The use and effectiveness of electronic clinical decision support
tools in the ambulatory/primary care setting: a systematic review
of the literature

Bryan, Cathy; Boren, Suzanne Austin

doi:10.14236/jhi.v16i2.679

Cited by 114 publications

(110 citation statements)

References 33 publications

(85 reference statements)

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“…Many experts have suggested that clinical decision support is essential for EHRs to fulfi ll the promise of quality improvement. [26][27][28] The current study suggests that even when clinical decision support is incorporated into the EHR, improvements in quality of care may be small. In fact, this is not the fi rst study to fi nd little benefi t from EHRbased clinical decision support.…”

Section: Discussionmentioning

confidence: 99%

Impact of EHR-Based Clinical Decision Support on Adherence to Guidelines for Patients on NSAIDs: A Randomized Controlled Trial

Gill

Mainous

Koopman

et al. 2011

The Annals of Family Medicine

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PURPOSE Electronic health records (EHRs) with clinical decision support hold promise for improving quality of care, but their impact on management of chronic conditions has been mixed. This study examined the impact of EHR-based clinical decision support on adherence to guidelines for reducing gastrointestinal complications in primary care patients on nonsteroidal anti-infl ammatory drugs (NSAIDs). METHODSThis randomized controlled trial was conducted in a national network of primary care offi ces using an EHR and focused on patients taking traditional NSAIDs who had factors associated with a high risk for gastrointestinal complications (a history of peptic ulcer disease; concomitant use of anticoagulants, antiplatelet medications [including aspirin], or corticosteroids; or an age of 75 years or older). The offi ces were randomized to receive EHR-based guidelines and alerts for high-risk patients on NSAIDs, or usual care. The primary outcome was the proportion of patients who received guideline-concordant care during the 1-year study period (June 2007-June 2008), defi ned as having their traditional NSAID discontinued (including a switch to a lower-risk medication), having a gastroprotective medication coprescribed, or both. RESULTSParticipants included 27 offi ces with 119 clinicians and 5,234 highrisk patients. Intervention patients were more likely than usual care patients to receive guideline-concordant care (25.4% vs 22.4%, adjusted odds ratio = 1.19; 95% confi dence interval, 1.01-1.42). For individual high-risk groups, patients on low-dose aspirin were more likely to receive guideline-concordant care with the intervention vs usual care (25.0% vs 20.8%, adjusted odds ratio = 1.30; 95% confi dence interval, 1.04-1.62), but there was no signifi cant difference for patients in other high-risk groups.CONCLUSIONS This study showed only a small impact of EHR-based clinical decision support for high-risk patients on NSAIDs in primary care offi ces. These results add to the growing literature about the complexity of EHR-based clinical decision support for improving quality of care.

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Section: Discussionmentioning

confidence: 99%

Impact of EHR-Based Clinical Decision Support on Adherence to Guidelines for Patients on NSAIDs: A Randomized Controlled Trial

Gill

Mainous

Koopman

et al. 2011

The Annals of Family Medicine

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“…The results were similar when only the CVD risk reduction studies were considered in 8 SRs. 9,11,[13][14][15][16][17][18] However, reminders were generally effective in improving clinical outcomes for hypertension. 9 Provider incentive interventions were included in 5 good-quality SRs and showed mixed effectiveness for both process-of-care and clinical outcomes-most of the positive outcomes were related to diabetes mellitus and asthma.…”

Section: Critical Questionmentioning

confidence: 99%

ACC/AHA Special Report: Clinical Practice Guideline Implementation Strategies: A Summary of Systematic Reviews by the NHLBI Implementation Science Work Group

et al. 2017

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Background: In 2008, the National Heart, Lung, and Blood Institute convened an Implementation Science Work Group to assess evidence-based strategies for effectively implementing clinical practice guidelines. This was part of a larger effort to update existing clinical practice guidelines on cholesterol, blood pressure, and overweight/obesity. Objectives: Review evidence from the published implementation science literature and identify effective or promising strategies to enhance the adoption and implementation of clinical practice guidelines. Methods: This systematic review was conducted on 4 critical questions, each focusing on the adoption and effectiveness of 4 intervention strategies: (1) reminders, (2) educational outreach visits, (3) audit and feedback, and (4) provider incentives. A scoping review of the Rx for Change database of systematic reviews was used to identify promising guideline implementation interventions aimed at providers. Inclusion and exclusion criteria were developed a priori for each question, and the published literature was initially searched up to 2012, and then updated with a supplemental search to 2015. Two independent reviewers screened the returned citations to identify relevant reviews and rated the quality of each included review. Results: Audit and feedback and educational outreach visits were generally effective in improving both process of care (15 of 21 reviews and 12 of 13 reviews, respectively) and clinical outcomes (7 of 12 reviews and 3 of 5 reviews, respectively). Provider incentives showed mixed effectiveness for improving both process of care (3 of 4 reviews) and clinical outcomes (3 reviews equally distributed between generally effective, mixed, and generally ineffective). Reminders showed mixed effectiveness for improving process of care outcomes (27 reviews with 11 mixed and 3 generally ineffective results) and were generally ineffective for clinical outcomes (18 reviews with 6 mixed and 9 generally ineffective results). Educational outreach visits (2 of 2 reviews), reminders (3 of 4 reviews), and provider incentives (1 of 1 review) were generally effective for cost reduction. Educational outreach visits (1 of 1 review) and provider incentives (1 of 1 review) were also generally effective for cost-effectiveness outcomes. Barriers to clinician adoption or adherence to guidelines included time constraints (8 reviews/overviews); limited staffing resources (2 overviews); timing (5 reviews/overviews); clinician skepticism (5 reviews/overviews); clinician knowledge of guidelines (4 reviews/overviews); and higher age of the clinician (1 overview). Facilitating factors included guideline characteristics such as format, resources, and end-user involvement (6 reviews/overviews); involving stakeholders (5 reviews/overviews); leadership support (5 reviews/overviews); scope of implementation (5 reviews/overviews); organizational culture such as multidisciplinary teams and low-baseline adherence (9 reviews/overviews); and electronic guidelines systems (3 reviews). Conclusion: The strategies of audit and feedback and educational outreach visits were generally effective in improving both process of care and clinical outcomes. Reminders and provider incentives showed mixed effectiveness, or were generally ineffective. No general conclusion could be reached about cost effectiveness, because of limitations in the evidence. Important gaps exist in the evidence on effectiveness of implementation interventions, especially regarding clinical outcomes, cost effectiveness and contextual issues affecting successful implementation.

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“…In addition, CDSS development involves much more than just the implementation of a software application. It requires adaptation by clinicians to use and engage in the refinement of CDSS both as a process and as a tool, as we move toward the goal of healthcare delivery that is consistent, effective, and of high quality [52] . In order to accomplish the above objectives and to reinforce the application of CDSS in clinical routine, there are a number of future perspectives that should be implemented.…”

Section: Future Perspectivesmentioning

confidence: 99%

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Tsolaki

Kousi

Svolos

et al. 2014

WJR

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In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and noninvasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

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The use and effectiveness of electronic clinical decision support tools in the ambulatory/primary care setting: a systematic review of the literature

Cited by 114 publications

References 33 publications

Impact of EHR-Based Clinical Decision Support on Adherence to Guidelines for Patients on NSAIDs: A Randomized Controlled Trial

Impact of EHR-Based Clinical Decision Support on Adherence to Guidelines for Patients on NSAIDs: A Randomized Controlled Trial

ACC/AHA Special Report: Clinical Practice Guideline Implementation Strategies: A Summary of Systematic Reviews by the NHLBI Implementation Science Work Group

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

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