Prediction of the Unplanned and Avoidable Readmissions in Acute Care in South Africa

Jennifer Chipps of the University of the Western Cape and Damian Clark of the University of KwaZulu-Natal in South Africa will apply machine learning to a hospital digital registry of trauma and surgical patients to develop an algorithm for predicting unplanned and avoidable readmissions to improve patient outcomes and reduce the burden on the healthcare system. Unplanned hospital readmission within 30 days is an important indicator of the quality of patient care. They will use de-identified patient data from a South African public hospital to train an AI model to predict readmissions avoidable under the current standard of care. The resulting algorithm will be validated using real-time patient data, integrated into the hospital workflow as a Readmission Prediction Classifier Tool, and tested in different clinical settings. The tool will enable treatment plans tailored to individual patients and their risks, improving health outcomes and reducing financial burdens for patients and healthcare providers.