Literature DB >> 31862329

Computerized Advisory Decision Support for Cardiovascular Diseases in Primary Care: A Cluster Randomized Trial.

Paul M McKie1, Daryl J Kor2, David A Cook3, Maya E Kessler4, Rickey E Carter5, Patrick M Wilson6, Laurie J Pencille7, Branden C Hickey8, Rajeev Chaudhry9.   

Abstract

PURPOSE: The purpose of this research was to evaluate the impact of an outpatient computerized advisory clinical decision support system (CDSS) on adherence to guideline-recommended treatment for heart failure, atrial fibrillation, and hyperlipidemia.
METHODS: Twenty care teams (109 clinicians) in a primary care practice were cluster-randomized to either access or no access to an advisory CDSS integrated into the electronic medical record. For patients with an outpatient visit, the CDSS determined if they had heart failure with reduced ejection fraction, hyperlipidemia, or atrial fibrillation; and if so, was the patient receiving guideline-recommended treatment. In the intervention group, an alert was visible in the medical record if there was a discrepancy between current and guideline-recommended treatment. Clicking the alert displayed the treatment discrepancy and recommended treatment. Outcomes included prescribing patterns, self-reported use of decision aids, and self-reported efficiency. The trial was conducted between May 1 and November 15, 2016, and incorporated 16,310 patient visits.
RESULTS: The advisory CDSS increased adherence to guideline-recommended treatment for heart failure (odds ratio [OR] 7.6, 95% confidence interval [CI], 1.2, 47.5) but had no impact in atrial fibrillation (OR 0.94, 95% CI 0.15, 5.94) or hyperlipidemia (OR 1.1, 95% CI 0.6, 1.8). Clinicians with access to the CDSS self-reported greater use of risk assessment tools for heart failure (3.6 [1.1] vs 2.7 [1.0], mean [standard deviation] on a 5-point scale) but not for atrial fibrillation or hyperlipidemia. The CDSS did not impact self-assessed efficiency. The overall usage of the CDSS was low (19%).
CONCLUSIONS: A computerized advisory CDSS improved adherence to guideline-recommended treatment for heart failure but not for atrial fibrillation or hyperlipidemia.
Copyright © 2020. Published by Elsevier Inc.

Entities:  

Keywords:  Atrial fibrillation; Clinical decision support; Electronic medical record; Heart failure; Hyperlipidemia

Mesh:

Year:  2019        PMID: 31862329     DOI: 10.1016/j.amjmed.2019.10.039

Source DB:  PubMed          Journal:  Am J Med        ISSN: 0002-9343            Impact factor:   4.965


  6 in total

Review 1.  Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research.

Authors:  Pishoy Gouda; Justin Ezekowitz
Journal:  J Cardiovasc Transl Res       Date:  2022-09-14       Impact factor: 3.216

2.  Implementing Clinical Decision Support Tools and Pharmacovigilance to Reduce the Use of Potentially Harmful Medications and Health Care Costs in Adults With Heart Failure.

Authors:  Armando Silva Almodóvar; Milap C Nahata
Journal:  Front Pharmacol       Date:  2021-04-30       Impact factor: 5.988

3.  A virtual platform to deliver ambulatory care for patients with atrial fibrillation.

Authors:  Willy Weng; Chris Blanchard; Jennifer L Reed; Kara Matheson; Ciorsti McIntyre; Chris Gray; John L Sapp; Martin Gardner; Amir AbdelWahab; Jason Yung; Ratika Parkash
Journal:  Cardiovasc Digit Health J       Date:  2020-11-28

Review 4.  A systematic review on the effectiveness and impact of clinical decision support systems for breathlessness.

Authors:  Anthony P Sunjaya; Sameera Ansari; Christine R Jenkins
Journal:  NPJ Prim Care Respir Med       Date:  2022-08-20       Impact factor: 3.289

5.  Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis.

Authors:  Winnie Chen; Kirsten Howard; Gillian Gorham; Claire Maree O'Bryan; Patrick Coffey; Bhavya Balasubramanya; Asanga Abeyaratne; Alan Cass
Journal:  J Am Med Inform Assoc       Date:  2022-09-12       Impact factor: 7.942

6.  Validation of the WATCH-DM and TRS-HFDM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis.

Authors:  Matthew W Segar; Kershaw V Patel; Anne S Hellkamp; Muthiah Vaduganathan; Yuliya Lokhnygina; Jennifer B Green; Siu-Hin Wan; Ahmed A Kolkailah; Rury R Holman; Eric D Peterson; Vaishnavi Kannan; Duwayne L Willett; Darren K McGuire; Ambarish Pandey
Journal:  J Am Heart Assoc       Date:  2022-06-03       Impact factor: 6.106

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.