TRENDS IN CLINICAL REASONING ASSESSMENT: A BIBLIOMETRIC ANALYSIS SPANNING 1974-2024
DOI:
https://doi.org/10.56238/arev6n2-187Palabras clave:
Clinical Reasoning, Education, Medical, Technology, Clinical Decision-Making, Analysis, BibliometricResumen
Introduction: Clinical reasoning guides diagnosis and patient care. Bibliometric analysis helps navigate scholarly publications, offering insights into trends and influential works, aiding evidence-based decisions, and improving patient outcomes. This study aims to analyze clinical reasoning assessment literature, exploring distribution across document types and languages, influential sources, volume of publications and citations, thematic clusters from author keywords, and innovative research shaping the forefront of investigation. Materials and Methods: A bibliometric analysis was performed using the Scopus database as of February 10th, 2024, covering data from 1974 to the research date. Data extraction involved document types, languages, key sources, globally cited publications, and trends over time, with network visualization of author keywords. Analysis employed the Bibliometrix package in Rstudio and VOSviewer software. Reporting adhered to PRIBA guidelines by Koo & Lin. Results: The Scopus database search yielded 1827 documents, predominantly in English in the article format. Notable sources included BMC Medical Education (UK), Academic Medicine (USA), Advances in Health Sciences Education (Netherlands), and Diagnosis (Germany). Trends from 1974 to 2024 showed increasing publications and citations. Recent publications highlighted emerging themes such as artificial intelligence, electronic health records, and chatGPT, reflecting the evolving landscape of medical assessment practices. Conclusions: This bibliometric analysis highlights the evolving landscape of clinical reasoning assessment within medical education, where recent trends embrace innovative methodologies like artificial intelligence, electronic health records, and chatGPT. These trends reflect a dynamic shift towards the use of technology to enhance diagnostic accuracy and decision-making processes.