Unnecessary Length of Hospital Stay (LOS) has significant consequences on the economy of the national healthcare system. Numerous factors may influence LOS, such as bad management of resources, beds and surgery procedures. In this work we investigate, among the demographic, clinical and organizational variables, those most affecting the LOS, through the use of Multiple Linear Regression model. Data of 262 patients were collected from the hospital information system of the General Medicine Department of the University L.P-o Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno. The regression model has been tested and optimized by selecting the most appropriate predictors and by finding the best trade-off between the number of independent variables and the absence of multicollinearity in the data. Results show that the variables influencing LOS were the gender, the number of procedures and the discharge modality.
Influence of demographic and organizational factors on the length of hospital stay in a general medicine department / Profeta, Martina; Cesarelli, Giuseppe; Giglio, Cristiana; Ferrucci, Giuseppe; Borrelli, Anna; Amato, Francesco. - (2021), pp. 1-4. (Intervento presentato al convegno BECB 2021 tenutosi a Nanchang, China nel August 13–15, 2021) [10.1145/3502060.3503652].
Influence of demographic and organizational factors on the length of hospital stay in a general medicine department
Profeta, Martina;Cesarelli, Giuseppe;Borrelli, Anna;Amato, Francesco
2021
Abstract
Unnecessary Length of Hospital Stay (LOS) has significant consequences on the economy of the national healthcare system. Numerous factors may influence LOS, such as bad management of resources, beds and surgery procedures. In this work we investigate, among the demographic, clinical and organizational variables, those most affecting the LOS, through the use of Multiple Linear Regression model. Data of 262 patients were collected from the hospital information system of the General Medicine Department of the University L.P-o Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno. The regression model has been tested and optimized by selecting the most appropriate predictors and by finding the best trade-off between the number of independent variables and the absence of multicollinearity in the data. Results show that the variables influencing LOS were the gender, the number of procedures and the discharge modality.File | Dimensione | Formato | |
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