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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
Utilization of Artificial Intelligence in Reducing the Incidence of Medication Error: A Bibliometric Analysis
Michael Joseph S Dino1,2, Yobhel B Arellano1,3, Abigail G Evangelista1,4
1Research Development and Innovation Center Our Lady of Fatima University Valenzuela City Philippines.
Background And Aims:
Medication errors (MEs) represent a significant challenge in healthcare, compromising patient safety and contributing to adverse outcomes. Artificial intelligence (AI) has emerged as a promising tool to address this issue by enhancing medication management processes and decision support systems. This study aims to visualize and examine the dissemination of published work on AI-related research in reducing MEs.
Methods:
Data collected from the Scopus database was used for bibliometric analysis. One hundred eighty-four (n = 184) relevant articles were identified and analyzed using VOS Viewer software, which examined citations, keyword relations, and network analysis to identify key contributors, influential publications, and emerging themes.
Results:
The study revealed that most articles published were empirical, written by multiple authors from more developed nations, and published in medical-related journals. There has been a stable increase in publications since 1991, peaking in 2023, with several authors, organizations, and journals publishing more than others. Notable keywords such as "medication error", "artificial intelligence", and "patient safety" highlight central concepts explored in the research on AI and medication error reduction. The clustering analysis identified overarching themes, including providing insights into the right patient, right dose, right time, right assessment, right route, and right drug (6R's) to AI's potential roles in mitigating MEs.
Conclusion:
Empirical research is crucial for understanding AI utilization in reducing MEs. The medical community is increasingly interested in using AI to mitigate MEs and address critical issues related to patient safety in medication administration. The identified prominent keywords and themes illustrate AI's potential in enhancing healthcare delivery and reducing mistakes, paving the way for further exploration and practical application in clinical settings. Additional studies on AI use in reducing MEs should be conducted in less developed countries.
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