Clinicians' perspectives on the adoption and implementation of EMR-integrated clinical decision support tools in primary care
- PMID: 40297364
- PMCID: PMC12035066
- DOI: 10.1177/20552076251334043
Clinicians' perspectives on the adoption and implementation of EMR-integrated clinical decision support tools in primary care
Abstract
Objective: Understand the perceptions of primary care clinicians on the challenges, barriers, and successful strategies for implementing and disseminating clinical decision support (CDS) tools in primary care.
Methods: Qualitative research involving in-depth interviews with 32 primary care clinicians practicing in a range of settings across the United States. Semi-structured interviews were conducted between July 2021 and September 2023.
Results: All participants reported using CDS tools for patient care, with high variability in the frequency of use and the type of tools used. Fewer clinicians described using machine learning-based systems and risk assessment tools using predictive analytics. Most clinicians were favorable toward enhanced use of CDS tools for patient care if used along with clinical judgment and patient preferences. Clinicians described tremendous barriers to the adoption and implementation of EMR-integrated CDS tools, including clinician resistance, organizational approval, and lack of infrastructure and resources. Clinicians stressed the importance of communicating evidence on the effectiveness of CDS tools, integrating tools with existing EMR systems, and having an easy-to-navigate interface. Strategies for the implementation of CDS tools included an organizational champion, technical assistance, and education and training.
Conclusions: CDS tools have the potential to be valuable assets in treating patients in primary care and could improve diagnostic accuracy, enhance personalized treatment plans, and ultimately advance the quality of patient care. There are many concerns with the use of EMR-integrated CDS tools in primary care that should be considered including evidence of the tool's effectiveness, data security and privacy protocols, workflow integration, and clinician burden.
Keywords: Clinical decision support; artificial intelligence; electronic medical record; primary care; qualitative research.
© The Author(s) 2025.
Conflict of interest statement
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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References
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- Lopez MH, Weingarten S, Teich JM, et al. (eds). National Academy of Medicine; The Learning Health System Series. Optimizing Strategies for Clinical Decision Support: Summary of a Meeting Series . Washington: (DC: ): National Academies Press (US); 2017. PMID: 37733888. - PubMed
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- HealthIT.gov. What is Clinical Decision Support (CDS)?. Clinical Decision Support | HealthIT.gov (Accessed on February 12, 2024).
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- . USC § 9401(3) Chapter 119-National Artificial Intelligence Initiative. https://uscode.house.gov/view.xhtml?req=(title:15%20section:9401%20editi... (Accessed on April 30, 2024)
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