Modelling the hospital length of stay for patients undergoing laparoscopic cholecystectomy through a multiple regression model

A Scala, TA Trunfio, A Borrelli, G Ferrucci… - Proceedings of the 5th …, 2021 - dl.acm.org
A Scala, TA Trunfio, A Borrelli, G Ferrucci, M Triassi, G Improta
Proceedings of the 5th International Conference on Medical and Health …, 2021dl.acm.org
The need for containing and rationalizing the health expenditure has influenced the
management of various healthcare processes. As far as cholecystectomy interventions, the
laparoscopic surgery is considered as the gold standard, since its effectiveness is
accompanied by a shorter post-operative hospital length of stay (LOS). The LOS represents,
indeed, a performanceindicator and a measure of efficiency for most healthcare processes,
with particular regard to surgical interventions, and its prediction and control is of great …
The need for containing and rationalizing the health expenditure has influenced the management of various healthcare processes. As far as cholecystectomy interventions, the laparoscopic surgery is considered as the gold standard, since its effectiveness is accompanied by a shorter post-operative hospital length of stay (LOS). The LOS represents, indeed, a performanceindicator and a measure of efficiency for most healthcare processes, with particular regard to surgical interventions, and its prediction and control is of great importance for the management of healthcare organizations. Within this framework, here we propose a multiple linear regression model to predict the LOS for patients undergoing laparoscopic cholecystectomy at the University Hospital “San Giovanni di Dio and Ruggi d'Aragona” of Salerno (Italy). Data were retrospectively collected from the hospital information system and divided intwo groups, before and after the implementation of a corrective actions for the appropriate management of patients undergoing laparoscopic cholecystectomy. A multiple regression model is built for each group and results are compared. Multiple socio-demographic and clinical factors, such as age, gender, postoperative complications and pre-operative hospitalization are considered and included in each model. Obtained results show a good predictive power of the two models (R2= 0.84 and R2 = 0.97, whose comparison is then used to assess the effectiveness of the implemented actions.
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