Canadian Journal of Anesthesia

, Volume 50, Issue 8, pp 767–774 | Cite as

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

General Anesthesia

Abstract

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.

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

Résumé

Objectif

La qualité du processus anesthésique se vérifie par le taux d’événements peropératoires indésirables. Nous avons voulu illustrer comment analyser la qualité de l’anesthésie en utilisant les méthodes de contrôle statistique du processus et comment cette analyse peut améliorer la qualité.

Méthode

Nous avons prospectivement recueilli des données anesthésiques sur cinq ans. Elles comprenaient des événements indésirables, classés selon quatre niveaux de sévérité. Nous avons sélectionné quatre événements indésirables qui représentent des aspects importants de la qualité et de la sécurité pour l’analyse du contrôle statistique du processus. Il s’agissait : de l’anesthésie régionale inadéquate, du retour à la conscience difficile après une anesthésie générale, des difficultés d’intubation et des erreurs de médicaments. Nous avons analysé le processus d’origine à l’aide des “graphiques-p” pour le contrôle statistique du processus.

Résultats

Pour 65 170 anesthésies réalisées, nous avons noté des événements indésirables dans 18,3 % des cas, la plupart bénins. Nous avons utilisé les graphiques de contrôle pour définir statistiquement la variation prévisible normale du taux de problème et, ensuite, l’utiliser comme base de l’analyse des problèmes sélectionnés. Les résultats sont les suivants :
  • - Anesthésie régionale inadéquate : processus stable, mais taux d’échec élevé inacceptable.

  • - Réveil difficile : processus instable à cause des efforts d’amélioration de la qualité.

  • - Difficultés d’intubation : processus stable, taux acceptable.

  • - Erreurs de médicaments : méthodologie inappropriée à cause du faible taux d’erreurs.

Conclusion

En appliquant le contrôle statistique du processus à l’analyse d’événements indésirables, nous avons montré comment il permet de déterminer si un processus est stable, si une intervention est nécessaire et si les efforts d’amélioration de la qualité ont produit les effets recherchés.

References

  1. 1.
    Cohen MM, Duncan PG, Pope WD, et al. The Canadian four-centre study of anaesthetic outcomes: II. Can outcomes be used to assess the quality of anaesthesia care? Can J Anaesth 1992; 39: 430–9.PubMedGoogle Scholar
  2. 2.
    Cooper JB, Cullen DJ, Nemeskal R, et al. Effects of information feedback and pulse oximetry on the incidence of anesthesia complications. Anesthesiology 1987; 67: 686–94.PubMedCrossRefGoogle Scholar
  3. 3.
    Levett JM, Carey RG. Measuring for improvement: from Toyota to thoracic surgery. Ann Thorac Surg 1999; 68: 353–8.PubMedCrossRefGoogle Scholar
  4. 4.
    Fasting S, Gisvold SE. Data recording of problems during anaesthesia: presentation of a well-functioning and simple system. Acta Anaesthesiol Scand 1996; 40: 1173–83.PubMedGoogle Scholar
  5. 5.
    Fasting S, Gisvold SE. Adverse drug errors in anesthesia, and the impact of coloured syringe labels. Can J Anesth 2000; 47: 1060–7.PubMedGoogle Scholar
  6. 6.
    Benneyan JC. Statistical quality control methods in infection control and hospital epidemiology, part I: introduction and basic theory. Infect Control Hosp Epidemiol 1998; 19: 194–214.PubMedGoogle Scholar
  7. 7.
    Benneyan JC. Statistical quality control methods in infection control and hospital epidemiology, part II: chart use, statistical properties, and research issues. Infect Control Hosp Epidemiol 1998; 19: 265–83.PubMedGoogle Scholar
  8. 8.
    Lee K, McGreevey C. Using control charts to assess performance measurement data. Jt Comm J Qual Improv 2002; 28: 90–101.PubMedGoogle Scholar
  9. 9.
    Finison LJ, Finison KS, Bliersbach CM. The use of control charts to improve healthcare quality. J Healthc Qual 1993; 15: 9–23.PubMedGoogle Scholar
  10. 10.
    Rosner B. Normal approximation to the binomial distribution In: Rosner B (Ed.). Fundamentals of Biostatistics. Pacific Grove: Duxbury; 2000: 138–44.Google Scholar
  11. 11.
    Cooper JB. How to measure what happens. Can J Anaesth 1991; 38: 1032–3.PubMedGoogle Scholar
  12. 12.
    Lee A, Lum ME. Measuring anaesthetic outcomes. Anaesth Intensive Care 1996; 24: 685–93.PubMedGoogle Scholar
  13. 13.
    Barach P, Small SD. Reporting and preventing medical mishaps: lessons from non-medical near miss reporting systems. BMJ 2000; 320: 759–63.PubMedCrossRefGoogle Scholar
  14. 14.
    Spencer FC. Human error in hospitals and industrial accidents: current concepts. J Am Coll Surg 2000; 191: 410–8.PubMedCrossRefGoogle Scholar
  15. 15.
    Orkin FK, Cohen MM, Duncan PG. The quest for meaningful outcomes (Editorial). Anesthesiology 1993; 78: 417–22.PubMedCrossRefGoogle Scholar
  16. 16.
    Bothner U, Georgieff M, Schwilk B. The impact of minor perioperative anesthesia-related incidents, events, and complications on postanesthesia care unit utilization. Anesth Analg 1999; 89: 506–13.PubMedCrossRefGoogle Scholar
  17. 17.
    Fasting S, Gisvold SE. Serious intraoperative problems — a five-year review of 83,844 anesthetics. Can J Anesth 2002; 49: 545–53.PubMedGoogle Scholar
  18. 18.
    Boelle PY, Garnerin P, Sicard JF, Clergue F, Bonnet F. Voluntary reporting system in anaesthesia: is there a link between undesirable and critical events? Qual Health Care 2000; 9: 203–9.PubMedCrossRefGoogle Scholar
  19. 19.
    Schwilk B, Muche R, Treiber H, Brinkmann A, Georgieff M, Bothner U. A cross-validated multifactorial index of perioperative risks in adults undergoing anaesthesia for non-cardiac surgery. Analysis of perioperative events in 26907 anaesthetic procedures. J Clin Monit Comput 1998; 14: 283–94.PubMedCrossRefGoogle Scholar
  20. 20.
    Bothner U, Georgieff M, Schwilk B. Building a largescale perioperative anaesthesia outcome-tracking database: methodology, implementation, and experiences from one provider within the German quality project. Br J Anaesth 2000; 85: 271–80.PubMedCrossRefGoogle Scholar
  21. 21.
    Schwilk B, Muche R, Bothner U, Goertz A, Friesdorf W, Georgieff M. Quality control in anesthesiology. Results of a prospective study following the recommendations of the German Society of Anesthesiology and Intensive Care (German). Anaesthesist 1995; 44: 242–9.PubMedCrossRefGoogle Scholar
  22. 22.
    Sanborn KV, Castro J, Kuroda M, Thys DM. Detection of intraoperative incidents by electronic scanning of computerized anesthesia records. Comparison with voluntary reporting. Anesthesiology 1996; 85: 977–87.PubMedCrossRefGoogle Scholar
  23. 23.
    Cullen DJ, Bates DW, Leape LL, Adverse Drug Event Prevention Study Group. Prevention of adverse drug events: a decade of progress in patient safety. J Clin Anesth 2000; 12: 600–14.PubMedCrossRefGoogle Scholar
  24. 24.
    Gaitini LA, Vaida SJ, Ben-David B, et al. Using ratebased events to improve clinical practice. J Healthc Qual 2000; 22: 4–9.PubMedGoogle Scholar
  25. 25.
    Berwick DM. Controlling variation in health care. A consultation from Walter Shewhart. Med Care 1991; 29: 1212–25.PubMedCrossRefGoogle Scholar
  26. 26.
    Schumock GT, Seeger JD, Kong SX. Control charts to monitor rates of adverse drug reactions. Hosp Pharm 1995; 30: 1088; 1091–2; 1095–6.PubMedGoogle Scholar
  27. 27.
    Clark DE, Cushing BM, Bredenberg CE. Monitoring hospital trauma mortality using statistical process control methods. J Am Coll Surg 1998; 186: 630–5.PubMedCrossRefGoogle Scholar
  28. 28.
    Boggs PB, Hayati F, Washburne WF, Wheeler DA. Using statistical process control charts for the continual improvement of asthma care. Jt Comm J Qual Improv 1999; 25: 163–81.PubMedGoogle Scholar
  29. 29.
    Lagasse RS, Steinberg ES, Katz RI, Saubermann AJ. Defining quality of perioperative care by statistical process control of adverse outcomes. Anesthesiology 1995; 82: 1181–8.PubMedCrossRefGoogle Scholar
  30. 30.
    Bothner U, Georgieff M, Schwilk B. Validation of routine incidence reporting of one anaesthesia provider institution within a nation-wide quality of process assessment program. J Clin Monit Comput 1998; 14: 305–11.PubMedCrossRefGoogle Scholar
  31. 31.
    Vitez TS, Macario A. Setting performance standards for an anesthesia department. J Clin Anesth 1998; 10: 166–75.PubMedCrossRefGoogle Scholar
  32. 32.
    Mohammed MA, Cheng KK, Rouse A, Marshall T. Bristol, Shipman, and clinical governance: Shewhart’s forgotten lessons. Lancet 2001; 357: 463–7.PubMedCrossRefGoogle Scholar
  33. 33.
    Laffel G, Blumenthal D. The case for using industrial quality management science in health care organizations. JAMA 1989; 262: 2869–73.PubMedCrossRefGoogle Scholar
  34. 34.
    Benneyan JC. Use and interpretation of statistical quality control charts. Int J Qual Health Care 1998; 10: 69–73.PubMedCrossRefGoogle Scholar
  35. 35.
    Humble C. Caveats regarding the use of control charts. Infect Control Hosp Epidemiol 1998; 19: 865–8.PubMedGoogle Scholar
  36. 36.
    Neal JM, Hebl JR, Gerancher JC, Hogan QH. Brachial plexus anesthesia: essentials of our current understanding. Reg Anesth Pain Med 2002; 27: 402–28.PubMedCrossRefGoogle Scholar
  37. 37.
    Rose DK, Cohen MM. The airway: problems and predictions in 18,500 patients. Can J Anaesth 1994; 41: 372–83.PubMedCrossRefGoogle Scholar

Copyright information

© Canadian Anesthesiologists 2003

Authors and Affiliations

  1. 1.Department of Anesthesia and Intensive CareSt. Olav’s Hospital, University Hospital of TrondheimTrondheimNorway

Personalised recommendations