Dubbeldam, A. L., Ketykó, I., de Carvalho, R. M., & Mannhardt, F. (2023). Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 366-377). (Lecture Notes in Business Information Processing; Vol. 468 LNBIP). Springer. https://doi.org/10.1007/978-3-031-27815-0_27
Abstract
Patients, when in a hospital, will go through a personalized treatment scheduled for many different reasons and with various outcomes. Furthermore, some patients and/or treatments require aftercare. Identifying the need for aftercare is crucial for improving the process of the patient and hospital. A late identification results in a patient staying longer than needed, occupying a bed that otherwise could serve another patient. In this paper, we will investigate to what extent events from the first hours of stay can help in predicting the need for aftercare. For that, we explored a dataset from a Dutch hospital. We compared different methods, considering different prediction moments (depending of the amount of initial hours of stay), and we evaluate the gain in earlier predicting the need for aftercare.