Incidence of Wrong-Site Surgery List Errors for a 2-Year Period in a Single National Health Service Board

J Patient Saf. 2020 Mar;16(1):79-83. doi: 10.1097/PTS.0000000000000426.

Abstract

Introduction: Wrong-site/side surgical "never events" continue to cause considerable harm to patients, healthcare professionals, and organizations within the United Kingdom. Incidence has remained static despite the mandatory introduction of surgical checklists. Operating theater list errors have been identified as a regular contributor to these never events. The aims of the study were to identify and to learn from the incidence of wrong-site/side list errors in a single National Health Service board.

Methods: The study was conducted in a single National Health Service board serving a population of approximately 300,000. All theater teams systematically recorded errors identified at the morning theater brief or checklist pause as part of a board-wide quality improvement project. Data were reviewed for a 2-year period from May 2013 to April 2015, and all episodes of wrong-site/side list errors were identified for analysis.

Results: No episodes of wrong-site/side surgery were recorded for the study period. A total of 86 wrong-site/side list errors were identified in 29,480 cases (0.29%). There was considerable variation in incidence between surgical specialties with ophthalmology recording the largest proportion of errors per number of surgical cases performed (1 in 87 cases) and gynecology recording the smallest proportion (1 in 2671 cases). The commonest errors to occur were "wrong-side" list errors (62/86, 72.1%).

Discussion: This is the first study to identify incidence of wrong-site/site list errors in the United Kingdom. Reducing list errors should form part of a wider risk reduction strategy to reduce wrong-site/side never events. Human factors barrier management analysis may help identify the most effective checks and controls to reduce list errors incidence, whereas resilience engineering approaches should help develop understanding of how to best capture and neutralize errors.

MeSH terms

  • Female
  • Humans
  • Incidence
  • Male
  • Medical Errors / statistics & numerical data*
  • State Medicine
  • Time Factors