ArticleDigital health
Clinicians' perspectives on the adoption and implementation of EMR-integrated clinical decision support tools in primary care.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Implementation of Electronic Clinical Decision Support for Radiology Referrals: The Role of Governance, Clinician Engagement and Education.Emergency medicine Australasia : EMA · 2026Article
- Factors associated with chronic pain clinical decision support use in primary care.PLOS digital health · 2026Article
- Surgeon-Informed Clinical Decision Support Software for Surgical Risk Prediction and Outcomes Tracking.The Journal of surgical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.