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Statistical process control methods allow the analysis and improvement of anesthesia care

[Les méthodes de contrôle statistique du processus permettent d’analyser et d’améliorer les soins anesthésiques]

From the Department of Anesthesia Intensive Care, St. Olav’s Hospital University Hospital of Trondheim, Trondheim, Norway.

Address correspondence to: Dr. Sigurd Fasting, Department of Anesthesia and Intensive Care, St. Olav’s Hospital, University Hospital of Trondheim, N-7006 Trondheim, Norway. Phone: +47-73868108; Fax: +47-73868117; E-mail: sigurd.fasting{at}medisin.ntnu.no

Purpose: Quality aspects of the anesthetic process are reflected in the rate of intraoperative adverse events. The purpose of this report is to illustrate how the quality of the anesthesia process can be analyzed using statistical process control methods, and exemplify how this analysis can be used for quality improvement.

Methods: We prospectively recorded anesthesia-related data from all anesthetics for five years. The data included intraoperative adverse events, which were graded into four levels, according to severity. We selected four adverse events, representing important quality and safety aspects, for statistical process control analysis. These were: inadequate regional anesthesia, difficult emergence from general anesthesia, intubation difficulties and drug errors. We analyzed the underlying process using ‘p-charts’ for statistical process control.

Results: In 65,170 anesthetics we recorded adverse events in 18.3%; mostly of lesser severity. Control charts were used to define statistically the predictable normal variation in problem rate, and then used as a basis for analysis of the selected problems with the following results:

-Inadequate plexus anesthesia: stable process, but unacceptably high failure rate;

-Difficult emergence: unstable process, because of quality improvement efforts;

-Intubation difficulties: stable process, rate acceptable;

-Medication errors: methodology not suited because of low rate of errors.

Conclusion: By applying statistical process control methods to the analysis of adverse events, we have exemplified how this allows us to determine if a process is stable, whether an intervention is required, and if quality improvement efforts have the desired effect.

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