Objectives Patient non-attendance at outpatient appointments (DNA) is a major concern for healthcare providers. Non-attendances increase waiting lists, reduce access to care and may be detrimental not for the patient who did not attend. However, non-targeted interventions to reduce the DNA rate may not be effective and thus we aim to produce a model which can accurately predict which appointments will be attended. Methods In this work, a random forest classification algorithm was trained to predict whether an appointment will be missed using 7 million past outpatient appointments. The model was applied to patients of all ages and appointments with all specialties at a major London teaching hospital including a validation set covering the COVID-19 pandemic. Results The model achieves an AUROC score of 0.76 and accuracy of 73% on test data. We find that the waiting period between booking an the appointment, the patient's past DNA behaviour, and the levels of deprivation in their local area are important factors in predicting future DNAs. Discussion Our model is strongly predictive of whether a hospital outpatient appointment will be attended. Its performance on both patients who did not appear in the training data and appointments from a different time period which covers the Covid-19 pandemic indicate it generalized well across both face to face and virtual appointments and could be used to target resources and intervention towards those patients who are likely to miss an appointment. Moreover, it highlights the impact of deprivation on patient access to healthcare Conclusion Our model successfully predicts patient attendance at outpatient appointments.
Developing a multivariate predictive model of hospital outpatient attendance for patients of all ages