Visual Analytics Dashboard Promises to Improve Hypertension Guideline Implementation. (27th May 2021)

• Record Type: Journal Article
• Authors: Fadel, Raef Ali; Ross, Jacob; Asmar, Timothy; Sridasyam, Karthik; Demertzis, Zachary; Ahluwalia, Guneet; Roumayah, Tamara; Scott, Megan; Ibrahim, Hanan; Hammoudeh, Rawan; Gandhi, Nitesh; Flynn, Meaghan; Haftka-George, Alexis; Heidemann, Danielle; Sims, Sarah; Levy, Phillip; Miller, Joseph
• Is Part Of: American journal of hypertension. Volume 34:Number 10(2021)
• Page Start: 1078
• Page End: 1082
• Publication Date: 2021-05-27
• Subjects: blood pressure -- decision support system -- guidelines -- hypertension -- primary care -- social determinants of health; Hypertension -- Periodicals; 616.132005
• DOI: 10.1093/ajh/hpab081
• Languages: English
• ISSNs: 0895-7061

Abstract

Abstract: BACKGROUND: Primary care management of hypertension under new guidelines incorporates assessment of cardiovascular disease risk and commonly requires review of electronic health record (EHR) data. Visual analytics can streamline the review of complex data and may lessen the burden clinicians face using the EHR. This study sought to assess the utility of a visual analytics dashboard in addition to EHR in managing hypertension in a primary care setting. METHODS: Primary care physicians within an urban, academic internal medicine clinic were tasked with performing 2 simulated patient encounters for hypertension management: the first using standard EHR, and the second using EHR paired with a visual dashboard. The dashboard included graphical blood pressure trends with guideline-directed targets, calculated atherosclerotic cardiovascular disease risk score, and relevant medications. Guideline-appropriate antihypertensive prescribing, correct target blood pressure goal, and total encounter time were assessed. RESULTS: We evaluated 70 case simulations. Use of the dashboard with the EHR compared with use of the EHR alone was associated with greater adherence to prescribing guidelines (95% vs. 62%, P < 0.001) and more correct identification of blood pressure target (95% vs. 57%, P < 0.01). Total encounter time fell an average of 121 seconds (95% confidence interval 69–157 seconds, P < 0.001) in encounters that used the dashboard combined with the EHR. CONCLUSIONS: The integration of a hypertension-specific visual analytics dashboard with EHR demonstrates the potential to reduce time and improve hypertension guideline implementation. Further widespread testing in clinical practice is warranted.