Effect of Clinical and Demographic Variables on the Hospital Stay of Patients Undergoing Total Knee Arthroplasty.

The knee is the joint most affected by osteoarthritis and in its severe form can significantly affect people's physical and functional abilities. The increased demand for surgery leads to greater attention by health care management to be able to keep costs down. A major expense item for this procedure is Length of Stay (LOS). In this study, several Machine Learning algorithms were tested in order to construct not only a valid predictor of LOS but also to know among the selected variables the main risk factors. To do so, activity data from the Evangelical Hospital "Betania" in Naples, Italy, from 2019-2020 were used. Among the algorithms, the best are the classification algorithms with accuracy values exceeding 90%. Finally, the results are in line with those shown by two other comparison hospitals in the area.

The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery.

The revolutions of recent years in health care have involved several areas ranging from patient treatment to resource management. Therefore, several strategies have been put in place to increase patient value while trying to reduce spending. Several indicators have arisen to evaluate the performance of healthcare processes. The main one is Length of Stay (LOS). In this study, classification algorithms were used to predict the LOS of patients undergoing lower extremity surgery, an increasingly common condition given the progressive aging of the population. The context is the Evangelical Hospital "Betania" in Naples (Italy) in 2019-2020, which augments a multicenter study conducted by the same research team on several hospitals in southern Italy. All selected algorithms show an Accuracy above 90% but among them, the best is Logistic Regression with a value reaching 94%.