Abstract
Objective. 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. Methods. 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. Results. 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. Conclusions. 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. Key words: critical care; Monte Carlo simulation; operations research; transport
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