article 'Using machine learning tools to investigate factors associated with trends in ‘no-shows’ in outpatient appointments' published in the journal 'Health and Place'.
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.
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
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.