Real-World Evaluation of an Automated Algorithm to Detect Patients with Potentially Undiagnosed Hypertension in an Ethnically Diverse, Large Health System in Hawaiʻi

Abstract

ABSTRACTObjectiveThis real-world evaluation considers an algorithm designed to detect patients with potentially undiagnosed hypertension, receiving routine care, in a large health system in Hawaiʻi. It quantifies patients identified as potentially undiagnosed with hypertension, summarizes the individual, clinical, and health system factors associated with undiagnosed hypertension, and examines if the COVID-19 pandemic impacted detection.MethodsWe analyzed the electronic health records (EHR) of patients treated across 6 clinics from 2018-2021. We calculated total patients with potentially undiagnosed hypertension and compared patients flagged for undiagnosed hypertension to those with diagnosed hypertension and to the full patient panel across individual characteristics, clinical and health system factors (e.g., clinic of care), and timing. Modified Poisson regression was used to calculate crude and adjusted risk ratios.ResultsAmong the eligible patients (N=13,364), 52.6% had been diagnosed with hypertension, 2.7% were flagged as potentially undiagnosed, and 44.6% had no evidence of hypertension. Factors associated with a higher risk of potentially undiagnosed hypertension included: individual characteristics (ages 40-84 compared to 18-39 years), clinical (lack of diabetes diagnosis) and health system factors (clinic site and being a Medicaid versus a Medicare beneficiary), and timing (readings obtained after the COVID-19 Stay-At-Home Order in Hawaiʻi).ConclusionsThis evaluation provided evidence that a clinical algorithm implemented within a large health systems’s EHR could detect patients in need of follow-up to determine hypertension status, and it identified key individual characteristics, clinical and health system factors, and timing considerations that may contribute to undiagnosed hypertension among patients receiving routine care.