Outpatient Length of Stay (OLOS) Analysis at Edelweis Hospital using Machine Learning Algorithm

Patient satisfaction may be impacted by the length of stay (LOS) that a patient perceives during an outpatient clinic visit. With the increasing competition in the healthcare industry and patients’ demands for higher-quality care, hospitals are focusing more on enhancing their quality from a clinical and management perspective. The Indonesia Ministry of Health has established minimum standards (SPM) for healthcare services that all Indonesian hospitals are required to meet, particularly the hospital waiting time indicator, which must be no longer than 60 minutes. Furthermore, there is a term in healthcare called outpatient length of stay (OLOS) that is not yet specified in SPM. OLOS is defined as the amount of time a patient spends in a hospital from the moment he or she arrives at the administration until he or she leaves. Edelweis Hospital is one of a private hospital located in Bandung that has established a 2-hour maximum LOS standard for its outpatient services. Providing accurate information about LOS may increase patient satisfaction by reducing uncertainty. However, effective methods to predict the length of stay for outpatients (OLOS) in Pediatric Clinics are seldom known. This study’s goal is to design a prediction model for OLOS based on patient characteristics and several other clinical attributes. By identifying the attributes that affected OLOS, the model will help hospital make relevant decisions. We used machine learning algorithms such as random forest, decision tree, k-nearest neighbor (kNN), adaboost, and gradient boosting to design prediction models for OLOS. From the validation set, random forest has the highest accuracy rate with a value of 99.3%, followed by decision tree and gradient boosting were 99.2% each. Furthermore, machine learning models were used to determine the importance of attributes. These models could eventually be used alongside with real-time IT system data to provide accurate real-time estimates of OLOS at the Pediatric Clinic.