Developing an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records

The digital transformation of medical data presents opportunities for novel approaches to manage patients with persistent hypertension. We sought to develop an actionable taxonomy of patients with persistent hypertension (defined as 5 or more consecutive measurements of blood pressure ≥160/100 mmHg over time) based on data from the electronic health records.

This qualitative study was a content analysis of clinician notes in the electronic health records of patients in the Yale New Haven Health System. Eligible patients were 18 to 85 years and had blood pressure ≥160/100 mmHg at 5 or more consecutive outpatient visits between January 1, 2013 and October 31, 2018. A total of 1664 patients met criteria, of which 200 records were randomly selected for chart review. Through a systematic, inductive approach, we developed a rubric to abstract data from the electronic health records and then analyzed the abstracted data qualitatively using conventional content analysis until saturation was reached.

We reached saturation with 115 patients, who had a mean age of 66.0 (SD, 11.6) years; 54.8% were female; 52.2%, 30.4%, and 13.9% were White, Black, and Hispanic patients. We identified 3 content domains related to persistence of hypertension: (1) non-intensification of pharmacological treatment, defined as absence of antihypertensive treatment intensification in response to persistent severely elevated blood pressure; (2) non-implementation of prescribed treatment, defined as a documentation of provider recommending a specified treatment plan to address hypertension but treatment plan not being implemented; and (3) non-response to prescribed treatment, defined as clinician-acknowledged persistent hypertension despite documented effort to escalate existing pharmacologic agents and addition of additional pharmacologic agents with presumption of adherence.

This study presents a novel actionable taxonomy for classifying patients with persistent hypertension by their contributing causes based on electronic health record data. These categories can be automated and linked to specific types of actions to address them.