From research to resources: developing a case-based learning curriculum for navigating clinical uncertainty

Purpose This study aimed at rethinking ways in which educators from different fields can collaborate to respond to the rapidly evolving demands of health professions education (HPE). The goal was to investigate how a research-to-resources approach can be applied to engage in knowledge translation (KT) of research findings for the benefit of introducing medical students to uncertainty in the clinical learning environment. Design/methodology/approach An interdisciplinary team of medical educators, human resource development (HRD) scholars and emergency medicine fellows engaged in iterative cycles of action research (AR) to develop, pilot and refine case-based learning resources on clinical uncertainty. The team leveraged prior research on physicians’ decision-making during COVID-19, experimented with generative AI tools, and collected feedback from medical students to guide resource development. Findings The findings of this study are twofold. On the one hand, the authors reflect on the lessons learned of developing case-based learning with the help of generative AI. While student feedback indicated that the case helped normalize and process experiences with uncertainty, key challenges included adapting research data to create relevant, sustainable learning resources and designing effective discussion prompts. On the other hand, the authors provide insights into the opportunities and challenges of our interdisciplinary collaboration. The authors show that knowledge utilization is not simple, but complex, and that more work needs to be done to effectively disseminate resources as part of the desired uncertainty curriculum. Practical implications This study attempts to apply a KT framework for bridging the research-practice gap in HPE through interdisciplinary collaboration and AR. It provides lessons learned for developing case-based curricula on complex topics like uncertainty. The findings highlight the need for adaptive KT processes when dealing with rapidly evolving healthcare contexts. Originality/value This paper offers a novel example of research-to-resource KT in medical education, integrating perspectives from HRD and leveraging emerging technologies. It contributes to understanding how interdisciplinary teams can collaborate to create timely, evidence-based educational resources for navigating uncertainty in professional practice. The study also provides insights into the challenges and opportunities of translating complex research findings into practical learning tools to tackle real-world challenges in HPE.

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Publication Details

Journal
European journal of training and development
Published
2024-10-25
DOI
https://doi.org/10.1108/ejtd-03-2024-0044
Citations
2
Primary Topic
Clinical Reasoning and Diagnostic Skills
Type
article
Field-Weighted Citation Impact
1.01
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article

From research to resources: developing a case-based learning curriculum for navigating clinical uncertainty

Henriette Lundgren, Dimitrios Papanagnou, Karen E. Watkins, Urvashi Vaid et al.
2 citations
European journal of training and development
Clinical Reasoning and Diagnostic Skills
1.01
article

From research to resources: developing a case-based learning curriculum for navigating clinical uncertainty

Henriette Lundgren, Dimitrios Papanagnou, Karen E. Watkins, Urvashi Vaid, Ridhima Ghei, Victoria J. Marsick, Abagayle Bierowski, Casey Morrone
article en
2 citations

Abstract

Purpose This study aimed at rethinking ways in which educators from different fields can collaborate to respond to the rapidly evolving demands of health professions education (HPE). The goal was to investigate how a research-to-resources approach can be applied to engage in knowledge translation (KT) of research findings for the benefit of introducing medical students to uncertainty in the clinical learning environment. Design/methodology/approach An interdisciplinary team of medical educators, human resource development (HRD) scholars and emergency medicine fellows engaged in iterative cycles of action research (AR) to develop, pilot and refine case-based learning resources on clinical uncertainty. The team leveraged prior research on physicians’ decision-making during COVID-19, experimented with generative AI tools, and collected feedback from medical students to guide resource development. Findings The findings of this study are twofold. On the one hand, the authors reflect on the lessons learned of developing case-based learning with the help of generative AI. While student feedback indicated that the case helped normalize and process experiences with uncertainty, key challenges included adapting research data to create relevant, sustainable learning resources and designing effective discussion prompts. On the other hand, the authors provide insights into the opportunities and challenges of our interdisciplinary collaboration. The authors show that knowledge utilization is not simple, but complex, and that more work needs to be done to effectively disseminate resources as part of the desired uncertainty curriculum. Practical implications This study attempts to apply a KT framework for bridging the research-practice gap in HPE through interdisciplinary collaboration and AR. It provides lessons learned for developing case-based curricula on complex topics like uncertainty. The findings highlight the need for adaptive KT processes when dealing with rapidly evolving healthcare contexts. Originality/value This paper offers a novel example of research-to-resource KT in medical education, integrating perspectives from HRD and leveraging emerging technologies. It contributes to understanding how interdisciplinary teams can collaborate to create timely, evidence-based educational resources for navigating uncertainty in professional practice. The study also provides insights into the challenges and opportunities of translating complex research findings into practical learning tools to tackle real-world challenges in HPE.

European journal of training and developmentVol. 49(3/4)
Thomas Jefferson University (US), University of Georgia (US), Columbia University (US)
Openalex Percentile: Top 20%
Clinical Reasoning and Diagnostic Skills
1.01
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