Title

Using clinical decision support to improve referral rates in severe symptomatic aortic stenosis: a quality improvement initiative

Aurora Affiliations

Cardiovascular and Thoracic Surgery, Cardiology, Aurora Medical Group, Administration

Abstract

Clinical decision support systems are used to ensure compliance with guidelines and can assist providers in improving quality of care. This quality improvement initiative was designed to evaluate the use of a clinical decision support system to improve specialist referral rate for patients with severe aortic stenosis. A clinical decision support system for cardiology and primary care providers was implemented to improve diagnosis of severe aortic stenosis. The ordering provider received an electronic medical record in-basket alert providing feedback and recommendations for referral to specialist for evaluation. The echocardiogram data were evaluated for change in specialist referral rate. Before clinical decision support system implementation, the referral rate was 72% for a 3-month period. All providers ordering echocardiograms received clinical decision support system notification if patient results met criteria based on valve severity (aortic valve area < 1.0 cm, mean gradient ≥ 40 mm Hg, peak velocity ≥ 4.0 m/s). After implementation, clinical decision support system referral rate was 97.5%, a 24.6% increase in referral rates (P < .001). Low referral rates for patients with severe aortic stenosis are a recognized challenge. Utilizing the clinical decision support system to improve awareness of quality guidelines and recommendations was associated with increased adherence to referral guidelines by providers. This innovation is pertinent to practice and enhances the functionality of the electronic medical record by providing real-time feedback to providers to improve practice. Referral rates for patients with severe aortic stenosis can be improved with use of provider clinical decision support system.

Document Type

Article

PubMed ID

30134257

DOI

10.1097/CIN.0000000000000471

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