UC San Francisco has partnered with the city’s Department of Public Health to provide a simplified intervention that improves blood pressure control.

Researchers adapted Kaiser Permanente evidence-based protocols to a racially and ethnically diverse population at 12 safety net clinics and developed an internal hypertension patient registry—in order to track blood pressure management—as well as a treatment algorithm that encouraged the use of fixed-dose medications, with two or more drugs in a single pill.

In addition to the algorithm, the program included regular check-up visits in which nurses and pharmacists were allowed to take standardized blood pressure measurements. All 12 of the safety net clinics shared the same eClinicalWorks electronic health record system.

Also See: Algorithm uses EHR data to identify undiagnosed hypertension patients

Results of a study published earlier this month in Circulation: Cardiovascular Quality and Outcomes, showed that blood pressure control increased from 68 percent to 74 percent at an initial pilot site over 24 months, and when the other 11 clinics were added the hypertension, control rates rose from 69 percent to 74 percent over 15 months.

“The intervention was associated with increases in blood pressure control for all racial and ethnic groups, with higher rates of improvement for black patients as compared with whites,” state the authors. “This study demonstrates that evidence-based treatment protocols are transportable to safety net settings and could play a pivotal role in achieving improved blood pressure control and reducing racial disparities in hypertension.”

Valy Fontil, MD, the study’s first author and assistant professor of medicine at UC San Francisco, notes the importance of “pulling” patient data on a weekly basis as part of the blood pressure control program to ensure that clinicians have the information to track hypertension management over time.

“My next step as an investigator is to computerize the treatment algorithm,” adds Fontil. “I’m in the process of automating it and having it be interoperable with the electronic health record as a computerized clinical decision support system.”

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