Centre for Operations Excellence, Sauder School of Business, University of British Columbia, Vancouver, British Columbia, Canada.
Prehosp Emerg Care. 2013 Oct-Dec;17(4):466-74. doi: 10.3109/10903127.2013.811561.
Operations research is the application of mathematical modeling, statistical analysis, and mathematical optimization to understand and improve processes in organizations. The objective of this study was to illustrate how the methods of operations research can be used to identify opportunities to reduce the absolute value and variability of interfacility transport intervals for critically ill patients.
After linking data from two patient transport organizations in British Columbia, Canada, for all critical care transports during the calendar year 2006, the steps for transfer of critically ill patients were tabulated into a series of time intervals. Statistical modeling, root-cause analysis, Monte Carlo simulation, and sensitivity analysis were used to test the effect of changes in component intervals on overall duration and variation of transport times. Based on quality improvement principles, we focused on reducing the 75th percentile and standard deviation of these intervals.
We analyzed a total of 3808 ground and air transports. Constraining time spent by transport personnel at sending and receiving hospitals was projected to reduce the total time taken by 33 minutes with as much as a 20% reduction in standard deviation of these transport intervals in 75% of ground transfers. Enforcing a policy of requiring acceptance of patients who have life- or limb-threatening conditions or organ failure was projected to reduce the standard deviation of air transport time by 63 minutes and the standard deviation of ground transport time by 68 minutes.
Based on findings from our analyses, we developed recommendations for technology renovation, personnel training, system improvement, and policy enforcement. Use of the tools of operations research identifies opportunities for improvement in a complex system of critical care transport.
运筹学是运用数学建模、统计分析和数学优化来理解和改进组织中的流程。本研究的目的是说明如何运用运筹学方法来确定减少危重症患者院内转运间隔绝对值和变异性的机会。
在将加拿大不列颠哥伦比亚省两家患者转运机构的 2006 年全年所有重症监护转运数据进行链接后，将危重症患者的转运步骤列成一系列时间间隔。运用统计建模、根本原因分析、蒙特卡罗模拟和敏感性分析来测试改变各组成部分间隔对总转运时间和变异性的影响。基于质量改进原则，我们专注于减少这些间隔的第 75 百分位数和标准差。
共分析了 3808 次地面和空中转运。预计限制转运人员在送诊和接诊医院的停留时间可将总转运时间缩短 33 分钟，在 75%的地面转运中，这些转运间隔的标准差最多可降低 20%。执行一项要求接受生命或肢体有威胁或器官衰竭的患者的政策，预计可将空中转运时间的标准差降低 63 分钟，地面转运时间的标准差降低 68 分钟。
根据我们分析的结果，我们制定了技术革新、人员培训、系统改进和政策执行方面的建议。运用运筹学工具可识别出重症患者转运这一复杂系统中存在的改进机会。
