Learning from what goes right: a safety-II framework for improving diagnosis at the point of care
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Taro Shimizu
Abstract
Traditional approaches to improving diagnosis in medicine have focused mainly on identifying and analyzing errors using the Safety-I perspective. Yet, the vast majority of diagnostic encounters are successful, and structured reflection on these positive outcomes remains uncommon in current practice. In this article, I introduce SIDER (Specification, Ishikawa diagram, driver diagram, Engaging the patient and the team, Reflection), a practical protocol designed to embed Safety-II principles into routine diagnostic reflection by encouraging clinicians to learn from what goes right. SIDER guides clinicians through five clear phases: specifying a particularly challenging or instructive case, mapping contributing factors using an Ishikawa diagram, translating those findings into actionable strategies with a driver diagram, engaging the care team and patient to gather collective insights, and concluding with individual reflection to support ongoing calibration and learning. I describe how SIDER enables clinicians to extract broad and transferable lessons from successful but complex diagnostic cases, complementing traditional error analysis and supporting a culture of continuous improvement. By adopting this framework, healthcare teams can expand opportunities for experiential learning, strengthen adaptive expertise, and advance safer diagnostic practice. Future studies are warranted to evaluate the effectiveness of SIDER-guided reflection in enhancing diagnostic performance and improving patient outcomes.
Acknowledgments
The author would like to thank Professors Anjala V. Tess and Brittany Esty for their valuable advice and encouragement regarding the use of quality improvement tools. Their insights and support were helpful in the development of the concepts presented in this manuscript.
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Research ethics: Not applicable.
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Informed consent: Not applicable. All cases presented in this paper are partially fictionalized based on the author’s clinical experiences and do not involve identifiable personal information.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: None declared.
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Research funding: None declared.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/dx-2025-0086).
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