不确定环境下临床路径变异监控、处理机制与稀缺资源配置优化方法研究

项目名称： 不确定环境下临床路径变异监控、处理机制与稀缺资源配置优化方法研究

项目编号： No.71472065

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 管理科学

项目作者： 杜刚

作者单位： 华东师范大学

项目金额： 58万元

中文摘要： 临床路径作为一种以病人为中心新型的医疗管理模式是当今社会的研究热点之一。变异的管理是临床路径管理和控制的重点和难点。合理、有效的变异管理和稀缺医疗资源优化配置对于我国提高治疗水平、抢救生命和提高病人满意度有着重要的意义。 针对未预期临床路径变异管理以及由此引起的稀缺医疗资源配置优化和控制难题，在探讨临床路径医疗服务需求的复杂性和多样性、诊疗时间的高度不确定性、过程的复杂性等特征的基础上，研究基于混合机理和数据挖掘的面向未预期临床路径变异的智能监控、处理机制与稀缺医疗资源动态优化决策等若干新问题，以达到提前预测、控制或者逆转变异，并根据变异的特点和类型，科学、合理的分配相关医疗资源，以支持临床路径有效的执行。课题的研究对于减少患者的平均等待时间，降低平均住院日、提高医疗效率和医疗管理水平、切实解决看病难、看病贵问题具有重要的应用价值。

中文关键词： 数据挖掘；未预期变异；稀缺资源；配置优化；不确定环境

英文摘要： Clinical pathway (CP) as a patient-centered new medical management model is one of the hot social topics nowadays. Variance management is the key issue in CP management and control. Reasonable and effective variance management and the optimization allocation of scarce medical resources are of high significance for improving the medical treatment, saving lives and improving patient satisfaction in our country. Aimed at unexpected CP variance management and challenges of relevant allocation optimization and control of scarce medical resources, considering complexity and diversity of medical service demand, the highly uncertainty of the diagnosis and treatment time, and the complexity of medical process, this project, which is based on hybrid mechanism and data mining, aims to research intelligent monitoring,handling mechanism of the unexpected CP variance of and some new problems such as dynamic optimization decision of scarce medical resources, in order to predict, control or reverse variances, allocate scarce medical resources scientifically and reasonably to support the effective implementation of the CP, according to the variances of the characteristics and types.The research has important application value on reducing patients' average waiting time and the mean hospitalization, improving the medical efficiency and management level, solving the difficulty of expensive medical treatment.

英文关键词： Data Mining;Unexpected Variances;Scarce Resources;Allocation Optimization;Uncertainty Environment