Despite near universal adoption of Electronic Health Records (EHRs), resulting in increasing amount of clinical data available to improve care, challenges remain about how to best leverage Health IT for Clinical Decision Support (CDS). In this proposal, we will use hypertension to use new technologies to make tailored, useful CDS available across settings and implementations. We will use Clinical Quality Language (CQL), a new Health Level Seven International Standard that focuses on a common model for representing expression logic for Clinical Decision Support (CDS), and the Fast Healthcare Interoperable Resources (FHIR) standard, to demonstrate how one can reduce the burden of implementing CDS rules across sites. Per the 5 Rights framework, well-implemented CDS aims to deliver the right information, to the right person, using the right format, in the right channel, and at the right time during workflow. This grant proposes to enhance our ability to tailor CDS by creating patient-centered, interoperable, sharable CDS tools by allowing providers and patients to make patient-centered decisions about clinical actions based on how they interpret variation in conflicting guidelines in practice. Next, leveraging previous work, we will build clinical decision support components using the AHRQ CDS Authoring tool and build a FHIR application that can elicit and provide guidance to both patients and health care teams, validating against a database of patients with hypertension. Finally, we will evaluate this application with appropriate patients and health care teams to learn its potential impact in assisting in tailored decision making, refining the approach and disseminating. We'll disseminate all of the elements in this study to the public and measure how often they are used and adapted.
Currently, vascular disease ? heart attacks and strokes ? are the number one causes of mortality in the United States; preventing these events is a significant public health need. Controlling blood pressure will reduce deaths, but the guidelines and respective Clinical Decision Support tools are neither flexible for patient need and can't be shared effectively across settings. In this application we propose the translation of hypertension guidelines and protocols into tailored, interoperable, sharable Clinical Decision Support by allowing providers and patients to make patient-centered decisions about clinical actions based on how they interpret variation in conflicting guidelines for treatment in practice in order to reduce these events and improve public health.
