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Rooted in reasoning: a clinical reasoning curriculum using diagnostic RCAs

  • David Klimpl ORCID logo EMAIL logo and Stacey Staudinger
Published/Copyright: October 7, 2025
Diagnosis
From the journal Diagnosis

Abstract

Objectives

Clinical reasoning skills are required for safe care, yet they are not consistently taught to advanced practice providers (APPs). In hospital medicine, where APPs work semi-independently, gaps in clinical reasoning can increase the likelihood of error. To address this, we developed a module that uses diagnostic root cause analysis (RCA) to teach clinical reasoning skills to hospital medicine APP fellows.

Methods

The curriculum was delivered from July 2021 to March 2025. Fellows selected real-world diagnostic errors encountered during clinical rotations, created cognitive fishbone diagrams, and presented their analysis in small-group.

Results

Twenty-seven fellows completed the module and pre-post assessment surveys. Statistically significant improvements were observed across all six domains of knowledge and confidence related to identifying error contributors, analyzing cases, and setting goals. Free-text responses highlighted the module’s emotional safety, peer learning value, and normalization of diagnostic reflection. Two learners published their projects as academic posters, and one graduate now co-facilitates the sessions.

Conclusions

This module offers a scalable, time-efficient approach to clinical reasoning education that is adaptable across learner levels and specialties. Its peer-led design fosters psychological safety, reflective practice, and creates a natural pathway for APPs to engage in microscholarship – addressing a critical gap in both education and academic inclusion.


Corresponding author: David Klimpl, MD, MS, Department of Medicine, Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, CO, USA, E-mail:

  1. Research ethics: University of Colorado IRB # 21-4386 awarded 2/2021.

  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: The data that support the findings of this study are available from the corresponding author, [DK], upon reasonable request.

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Received: 2025-07-05
Accepted: 2025-08-17
Published Online: 2025-10-07

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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