Robust reusable reliable models for health care optimization

加拿大人在医疗保健上的支出超过1420亿美元(CIHI，2005)。尽管2005年医疗保健支出占GDP的10.4%，但对服务的需求正在增加。确保及时获得各级急性护理的护理是一个紧迫的关切，最近一直是许多辩论的主题，特别是在获得可选程序方面。医院和卫生保健流程通常被认为是一个排队网络，拥有多种类型的客户和复杂的优先方案。相应地，模拟是建模和分析病人流的理想工具。对病人流的特定实例的模拟应用在文献中得到了很好的研究和记录(参见Jun，Jacobson and Swisher，1999或Preater，2002)。然而，关于病人流的运筹学文献中更有趣的一个方面是文献中出现的一次性研究的数量。尽管模拟方法有明显的好处，但在时间、精力和费用方面的开销限制了模拟方法在卫生保健领域的渗透。我们建议开发一个工具来评估本地、地区、我们假设，尽管护理提供的组织形式不同，但可以开发一个通用框架来描述患者流动实例。一旦确定，该框架可以用于开发可组合的模拟元素的功能规范，可以用于对患者流动过程进行建模。最终，我们相信可以创建一个基于模拟的决策环境，非专家可以使用该环境来做出知情的患者流动过程决策。

Canadians spend more than $142 billion on health care (CIHI, 2005).  Despite spending a record 10.4% of GDP in 2005 on health care, demand for services is increasing.  Ensuring timely access to care at all levels of acute care is a pressing concern and has been the subject of much debate recently, particularly with respect to access to elective procedures.Hospitals and health care processes are usually thought of as a network of queues with mulitple types of customers and complicated priority schemes.  Simulation, accordingly, is an ideal tool for modelling and analyzing patient flows.The application of simulation to particular instances of patient flow is well studied and documented in the literature (see Jun, Jacobson and Swisher, 1999 or Preater, 2002 for excellent reviews).  However, one of the more interesting aspects of the operational research literature on patient flow is the number of "one-off" studies that appear in the literature.  The overhead in terms of time, effort, and expense have limited the penetration of simulation methodologies into the health care arena, despite their obvious benefits.We propose the development of a tool to evaluate wait time issues at the local, regional, and provincial level.  We hypothesize that, despite variations in the organization of care delivery, a common framework can be developed to describe patient flow instances.  Once identified, the framework can be used to develop functional specifications for composable simulation elements that can be used to model patient flow processes.  Ultimately, we believe that it is possible to create a simulation-based environment for decision making that can be used by non-experts to make informed patient flow process decisions.