Using simulation to improve root cause analysis of adverse surgical outcomes.

OBJECTIVE: The purpose of this study was to develop and test a simulation method of conducting investigation of the causality of adverse surgical outcomes.
DESIGN: Six hundred and thirty-one closed claims of a major medical malpractice insurance company were reviewed. Each case had undergone conventional root cause analysis (RCA). Claims were categorized by comparing the predominant underlying cause documented in the case files. Three cases were selected for simulation.
SETTING: All records (medical and legal) were analyzed. Simulation scenarios were developed by abstracting data from the records and then developing paper and electronic medical records, choosing appropriate STUDY PARTICIPANTS: including test subjects and confederates, scripting the simulation and choosing the appropriate simulated environment. INTERVENTION: In a simulation center, each case simulation was run 6-7 times and recorded, with participants debriefed at the conclusion. MAIN OUTCOME MEASURES: Sources of error identified during simulation were compared with those noted in the closed claims. Test subject decision-making was assessed qualitatively.
RESULTS: Simulation of adverse outcomes (SAOs) identified more system errors and revealed the way complex decisions were made by test subjects. Compared with conventional RCA, SAO identified root causes less focused on errors by individuals and more on systems-based error.
CONCLUSIONS: The use of simulation for investigation of adverse surgical outcomes is feasible and identifies causes that may be more amenable to effective systems changes than conventional RCA. The information that SAO provides may facilitate the implementation of corrective measures, decreasing the risk of recurrence and improving patient safety.