Predicting Hospital Outpatient Attendance Using Random Forest Classifiers

Background: Patient non-attendance of outpatient appointments is a major concern for healthcare providers. These non-attendances are detrimental to the patient's health and a major cost to the provider. However, non-targeted interventions may cost more in administrative resources than the missed appointments themselves. Methods: In this work, a random forest classification algorithm was trained to predict whether an appointment will be missed for patients of all ages and appointments with all specialties at a major London hospital. Findings: The model achieves an AUROC score of 0.76 and accuracy of 73\% on test data which contained only appointments from patients who did not appear in the training data. Further, the model achieves an AUROC score of 0.75 in a second test set which contains only patients from the year 2020. Interpretation: 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 and could be used to target resources towards those patients who are likely to miss an appointment. Funding: This study was partially funded by STFC DiRAC innovation fellowships which funded the work of Jonathan Holdship and Harpreet Dhanoa. Declaration of Interests: The authors declare that there is no conflict of interest regarding the publication of this article. Ethics Approval Statement: The Trust Quality Improvement & Audit Committee at Guy’s & St Thomas NHS Foundation Trust approved audit/service evaluation number 116298 registered on the trust database.