Cited by 8Patient Safety and Medication ErrorsClinical Reasoning and Diagnostic SkillsHospital Admissions and Outcomes

The Problem with How We View Medical (and Diagnostic) Error in Emergency Medicine

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Ladell, Meagan M., Jacobson, Nancy L., Yale, Sarah C., McDermott, Katie L., Papautsky, Elizabeth L., Catchpole, Kenneth R., Scanlon, Matthew C.·2025·Academic Emergency Medicine

Abstract

Investigating error in healthcare has long been a central focus of patient safety efforts, yet this approach oversimplifies the complexities of a deeply interconnected and dynamic system. This paper argues that framing patient harm solely as "error" has failed to yield meaningful improvements, as it overlooks the broader factors contributing to adverse outcomes. Through a case study, we illustrate how an exclusive focus on error investigation missed critical insights and propose that attention should instead be directed towards identifying and managing hazards. Emphasizing the importance of treating healthcare as a sociotechnical system, we argue for the application of sociotechnical system thinking, particularly in addressing diagnostic errors in high-pressure environments like emergency medicine. The traditional methods, such as root cause analysis, error investigation, and the Swiss cheese model, are no longer sufficient. These outdated frameworks fail to address the systemic challenges clinicians face and often misguide safety interventions. The paper calls for a paradigm shift towards re-designing healthcare systems using human factors research to better align with the complexities of clinical work and the hazards that lead to harm.

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  • This fragmentation makes it challenging to understand how clinical, organizational, technological, and environmental factors collectively shape the complete picture of diagnosis.

    “This fragmentation makes it challenging to understand how clinical, organizational, technological, and environmental factors collectively shape the complete picture of diagnosis [34].”

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  1. [3]Reason J. Human Error. Cambridge University Press; 1990.
  2. [6]Hollnagel E, Amalberti R, eds. The emperor's new clothes: or whatever happened to “human error”. Proceedings of the 4th International Workshop on Human Error, Safety and Systems Development. Linköping University; 2001.
  3. [7]Karwowski W, Hollnagel E. Defining ergonomics/human factors. International Encyclopedia of Ergonomics and Human Factors‐3 Volume Set. CRC Press; 2006: 1934‐ 1941.
  4. [8]Kirwan B, Shorrock ST. A View from Elsewhere: Safety Culture in European Air Traffic Management. Patient Safety Culture. CRC Press; 2018: 349‐ 370.
  5. [20]Shorrock S. Human error’: the handicap of human factors. Hindsight. 2013; 18: 32‐ 37.
  6. [23]Cook RI, Woods DD. Operating at the Sharp End: the Complexity of Human Error. Human Error in Medicine. CRC Press; 2018: 255‐ 310.
  7. [25]Braithwaite J, Hollnagel E, Hunte G. Resilient Health Care: Muddling through with Purpose. Vol 6. CRC Press; 2021.
  8. [27]Abdellatif A, Bagian JP, Barajas ER, et al. Control of concentrated electrolyte solutions: patient safety solutions, volume 1, solution 5, may 2007. Jt Comm J Qual Patient Saf. 2007; 33(7): 447‐ 449.
  9. [41]Senders JW. Medical Devices, Medical Errors, and Medical Accidents. Human Error in Medicine. CRC Press; 2018: 159‐ 177.
  10. [46]Hollnagel E. Why Is Work‐as‐Imagined Different from Work‐as‐Done? Resilient Health Care. Vol 2. CRC Press; 2017: 279‐ 294.

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Authors
Ladell, M. M., Jacobson, N. L., Yale, S. C., McDermott, K. L., Papautsky, E. L., Catchpole, K. R., & Scanlon, M. C.
Affiliations
  • Medical College of Wisconsin (US)
  • University of Illinois Chicago (US)
  • Medical University of South Carolina Charleston South Carolina USA
Corresponding author
Ladell, Meagan M.
Venue
Academic Emergency Medicine
Publisher
Wiley
Published
2025-03
Volume
32
Issue
3
Pages
340-347
ISSN
  • 1069-6563 (ISSN-L)
  • 1553-2712
OpenAlex
W4408162122
Cited by
8
References
33
Citations by year
2026: 4 · 2025: 4
FWCI
10.39
Type
journal article
Language
en
Open access
Locations
  • Academic Emergency Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
  • Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
Keywords
medical errors, diagnostic errors, patient safety, emergency medicine, socio-technical systems, hazard identification, root cause analysis, Swiss cheese model, human factors, health system redesign, adverse events, error investigation
MeSH terms
Diagnostic Errors, Emergency Medicine, Humans, Medical Errors, Root Cause Analysis, Patient Safety

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