Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy.
Department of Public Health, University of Naples "Federico II", Naples, Italy.
BMC Med Inform Decis Mak. 2022 May 24;22(1):141. doi: 10.1186/s12911-022-01884-9.
The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value.
In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model.
The obtained model had an R value of 0.570 and, among the independent variables, the significant variables that most influence the total LOS were Age, Pre-operative LOS, Presence of Complication and Complicated diagnosis.
This work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources.
服务复杂性的快速增长和严格的质量要求对所有医疗保健机构构成了挑战，尤其是从经济角度来看。目标是实施不同的策略，以加强和获得更接近标准的卫生流程。住院时间 (LOS) 是医院内服务管理的一个非常有用的参数，也是评估成本管理的一个指标。实际上，患者的 LOS 可能会受到许多因素的影响，包括其特定状况、病史或医疗需求。为了减少和更好地管理 LOS，有必要能够预测这个值。
在这项研究中，为接受腹腔镜阑尾切除术的患者的总 LOS 建立了预测模型，腹腔镜阑尾切除术是最常见的急诊手术之一。萨勒诺“圣乔瓦尼·迪·迪奥和鲁吉·德拉戈纳”大学医院（意大利）收治的 357 名患者的人口统计学和临床数据被用作多元线性回归模型的自变量。
所得到的模型的 R 值为 0.570，在自变量中，最能影响总 LOS 的显著变量是年龄、术前 LOS、并发症和复杂诊断。
这项工作设计了一种有效且自动化的策略来提高 LOS 的预测能力，这对于增强术前路径可能是有用的。这样就可以对需求进行特征描述，并能够预先估计床位和其他相关医院资源的占用情况。
