Many hospitals in the world are under pressure to improve their efficiency and effectiveness so that they can achieve better health outcomes with limited resources. One common measure of performance is the rate of unplanned hospital readmissions (UHRs) within 30-days. Emergency readmissions for the same disease can be assumed to indicate inappropriate discharge or poor planning, are costly, increase patients’ mortality risks and put additional pressure on bed capacity. Data Mining (DM) techniques have been used to predict UHRs based on clinical and demographic features, but these ignore the process perspective. Predictive Process Monitoring (PPM) is a process mining technique using completed traces to make predictions for in progress cases with machine learning (ML) algorithms. The Outcome-Oriented PPM (OOPPM) is a sub-technique of PPM focusing on predicting categorical outcomes of process. Adaptation of OOPPM in healthcare settings has been limited to date. Here, we illustrate how to implement OOPPM in a healthcare context through an application of an OOPPM pipeline to hospital admissions using the open access MIMIC-IV dataset. Clinical, demographical and process features were used to build an extended event log, which was then employed for UHRs prediction. Results show prediction using OOPPM techniques outperformed traditional DM techniques. OOPPM tests using tree-based ML algorithms achieved better results compared to OOPPM tests using other ML algorithms. Our results suggest OOPPM can make a significant contribution to better understanding of hospital performance.