Modeling the length-of-stay of patients with geriatric diseases or alcohol use disorder using phase-type distributions with covariates
Modeling the length-of-stay of patients with geriatric diseases or alcohol use disorder using phase-type distributions with covariates
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DOI：
10.1080/24725579.2020.1866715
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发表时间：
2021-01
影响因子：
--
通讯作者：
Wanlu Gu;Neng Fan;H. Liao
中科院分区：
文献类型：
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作者：
Wanlu Gu;Neng Fan;H. Liao
住院日作为衡量医疗服务有效性的重要指标，代表着医疗需求水平，与医疗费用密切相关。在过去的几十年里，随着人类预期寿命的快速增长，迫切需要改善老年患者的卫生系统。同样，酒精使用障碍(AUD)作为一种与严重问题饮酒有关的慢性复发性脑部疾病，已经对社会造成了负面影响，并将患者的健康和安全置于危险之中。在这两种情况下，由于对长期医院治疗的要求不断增加，以及医疗成本不断上升，需要更有效的医院管理。为了提高医疗效率，有必要对LOS数据进行准确的建模，并进一步分析潜在的影响因素。本文利用Coxian位相型(PH)分布和极大似然估计(MLE)方法对某医院收集的老年患者和AUD患者的流量信息进行了拟合。通过期望最大化(EM)算法评估和比较了年龄、性别、录取类型、录取来源和经济状况等协变量对学习损失的影响。结果表明，该方法能够很好地对两类患者的视距数据进行建模，并能较准确地识别协变量之间的差异。利用拟合柯克逊PH值分布和估计的协变量系数，将为更好地进行医疗服务和资源配置决策提供指导。
Abstract The hospital length-of-stay (LOS), as an important measure of the effectiveness of healthcare, represents the level of medical requirement and is highly related to the treatment costs. As the human life expectancy has being increased rapidly in the past few decades, there is a pressing need to improve health systems for geriatric patients. Similarly, the alcohol use disorder (AUD), as a chronic relapsing brain disease related to severe problem drinking, has caused negative impacts to society and put patients’ health and safety at risk. In both cases, more efficient hospital management is in demand due to increasing requirements for long-term hospital treatment and the continuously rising medical cost. In order to improve the healthcare efficiency, an accurate modeling of the LOS data and the further analysis of potential influencing factors are necessary. In this paper, we utilize the Coxian Phase-Type (PH) distribution and apply Maximum Likelihood Estimation (MLE) to fit the patient flow information of both geriatric patients and AUD patients collected in a hospital. The influences of the covariates of age, gender, admission type, admit source, and financial class on LOS are assessed and compared through Expectation-Maximization (EM) algorithms. The results show that the LOS data of both types of patients can be modeled well, and the differences with respect to covariates can be accurately identified by the proposed methods. Using the fitted Coxian PH distribution and the estimated coefficients of covariates will provide a guide for better decision-making in healthcare service and resource allocation.