Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong.
Health Care Manag Sci. 2020 Jun;23(2):215-238. doi: 10.1007/s10729-019-09469-1. Epub 2019 Feb 4.
In the domain of Home Health Care (HHC), precise decisions regarding patient's selection, staffing level, and scheduling of health care staff have a significant impact on the efficiency and effectiveness of the HHC system. However, decentralized planning, the absence of well defined decision rules, delayed decisions and lack of interactive tools typically lead towards low satisfaction level among all the stakeholders of the HHC system. In order to address these issues, we propose an integrated three phase decision support methodology for the HHC system. More specifically, the proposed methodology exploits the structure of the HHC problem and logistic regression based approaches to identify the decision rules for patient acceptance, staff hiring, and staff utilization. In the first phase, a mathematical model is constructed for the HHC scheduling and routing problem using Mixed-Integer Linear Programming (MILP). The mathematical model is solved with the MILP solver CPLEX and a Variable Neighbourhood Search (VNS) based method is used to find the heuristic solution for the HHC problem. The model considers the planning concerns related to compatibility, time restrictions, distance, and cost. In the second phase, Bender's method and Receiver Operating Characteristic (ROC) curves are implemented to identify the thresholds based on the CPLEX and VNS solution. While the third phase creates a fresh solution for the HHC problem with a new data set and validates the thresholds predicted in the second phase. The effectiveness of these thresholds is evaluated by utilizing performance measures of the widely-used confusion matrix. The evaluation of the thresholds shows that the ROC curves based thresholds of the first two parameters achieved 67% to 71% accuracy against the two considered solution methods. While the Bender's method based thresholds for the same parameters attained more than 70% accuracy in cases where probability value is small (p ≤ 0.5). The promising results indicate that the proposed methodology is applicable to define the decision rules for the HHC problem and beneficial to all the concerned stakeholders in making relevant decisions.
在家庭医疗保健（HHC）领域，针对患者选择、人员配备水平和医疗保健人员排班的精确决策对 HHC 系统的效率和效果有重大影响。然而，分散式规划、缺乏明确的决策规则、延迟决策以及缺乏交互式工具通常会导致 HHC 系统的所有利益相关者的满意度降低。为了解决这些问题，我们为 HHC 系统提出了一种集成的三阶段决策支持方法。具体来说，所提出的方法利用 HHC 问题的结构和基于逻辑回归的方法来确定患者接受、人员招聘和人员利用的决策规则。在第一阶段，使用混合整数线性规划（MILP）为 HHC 调度和路线问题构建数学模型。使用 MILP 求解器 CPLEX 求解数学模型，并使用基于变量邻域搜索（VNS）的方法为 HHC 问题找到启发式解决方案。该模型考虑了与兼容性、时间限制、距离和成本相关的规划问题。在第二阶段，实施 Bender 方法和接收器工作特征（ROC）曲线来基于 CPLEX 和 VNS 解决方案确定阈值。而第三阶段则使用新数据集为 HHC 问题创建新的解决方案，并验证第二阶段预测的阈值。通过使用广泛使用的混淆矩阵的性能指标来评估这些阈值的有效性。对阈值的评估表明，前两个参数的基于 ROC 曲线的阈值在针对两种考虑的解决方案方法的情况下达到了 67%到 71%的准确性。而对于相同参数，基于 Bender 方法的阈值在概率值较小（p≤0.5）的情况下达到了 70%以上的准确性。有希望的结果表明，所提出的方法适用于为 HHC 问题定义决策规则，并有利于所有相关利益相关者做出相关决策。
