Robust reusable reliable models for health care optimization

加拿大人在保健方面的支出超过1 420亿美元（2005年加拿大卫生保健研究所）。尽管2005年在医疗保健方面的支出达到了GDP的10.4%，但对服务的需求仍在增加。确保及时获得各级急性护理是一个紧迫的问题，最近一直是许多辩论的主题，特别是在获得选择性手术方面。医院和医疗保健流程通常被认为是一个排队的网络，有多种类型的客户和复杂的优先级方案。因此，模拟是建模和分析病人流动的理想工具。将模拟应用于病人流动的特定实例得到了很好的研究和文献记录（参见Jun， Jacobson和Swisher， 1999或Preater， 2002的优秀评论）。然而，运筹学文献中关于病人流动的一个更有趣的方面是文献中出现的“一次性”研究的数量。时间、精力和费用方面的开销限制了模拟方法在医疗保健领域的渗透，尽管它们有明显的好处。我们建议开发一种工具来评估地方、区域和省级的等待时间问题。我们假设，尽管在护理交付的组织变化，一个共同的框架可以开发来描述病人流实例。一旦确定，该框架可用于为可组合的仿真元素开发功能规范，这些元素可用于对患者流流程进行建模。最终，我们相信有可能创建一个基于模拟的决策环境，非专家可以使用它来做出明智的患者流程决策。

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.