Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction

Bekelaar, J. W. R., Luime, J. J., & de Carvalho, R. M. (2023). Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 378-390). (Lecture Notes in Business Information Processing; Vol. 468 LNBIP). Springer.


In recent years, hospitals and other care providers in the Netherlands are coping with a widespread nursing shortage and a directly related increase in nursing workload. This nursing shortage combined with the high nursing workload is associated with higher levels of burnout and reduced job satisfaction among nurses. However, not only the nurses, but also the patients are affected as an increasing nursing workload adversely affects patient safety and satisfaction. Therefore, the aim of this research is to predict the care acuity corresponding to an individual patient for the next admission day, by using the available structured hospital data of the previous admission days. For this purpose, we make use of an LSTM model that is able to predict the care acuity of the next day, based on the hospital data of all previous days of an admission. In this paper, we elaborate on the architecture of the LSTM model and we show that the prediction accuracy of the LSTM model increases with the increase of the available amount of historical event data. We also show that the model is able to identify care acuity differences in terms of the amount of support needed by the patient. Moreover, we discuss how the predictions can be used to identify which patient care related characteristics and different types of nursing activities potentially contribute to the care acuity of a patient.

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