Improving Identification and Diagnosis of Hypertensive Patients Hiding in Plain Sight (HIPS) in Health Centers

Abstract

Background: Hypertension is the most prevalent chronic condition diagnosed among patients served in the safety net in the United States; however, many safety-net patients with hypertension are not formally diagnosed and may remain untreated and at increased risk for cardiovascular events. Identifying undiagnosed hypertension using algorithmic logic programmed into clinical decision support (CDS) approaches is a promising practice but has not been broadly tested in the safety-net setting.

Methods: The project used a quality improvement approach wherein information flows and actions related to blood pressure measurement were modified to include algorithm criteria to identify patients who might have undiagnosed hypertension. Identified patients were recalled for evaluation and hypertension diagnosis, if appropriate. Ten health centers in Arkansas, California, Kentucky, and Missouri were selected to participate in the project on the basis of high hypertension prevalence (compared to national average), demographic and geographic diversity, mature information systems infrastructure, and executive support. The project targeted patients from 18 to 85 years of age.

Results: After implementation of algorithm-based interventions, diagnosed hypertension prevalence increased significantly from 34.5% to 36.7% (p <0.05). A cohort of patients was tracked from 8 of the 10 health centers to assess follow-up evaluation and diagnosis rates; 65.2% completed a follow-up evaluation, of which 31.9% received a hypertension diagnosis.

Conclusion: Using algorithmic logic and other CDS-enabled care process improvements appears to be an effective way health centers can identify and engage patients at risk for undiagnosed hypertension. Appropriately diagnosing all hypertensive patients ensures that hypertension control efforts yield maximal improvements in population health.