Predictive analytics for hospital discharge flow determination

Abstract

In recent years, hospitals around the world are faced with large patient flows, which negatively affect the quality of patient care and become a crucial factor to consider in inpatient management. The main objective of this management is to maximize the number of available beds, using efficient planning. Intensive Care Units (ICU) are hospital units with a higher monetary consumption, and the importance of indicators that allow the achievement of useful information for a correct management is critical. This study allowed the prediction of the Length of Stay (LOS) based on their demographic data, information collected at the time of admission and clinical conditions, which can help health professionals in conducting a more assertive planning and a better quality service. The results obtained show that Machine Learning (ML) models, using demographic information simultaneously with the patient's pathway, as well as clinical data, drugs, tests and analysis, introduce a greater predictive ability for LOS.