Predictive analytics for hospital inpatient flow determination

Abstract

Currently, the efficient planning of resources in hospitals present a responsibility of extreme importance in the management of the various clinical units. In Intensive Care Unit (ICU), a hospital service where patients require constant observation and control, considering the high costs incurred with hospitalized patients, the optimization of these factors assumes an extremely important role. Given its unpredictability, this study focused on a characterization of this unit, identifying existing patterns, during a 5-year period, 2017 to 2021, at the Centro Hospitalar do Tâmega e Sousa (CHTS), providing a set of useful information crucial for decision making. Additionally, a prediction of future ICU admissions is performed using time series and Machine Learning (ML) models. However, the models did not reveal a predictive ability with an adequate level of reliability.