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A Computational Intelligence Framework for Length of Stay Prediction in Emergency Healthcare Services Department

Estimating Patient Length of Stay (LOS) in healthcare systems is significant for seemly decision making regarding capacity planning, resource allocation and scheduling. In most Emergency Healthcare Services Departments, the commonly used indicators to measuring performance include length of stay, waiting time, resource utilization and number of patients treated. The existing challenge of Prolonged Hospital Length of Stay (PHLOS), usually experienced in most healthcare system indicates constant surge, resulting in over demand of Healthcare resources (facilities and personnel). In this paper, a Machine Learning (ML) framework is proposed to predict patient Length of Stay (LOS) in emergency health care services departments. First, a study of the emergency health care services in the University of Uyo Teaching Hospital was conducted to gain insights into the operations of the emergency department and the contributions as well as limitations of important system parameters. Second, an analysis of several relevant factors such as Severity of Illness or Emergency Cases (SIC) was carried out to assess its performance. Third, the proposed ML framework was then implemented using the Intuitionistic Type-2 Fuzzy Logic System (IT2-FLS). Results of the model demonstrate the importance of ML in evaluating the performance of healthcare systems for efficient LOS Prediction Provisioning.