DEA方法在卫生经济学中的应用

自八十年代中期以来 ,对医院和医疗卫生系统进行效率评估引起了人们的广泛关注 ,越来越多的学者开始从事这一领域的研究 .数据包络分析方法 ( DEA)因其可对多输入 ,多输出的系统进行综合效率评价 ,这一特性正符合医院和医疗卫生系统中具有多投入和多产出的特点 ,从而在医院和医疗卫生系统的效率评估中受到重视并有着重要应用 .本文综述了现有文献中有关 DEA方法在医院和医疗卫生系统效率评估的应用 ,对已得到应用的 DEA模型作了简单的介绍 ,同时对 DEA模型和方法的进一步应用给出了建议 .     

A Model of Medical Record Storage

A model is developed to describe the growth of a medical records store subject to rules for microfilming or disposing of record files when they have not been used within a given period of time. Separate projections are given for the number of files in the system and for the volume of file contents. The model allows different microfilm policies to be compared and aids the long term planning of storage facilities.     

商业医疗保险损失分析:基于广义线性模型的实证研究

本文使用广义线性模型对商业医疗保险损失进行建模,并用某商业保险公司的医疗保险赔付数据进行了实证检验,结果表明,在影响医疗保险损失的诸多因素中,住院天数、医院级别、地区、保障档次等都是显著的因素,而性别和小于60岁以下年龄段内年龄则并不是显著因素,这些结论给医疗保险的经营和风险控制带来实际的意义.     

基于物联网的医用耗材智能屋管理系统

针对医用高值耗材种类繁多、管理难度大的问题,为减少人力成本、提高医用高值耗材的智能化管理水平。本文提出了基于物联网的高值医用耗材智能屋的管理系统,介绍了智能屋的整体架构、系统功能、核心业务及流程设计,主要分为结构模块、RFID模块、数据库模块、控制系统模块四大模块,具体有用户管理、出入库管理、综合查询、预警管理四大业务,通过GM(1,1)模型对2022年全年高值医用耗材的月消耗量进行预测分析,并且检验得该预测模型的精度等级为一级,模型预测效果很好,能够为医院采购人员采购提供了一个参考,避免因过度的主观估计而造成的耗材浪费。该系统能够有效地提高医院对于高值医用耗材的管理水平,减少人力、物力和财力的消耗,为广大患者提供更加优质的医疗服务。本文所提的高值医用耗材智能屋管理系统解决了传统的高值医用耗材管理的诸多难题。     

政府监管下的医院医疗信息分享演化博弈分析

在分析政府监管下医院间医疗信息分享特征的基础上,结合我国当前正在推行的医联体医院间信息分享运作模式,引入梅特卡夫定律并考虑患者评价的影响,分析了监管部门与医院各自的利益组成,建立了监管部门与医院两者之间的演化博弈模型,并采用复制动态方程研究了不同情形下医院间信息分享的演化博弈轨迹。研究表明,政府通过监管并建立激励惩罚机制等引导措施对医院最终达到的演化稳定态具有极大影响;降低医院主体信息分享的风险成本、规范提高医院分享信息的质量、引导患者增强对医院信息分享的关注、制定有吸引力和威慑力的奖惩政策,是促进医院医疗信息分享的关键。研究结果可为政府监管部门预测医院间的信息分享趋势和制定精准化政策以促进分享提供参考。     

应用随机前沿成本模型对我国大型综合医院成本效率的研究

为了精确考察我国大型医院医疗服务和管理的成本效率,采用随机前沿成本模型对我国73所大型综合医院的医疗服务的成本效率进行了评价研究,同时在3种不同低效率误差分布的假设下,对各评价单元所得的效率值进行了比较.结果显示,我国大型综合医院医疗服务的成本效率平均得分在0.9以上,表明它们的医疗服务和管理水平是比较高的,与其发展规模是相匹配的.并且在3种不同的低效率误差分布假设下各评价单元所得效率值也基本一致.     

精神疾病患者经济负担分析及预测

对某精神疾病的专科医院患者数量及费用进行分析,采用径向基函数(RBF)神经网络模型对精神疾病患者的看病费用进行拟合及预测,并比较该预测模型与BP神经网络的预测效果.将贵州省某精神类疾病的专科医院2015年1月-2016年12月医院HIS系统中的病人处方数据作为训练集,建立BP模型、RBF神经网络模型.分别对2017年1月1日-2017年1月16日病人用以精神类疾病看病费用情况进行预测.RBF神经网络模型均能够较好地拟合和预测精神类疾病患者看病费用,可以为医院管理者了解本院精神病患者看病费用的变化趋势提供依据,为制定精神病患者疾病负担的相关政策提供数据支撑.     

医疗资源影响下具有潜伏期的一类传染病模型建立及性态分析

建立了医疗资源影响下的考虑疾病具有潜伏期的一类传染病模型,并分析了模型的动力学性态.发现疾病流行与否由基本再生数和医院病床数共同决定,并得到了病床数的阈值条件.当基本再生数R_0大于1时,系统只存在惟一正平衡点,且通过构造Dulac函数证明了正平衡点只要存在一定是全局渐近稳定的;当R_01,我们得到系统存在两个正平衡点及无正平衡点的条件,且只有当医院的病床数小于阈值时,系统会经历后向分支.因此,可根据实际情况使医院病床的投入量不低于阈值条件,不仅有利于疾病的控制而且不会出现医疗资源过剩的现象.     

Use of VNS heuristics for scheduling of patients in hospital

One of the important problems in hospital management is how to schedule the treatments of resident patients in hospital for a given day due to the restrictions imposed by their medical condition as well as restrictions on medical machines and qualified medical personnel availability. Patients are to be subjected to different kinds of treatments, each requiring a medical machine of a certain type as well as a physician being qualified to operate it. This is a highly complex problem not yet adequately addressed in the literature. At present in most hospitals the problem is being solved manually by specialized personnel. However, the resulting schedules are very often inaccurate and inefficient with patients waiting for a long time to be treated and medical personnel often working overtime. In this paper we formulate the model for this problem and develop a simple and efficient method based on Variable Neighbourhood Search for solving it. The heuristics has been tested on real-life as well as on generated instances. Numerical results show that the heuristics proposed outperform commercial software for optimization as well as manual solutions both in quality of solution and in computational time.     

Combining simulation and goal programming for healthcare planning in a medical assessment unit

This paper describes a detailed simulation model for healthcare planning in a medical assessment unit (MAU) of a general hospital belonging to the national health service (NHS), UK. The MAU is established to improve the quality of care given to acute medical patients on admission, and to provide the organisational means of rapid assessment and investigation in order to avoid unnecessary admissions. The simulation model enables different scenarios to be tested to eliminate bottlenecks in order to achieve optimal clinical workflow. The link between goal programming (GP) and simulation for efficient resource planning is explored. A GP model is developed for trade-off analysis of the results obtained from the simulation. The implications of MAU management preferences to various objectives are presented.     

Decision support for hospital evacuation and emergency response

Evacuation planning is an important part of a hospital’s emergency management plan. In an evacuation the safety and health of patients is the fundamental success parameter. Thus, in this paper we introduce an evacuation model, appropriate for planning and operations, that has the objective of minimizing expected risk, both the threat risk that is forcing the evacuation, and the risk inherent in transporting patients, some in critical condition. Specifically, we study the allocation of patients, categorized by criticality and care requirements, to a limited fleet of vehicles of various capacities and medical capabilities, to be transported to appropriate receiving hospitals considering the current available space in each hospital for each category of patient. The model is an integer program, where the non-linear expected risks are calculated a-priori. This model has a structure that has excellent solution characteristics that permit us to solve large problems in a reasonable time, enabling the model to potentially be used for both planning and operations. To illustrate the solvability of this model and demonstrate its characteristics, we apply it to a realistic case study based on the evacuation of a large regional hospital.     

保险公司与医院合作的博弈分析

本文在委托-代理理论的框架下构建了在医疗保险风险控制过程中保险公司与医院的委托-代理模型,分析了这种合作的可行性;并运用无限期重复博弈模型探讨了合作的稳定性,同时引证国外管理医疗模式下的POS组织的实例 ,揭示了保险公司与医院之间应该建立"利益共享,风险共担"的有效的激励机制.     

Analysing quality with generalized kinetic methods

A mathematical structure is developed with the aim of analysing the time evolution of the quality of a composite system such as a medical service inside an hospital. The approach belongs to the so-called Generalized Kinetic Theory, and consists of a set of balanced statistical equations on the probability distribution functions of the system populations over a state variable that represents the perceived quality. Internal and external actions are taken into account by means of direct interactions and ensemble terms. The mathematical framework is developed for a general setting. As a particular case, a model is suggested with reference to the quality of a specific medical service.     

The implementor/adversary algorithm for the cyclic and robust scheduling problem in health-care

A general problem in health-care consists in allocating some scarce medical resource, such as operating rooms or medical staff, to medical specialties in order to keep the queue of patients as short as possible. A major difficulty stems from the fact that such an allocation must be established several months in advance, and the exact number of patients for each specialty is an uncertain parameter. Another problem arises for cyclic schedules, where the allocation is defined over a short period, e.g. a week, and then repeated during the time horizon. However, the demand typically varies from week to week: even if we know in advance the exact demand for each week, the weekly schedule cannot be adapted accordingly. We model both the uncertain and the cyclic allocation problem as adjustable robust scheduling problems. We develop a row and column generation algorithm to solve this problem and show that it corresponds to the implementor/adversary algorithm for robust optimization recently introduced by Bienstock for portfolio selection. We apply our general model to compute master surgery schedules for a real-life instance from a large hospital in Oslo.     

Hospital capacity,capability, and emergency preparedness

Disasters often result in shifts in hospital capacity utilization. Emergency preparedness plans must recognize capacity at the service-line level. This information can provide an additional level of detail to better design response activities and develop cost-effective disaster response plans. We model a possible preparedness plan for Florida hospitals in the case of a major disaster. We model a hurricane event because, in addition to its similarity to other disasters, it provides enough warning for substantive preparation activities. Following Johansen, we measure capacity in a frontier setting using data envelopment analysis. We also use a criterion of economic capability to ensure that a Pareto Optimal situation can be maintained. Information on hospital capacity, patient characteristics of inpatient discharges, and financial performance was merged to perform this study. Our findings suggest there is not enough excess capacity for some specialized services in Florida. However, possible evacuation policies can still be derived from our findings satisfying medical and economic capabilities.     

A multi-objective model to determine efficient resource levels in a medical assessment unit

This paper details models that determine the efficient allocation of resources on a medical assessment unit (MAU) of a general hospital belonging to the National Health Service (NHS) UK. The MAU was established to improve the quality of care given to acute medical patients on admission, and also provide the organizational means of rapid assessment and investigation in order to avoid unnecessary admissions. To analyse the performance of the MAU, doctors, nurses and beds are considered as the three main resources. Then a model is developed using the goal programming approach in multiobjective decision making and solved to deal with MAU performance. The developed model is solved under three different sets of patient admissions with the same resource levels using past data from the MAU. The results of the model are used to analyse the needed resource levels. Conclusions as to the appropriate staffing levels and functions of the MAU are drawn.     

Dimensioning hospital wards using the Erlang loss model

How many beds must be allocated to a specific clinical ward to meet production targets? When budgets get tight, what are the effects of downsizing a nursing unit? These questions are often discussed by medical professionals, hospital consultants, and managers. In these discussions the occupancy rate is of great importance and often used as an input parameter. Most hospitals use the same target occupancy rate for all wards, often 85%. Sometimes an exception is made for critical care and intensive care units. In this paper we demonstrate that this equity assumption is unrealistic and that it might result in an excessive number of refused admissions, particularly for smaller units. Queuing theory is used to quantify this impact. We developed a decision support system, based on the Erlang loss model, which can be used to evaluate the current size of nursing units. We validated this model with hospital data over the years 2004–2006. Finally, we demonstrate the efficiency of merging departments.     

DEA与DRF法的整合

本文针对利用DEA模型进行有效性评价时碰到问题，提出了用因子分析法（简称DRF法）与DEA法相结合来进行评价，这是对DEA法的一种改进。并在此基础上对医院绩效进行了评价，为财政部门科学制定《医疗单位财政支出效益评价实施办法》提供依据。     

Learning from discrete-event simulation: Exploring the high involvement hypothesis

Discussion of learning from discrete-event simulation often takes the form of a hypothesis stating that involving clients in model building provides much of the learning necessary to aid their decisions. Whilst practitioners of simulation may intuitively agree with this hypothesis they are simultaneously motivated to reduce the model building effort through model reuse. As simulation projects are typically limited by time, model reuse offers an alternative learning route for clients as the time saved can be used to conduct more experimentation. We detail a laboratory experiment to test the high involvement hypothesis empirically, identify mechanisms that explain how involvement in model building or model reuse affect learning and explore the factors that inhibit learning from models. Measurement of learning focuses on the management of resource utilisation in a case study of a hospital emergency department and through the choice of scenarios during experimentation. Participants who reused a model benefitted from the increased experimentation time available when learning about resource utilisation. However, participants who were involved in model building simulated a greater variety of scenarios including more validation type scenarios early on. These results suggest that there may be a learning trade-off between model reuse and model building when simulation projects have a fixed budget of time. Further work evaluating client learning in practice should track the origin and choice of variables used in experimentation; studies should also record the methods modellers find most effective in communicating the impact of resource utilisation on queuing.     

DEA model with shared resources and efficiency decomposition

Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input resources used in both stages of operations. For example, in hospital operations, some of the input resources such as equipment, personnel, and information technology are used in the first stage to generate medical record to track treatments, tests, drug dosages, and costs. The same set of resources used by first stage activities are used to generate the second-stage patient services. Patient services also use the services generated by the first stage operations of housekeeping, medical records, and laundry. These DMUs have not only inputs and outputs, but also intermediate measures that exist in-between the two-stage operations. The distinguishing characteristic is that some of the inputs to the first stage are shared by both the first and second stage, but some of the shared inputs cannot be conveniently split up and allocated to the operations of the two stages. Recognizing this distinction is critical for these types of DEA applications because measuring the efficiency of the production for first-stage outputs can be misleading and can understate the efficiency if DEA fails to consider that some of the inputs generate other second-stage outputs. The current paper develops a set of DEA models for measuring the performance of two-stage network processes with non splittable shared inputs. An additive efficiency decomposition for the two-stage network process is presented. The models are developed under the assumption of variable returns to scale (VRS), but can be readily applied under the assumption of constant returns to scale (CRS). An application is provided.