The use of statistical process control (risk-adjusted CUSUM, risk-adjusted RSPRT and CRAM with prediction limits) for monitoring the outcomes of out-of-hospital cardiac arrest patients rescued by the EMS system.

Objective: Based on previous experience from surgical surveillance, risk-adjusted cumulative sum (CUSUM)-type charts were applied to monitor out-of-hospital cardiac arrest (OHCA) patient mortality.
Materials and Methods: Data from 2356 OHCA patients were collected by the Taipei County Fire Bureau from June 2006 to November 2007. Logistic regression analysis was applied to create a risk-adjusted model. Next, a risk-adjusted CUSUM chart, a risk-adjusted resetting sequential probability ratio test chart and a cumulative risk-adjusted mortality with prediction limits chart were used to detect excess deaths of the OHCA patients rescued by the emergency medical service (EMS) system.
Results: The overall mortality rate, defined as having no return of spontaneous circulation, was 79.3%. These three charts signalled an increase in the death rate at similar sites, and also suggested a small process shift.
Conclusion: A visual approach to EMS systems monitoring that combines the risk-adjusted cumulative sum, Risk-adjusted resetting sequential probability ratio test and cumulative risk-adjusted mortality with prediction limits charts was established. It was found that this approach can be effectively used by the EMS community to monitor OHCA outcomes in real time.
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