Unplanned 30-day cancer readmissions are an important outcome of cancer hospitalization and can significantly raise mortality rates and costs for both the patient and the hospital. This paper aimed to develop a predictive model using machine learning and electronic health records to predict unplanned 30-day cancer readmissions and further develop it as a clinical decision support system. The three-stage study design followed the 2022 AMIA Artificial Intelligence Evaluation Showcase. In the first stage, the technical performance of the model was determined (81% of AUROC) and contributing factors were identified. In the second stage, the technical feasibility and workflow considerations of using such a predictive model were explored through semi-structured interviews. In the third stage, a decision tree analysis and a cost estimation showed that the model can reduce unplanned readmissions significantly if timely action is taken and that preventing a single readmission may significantly reduce costs.
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