Accepted for/Published in: JMIR Research Protocols
Date Submitted: Jul 12, 2023
Date Accepted: Nov 23, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Transformation and Articulation of Clinical Data to Understand Students’ and Health Professionals’ Clinical Reasoning: A Scoping Review Protocol
ABSTRACT
Background:
There are still unanswered questions regarding effective educational strategies to promote the transformation and articulation of clinical data while teaching and learning clinical reasoning. Additionally, understanding how this process can be analyzed and assessed is crucial, particularly considering the rapid growth of Natural Language Processing in artificial intelligence.
Objective:
To map educational strategies to promote the transformation and articulation of clinical data among students and healthcare professionals and to explore the methods used to assess these individuals’ transformation and articulation of clinical data.
Methods:
Design: A scoping review following the Joanna Briggs Institute framework for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. A literature search was performed in November 2022 using five databases: CINAHL, MEDLINE, EMBASE, PsycINFO and Web of Science. The protocol was registered on the Open Science Framework in May 2023. The scoping review will follow the nine-step framework proposed by Peters, et al. [1] of the Joanna Briggs Institute. A data extraction form has been developed using key themes from the research questions.
Results:
After removing duplicates, the initial search yielded 6,656 results, and study selection is underway. The extracted data will be qualitatively analyzed and presented in a diagrammatic or tabular form alongside a narrative summary. The review will be completed by the summer of 2023.
Conclusions:
By synthesizing the evidence on semantic transformation and articulation of clinical data during clinical reasoning education, this review aims to contribute to the refinement of educational strategies used in academic and continuing education programs. The insights gained from this review will help educators develop more effective approaches to promote and assess the transformation and articulation of clinical data among students and health professionals. The review will shed light on the potential of automatic Natural Language Processing software in educational context. Understanding the effective use of automatic Natural Language Processing software can enhance the educational experience by providing opportunities for realistic interactions, language modelling, and feedback.
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