Novel machine learning model for predicting multiple unplanned hospitalisations.

Background: In the Australian public healthcare system, hospitals are funded based on the number of inpatient discharges and types of conditions treated (casemix). Demand for services is increasing faster than public funding and there is a need to identify and support patients that have high service usage. In 2016, the Victorian Department of Health and Human Services developed an algorithm to predict multiple unplanned admissions as part of a programme, Health Links Chronic Care (HLCC), that provided capitation funding instead of activity based funding to support patients with high admissions.