Working with a team of primary care clinicians, medical informatics guideline experts, and experts on hypertension, researchers for this project will revise the existing automated decision support system for evidence-based management of primary hypertension -- ATHENA DSS -- to upgrade to the latest hypertension guidelines. We call the new system ATHENA-HTN. During year 1 of the project we plan to install the system and implement it so our collaborating clinicians can become familiar with it and help us fine-tune the installation.

Research objectives:

The primary goal of this project is to improve blood pressure control in patients with hypertension through a new model of care delivery, Group Medical Visits. Patients with hypertension receive regular medical care in a group setting that is designed to promote effective self-management of hypertension and to encourage patients to follow their primary care clinician's advice. Clinicians are given guideline-based information on antihypertensive drugs through the ATHENA Decision Support System.

Using pre-existing data sets, this study will evaluate changes from 1994-2004 in the detection, diagnosis and treatment of hypertension and will identify contributors (clinical and non-clinical) to disparities in the detection and treatment of hypertension, looking at comorbidities, age, sex, race/ethnicity, medical insurance coverage and physician specialty. Statistical analyses will be performed to quantify the extent to which treatment goals were achieved with various classes of anti-hypertensive medications.

Hypertension is common, harmful and undertreated. The Assessment and Treatment of Hypertension: Evidence-Based Automation (ATHENA) project seeks to improve the treatment of hypertension through a computer-based decision support system that analyzes clinical information about each patient to generate recommendations for managing hypertension. Physicians receive customized treatment recommendations, together with a rationale for the recommendations, at the time of patient visits.

This project aims to develop a standardized computational framework for clinical practice guideline specification that will allow text guidelines to be easily translated into computable formats by clinical domain experts. These computable guidelines will then be directly applicable to real-time clinical decision support and to retrospective quality assessment of electronic medical records.