ArticleApplied clinical informatics2017
Effect of a Novel Clinical Decision Support Tool on the Efficiency and Accuracy of Treatment Recommendations for Cholesterol Management.
Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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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.
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Who cites it
19 citing papers in PubMed.
- Use of a Metabolic Syndrome SeverityDiabetes care · 2018Trial
- The Intersection of Heart Failure with Preserved Ejection Fraction and CKD.Clinical journal of the American Society of Nephrology : CJASN · 2026Review
- Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review.Interactive journal of medical research · 2024Review
- From Calculation to Communication: Using Risk Score Calculators to Inform Clinical Decision Making and Facilitate Patient Engagement.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Large Language Models for Efficient Medical Information Extraction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Atherosclerotic cardiovascular disease landscape in Singapore.Frontiers in cardiovascular medicine · 2024Review
- Workload and workflow implications associated with the use of electronic clinical decision support tools used by health professionals in general practice: a scoping review.BMC primary care · 2023Article
- How health systems facilitate patient-centered care and care coordination: a case series analysis to identify best practices.BMC health services research · 2022Review
- Effect of a novel pharmacist-led reporting system on appropriate use of direct-acting oral anticoagulants (DOACs) in a patient-centered medical home.Journal of thrombosis and thrombolysis · 2021Article
- Task-sharing with artificial intelligence: a design hypothesis for an Emergency Unit in sub-Saharan Africa.The Pan African medical journal · 2021Article
- Noninterruptive Clinical Decision Support Decreases Ordering of Respiratory Viral Panels during Influenza Season.Applied clinical informatics · 2020Article
- Associations of a metabolic syndrome severity score with coronary heart disease and diabetes in fasting vs. non-fasting individuals.Nutrition, metabolism, and cardiovascular diseases : NMCD · 2020Article
- Review
- Fast-track cardiology clinics-going Dutch!Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2019Article
- Physicians Voluntarily Using an EHR-Based CDS Tool Improved Patients' Guideline-Related Statin Prescription Rates: A Retrospective Cohort Study.Applied clinical informatics · 2019Article
- A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness.Applied clinical informatics · 2019Article
- Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation.NPJ digital medicine · 2019Review
- Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease.Journal of the American Heart Association · 2018Article
- Population health challenges in primary care: What are the unfinished tasks and who should do them?SAGE open medicine · 2018Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 2013 American College of Cardiology / American Heart Association Guidelines for the Treatment of Blood Cholesterol emphasize treatment based on cardiovascular risk. But finding time in a primary care visit to manually calculate cardiovascular risk and prescribe treatment based on risk is challenging. We developed an informatics-based clinical decision support tool, MayoExpertAdvisor, to deliver automated cardiovascular risk scores and guideline-based treatment recommendations based on patient-specific data in the electronic heath record.
objectiveTo assess the impact of our clinical decision support tool on the efficiency and accuracy of clinician calculation of cardiovascular risk and its effect on the delivery of guideline-consistent treatment recommendations.
methodsClinicians were asked to review the EHR records of selected patients. We evaluated the amount of time and the number of clicks and keystrokes needed to calculate cardiovascular risk and provide a treatment recommendation with and without our clinical decision support tool. We also compared the treatment recommendation arrived at by clinicians with and without the use of our tool to those recommended by the guidelines.
resultsClinicians saved 3 minutes and 38 seconds in completing both tasks with MayoExpertAdvisor, used 94 fewer clicks and 23 fewer key strokes, and improved accuracy from the baseline of 60.61% to 100% for both the risk score calculation and guideline-consistent treatment recommendation.
conclusionInformatics solution can greatly improve the efficiency and accuracy of individualized treatment recommendations and have the potential to increase guideline compliance.
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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 OpenQuestion graph.