This study aims to provide an updated overview of medical error taxonomies by building on a robust review conducted in 2011. It seeks to identify the key characteristics of the most suitable taxonomy for use in high-fidelity simulation-based postgraduate courses in Critical Care. While many taxonomies are available, none seem to be explicitly designed for the unique context of healthcare simulation-based education, in which errors are regarded as essential learning opportunities. Rather than creating a new classification system, this study proposes integrating existing taxonomies to enhance their applicability in simulation training. Through data from surveys of participants and tutors in postgraduate simulation-based courses, this study provides an exploratory analysis of whether a generic or domain-specific taxonomy is more suitable for healthcare education. While a generic classification may cover a broad spectrum of errors, a domain-specific approach could be more relatable and practical for healthcare professionals in a given domain, potentially improving error-reporting rates. Seven strong links were identified in the reviewed classification systems. These correlations allowed the authors to propose various simulation training strategies to address the errors identified in both the classification systems. This approach focuses on error management and fostering a safety culture, aiming to reduce communication-related errors by introducing the principles of Crisis Resource Management, effective communication methods, and overall teamwork improvement. The gathered data contributes to a better understanding and training of the most prevalent medical errors, with significant correlations found between different medical error taxonomies, suggesting that addressing one can positively impact others. The study highlights the importance of simulation-based education in healthcare for error management and analysis.

Faculty of Medicine Department of Simulation Medicine Masaryk University Brno Czech Republic

University Hospital Brno and Faculty of Medicine Department of Pediatric Anesthesiology and Intensive Care Medicine Masaryk University Brno Czech Republic

See more in PubMed

Raab SS, Grzybicki DM, Janosky JE, Zarbo RJ, Geisinger KR, Meier FA, et al.. The Agency for Healthcare Research and Quality hospital labs studying patient safety consortium and error reduction. Pathol Patterns Rev. 2006;126(suppl_1):S12–20: S12–S20. doi: 10.1309/7GWUCGFLYG2VFHJ1 DOI

Brennan TA, Leape LL, Laird NM, Hebert L, Localio AR, Lawthers AG, et al.. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard medical practice Study I. N Engl J Med. 1991;324: 370–376. doi: 10.1056/NEJM199102073240604 PubMed DOI

Wilson RM, Runciman WB, Gibberd RW, Harrison BT, Newby L, Hamilton JD. The quality in Australian health care study. Med J Aust. 1995;163: 458–471. doi: 10.5694/j.1326-5377.1995.tb124691.x PubMed DOI

Dhingra DN. WHO flagship ‘A decade of patient safety 2020–2030’. Patient Saf.

Lipsitz LA. Understanding health care as a complex system: the foundation for unintended consequences. JAMA. 2012;308: 243–244. doi: 10.1001/jama.2012.7551 PubMed DOI PMC

Carayon P. Human factors of complex sociotechnical systems. Appl Ergon. 2006;37: 525–535. doi: 10.1016/j.apergo.2006.04.011 PubMed DOI

Alami H, Lehoux P, Denis JL, Motulsky A, Petitgand C, Savoldelli M, et al.. Organizational readiness for artificial intelligence in health care: insights for decision-making and practice. J Health Organ Manag. 2020;35: 106–114. doi: 10.1108/JHOM-03-2020-0074 PubMed DOI

Kim L, Lyder CH, McNeese-Smith D, Leach LS, Needleman J. Defining attributes of patient safety through a concept analysis. J Adv Nurs. 2015;71: 2490–2503. doi: 10.1111/jan.12715 PubMed DOI

Kirwan B. Human error identification in human reliability assessment. Part 1: Overview of approaches. Appl Ergon. 1992;23: 299–318. doi: 10.1016/0003-6870(92)90292-4 PubMed DOI

Chang A, Schyve PM, Croteau RJ, O’Leary DS, Loeb JM. The JCAHO patient safety event taxonomy: a standardized terminology and classification schema for near misses and adverse events. Int J Qual Health Care. 2005;17: 95–105. doi: 10.1093/intqhc/mzi021 PubMed DOI

The difference between classification & taxonomy and why it matters. Bounteous [Internet]. [Cited 2024 Mar 1]. https://www.bounteous.com/insights/2020/11/18/difference-between-classification-taxonomy.

Donaldson LJ, Fletcher MG. The WHO World Alliance for Patient Safety: towards the years of living less dangerously. Med J Aust. Internet. 2006. [Cited 2024 Mar 1];184(S 10). Available from: https://onlinelibrary.wiley.com/doi/abs/10.5694/j.1326-5377.2006.tb00367.x PubMed DOI

WHO_IER_PSP_2010.2_eng.pdf [Internet]. [Cited 2023 Jun 19]. https://apps.who.int/iris/bitstream/handle/10665/70882/WHO_IER_PSP_2010.2_eng.pdf

Taib IA, McIntosh AS, Caponecchia C, Baysari MT. A review of medical error taxonomies: A human factors perspective. Saf Sci. 2011;49: 607–615. doi: 10.1016/j.ssci.2010.12.014 DOI

Fong A, Behzad S, Pruitt Z, Ratwani RM. A machine learning approach to reclassifying miscellaneous patient safety event reports. J Patient Saf. 2021;17: e829–e833. doi: 10.1097/PTS.0000000000000731 PubMed DOI

Rasmussen J. Information processing and human-machine interaction. An approach to cognitive engineering. New York: North-Holland; 1987.

Christianson MK, Sutcliffe KM, Miller MA, Iwashyna TJ. Becoming a high reliability organization. Crit Care. 2011;15: 314. doi: 10.1186/cc10360 PubMed DOI PMC

Astion ML, Shojania KG, Hamill TR, Kim S, Ng VL. Classifying laboratory incident reports to identify problems that jeopardize patient safety. Am J Clin Pathol. 2003;120: 18–26. doi: 10.1309/8EXC-CM6Y-R1TH-UBAF PubMed DOI

Shah RK, Kentala E, Healy GB, Roberson DW. Classification and consequences of errors in otolaryngology. Laryngoscope. 2004;114: 1322–1335. doi: 10.1097/00005537-200408000-00003 PubMed DOI

Amalberti R, Brami J. ‘Tempos’ management in primary care: a key factor for classifying adverse events, and improving quality and safety. BMJ Qual Saf. 2012;21: 729–736. doi: 10.1136/bmjqs-2011-048710 PubMed DOI PMC

Suliburk JW, Buck QM, Pirko CJ, Massarweh NN, Barshes NR, Singh H, et al.. Analysis of human performance deficiencies associated with surgical adverse events. JAMA Netw Open. 2019;2: e198067. doi: 10.1001/jamanetworkopen.2019.8067 PubMed DOI PMC

Stone A, Jiang ST, Stahl MC, Yang CJ, Smith RV, Mehta V. Development and interrater agreement of a novel classification system combining medical and surgical adverse event reporting. JAMA Otolaryngol Head Neck Surg. 2023;149: 424–429. doi: 10.1001/jamaoto.2023.0169 PubMed DOI PMC

Griffey RT, Schneider RM, Todorov AA, Yaeger L, Sharp BR, Vrablik MC, et al.. Critical review, development, and testing of a taxonomy for adverse events and near misses in the Emergency Department. Acad Emerg Med. 2019;26: 670–679. doi: 10.1111/acem.13724 PubMed DOI

Taib IA, McIntosh AS, Caponecchia C, Baysari MT. Comparing the usability and reliability of a generic and a domain-specific medical error taxonomy. Saf Sci. 2012;50: 1801–1805. doi: 10.1016/j.ssci.2012.03.021 DOI

Reason J. Human error. 1st ed. Cambridge: Cambridge University Press; 1990. 320 p. ISBN: 978–0521314190.

Vaughan S, Bate T, Round J. Must we get it wrong again? A simple intervention to reduce medical error. Trends Anaesth Crit Care. 2012;2: 104–108. doi: 10.1016/j.tacc.2012.01.007 DOI

Rodziewicz TL, Houseman B, Hipskind JE. Medical error reduction and prevention. Florida: StatPearls Publishing; 2023. PubMed

Street RL, Petrocelli JV, Amroze A, Bergelt C, Murphy M, Wieting JM, et al.. How communication “failed” or “saved the day”: counterfactual accounts of medical errors. J Patient Exp. 2020;7: 1247–1254. doi: 10.1177/2374373520925270 PubMed DOI PMC

Østergaard D, Dieckmann P, Lippert A. Simulation and CRM. Best Pract Res Clin Anaesthesiol. 2011;25: 239–249. doi: 10.1016/j.bpa.2011.02.003 PubMed DOI