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A new perspective: blending ecological psychology and Safety II to learn from diagnostic excellence

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Published/Copyright: April 14, 2026
Diagnosis
From the journal Diagnosis

Abstract

Objectives

Traditional safety frameworks applied in isolation have been insufficient to address the problem of diagnostic error in healthcare. We created a novel blend of approaches – leveraging Safety II and ecological psychology – to investigate diagnostic good catch events where healthcare teams made timely and accurate diagnoses.

Methods

We performed a case series of qualitative interviews, grounded in ecological psychology, with individuals involved in diagnostic good catch events at our institution. From these interviews, we identified facilitators to the diagnostic process which were organized in a People Environments Tools and Technologies Scan.

Results

We conducted 3 semi-structured interviews with a total of 4 clinicians. We identified numerous strategies, activities and contextual features that contributed to an individual’s and team’s ability to make accurate and timely diagnoses. Examples included the use of a “trust but verify” practice when admitting new patients, a thorough physical exam enhanced by point of care ultrasound, and the utility of open spaces to encourage collaboration among providers.

Conclusions

A novel framework, coupling Safety II principles and ecological psychology, offers a complement to lessons learned from Safety I event reviews and provides opportunities to develop robust and pragmatic interventions to mitigate diagnostic error.


Corresponding author: Jonathan G. Sawicki, MD, MSCI, Department of Pediatrics, University of Utah School of Medicine, 100 N. Mario Capecchi Drive, Salt Lake City, UT 84113 USA; and Division of Hospital Medicine, Primary Children’s Hospital, Salt Lake City, UT, USA, E-mail:

Acknowledgments

The authors would like to thank Laura Zwann for her review of this manuscript.

  1. Research ethics: The study institution’s Institutional Review Board determined there to be minimal or no risk to individuals’ privacy by publication of this article.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/dx-2025-0149).


Received: 2025-10-17
Accepted: 2026-03-02
Published Online: 2026-04-14

© 2026 Walter de Gruyter GmbH, Berlin/Boston

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