IEEE J Biomed Health Inform. 2018 May;22(3):955-965. doi: 10.1109/JBHI.2017.2701779. Epub 2017 May 5.
Variability and unpredictability are typical features of emergency departments (EDs) where patients randomly arrive with diverse conditions. Patient length of stay (LOS) represents the consumption level of hospital resources, and it is positively skewed and heterogeneous. Both accurate modeling of patient ED LOS and analysis of potential blocking causes are especially useful for patient scheduling and resource management. To tackle the uncertainty of ED LOS, this paper introduces two methods: statistical modeling and distribution fitting. The models are applied to 894 respiratory diseases patients data in the year 2014 from ED of a Chinese public tertiary hospital. Covariates recorded include patient region, gender, age, arrival time, arrival mode, triage category, and treatment area. A Coxian phase-type (PH) distribution model with covariates is proposed as an alternative method for modeling ED LOS. The expectation-maximization (EM) algorithm is used to implement parameter estimation. The results show that ED LOS data can be modeled well by the proposed models. Distributions of ED LOS differ significantly with respect to patients' gender, arrival mode, and treatment area. Using the fitted Coxian PH model will assist ED managers in identifying patients who are most likely to have an extreme ED LOS and in predicting the forthcoming workload for resources.
变异性和不可预测性是急诊科（ED）的典型特征，患者会随机出现各种不同的病情。患者的住院时间（LOS）代表了医院资源的消耗水平，且其呈正偏态和异质性。准确地对患者 ED LOS 进行建模和分析潜在的阻塞原因，对于患者的安排和资源管理特别有用。为了应对 ED LOS 的不确定性，本文提出了两种方法：统计建模和分布拟合。该模型应用于 2014 年中国一家公立医院急诊科 894 例呼吸系统疾病患者的数据。记录的协变量包括患者的地区、性别、年龄、到达时间、到达方式、分诊类别和治疗区域。提出了一种具有协变量的 Coxian 相型（PH）分布模型作为建模 ED LOS 的替代方法。使用期望最大化（EM）算法进行参数估计。结果表明，提出的模型可以很好地对 ED LOS 数据进行建模。ED LOS 的分布与患者的性别、到达方式和治疗区域有显著差异。使用拟合的 Coxian PH 模型可以帮助 ED 管理人员识别最有可能出现极端 ED LOS 的患者，并预测未来资源的工作量。
