Development of an entirely remote, non‐physician led hypertension management program

Abstract

Background Hypertension remains poorly controlled on the population level. National rates of control, even when defined leniently by BP < 140/90 mm Hg, are only ~50%. As growing healthcare costs coincide with tighter blood pressure (BP) targets, innovative management programs are needed to maximize efficiency of care delivery and optimize control. Hypothesis We aimed to develop a remote, navigator‐led hypertension innovation program that would leverage algorithmic care pathways, home BP measurements and patient coaching to allow rapid and complete medication titration. Methods A multidisciplinary group of clinical experts from subspecialties and primary care collaborated to develop an evidence‐based clinical algorithm, designed to be automated and administered by non‐licensed patient navigators. In the development stage, a prospective pilot cohort of 130 patients was managed by nurse practitioners and pharmacists to ensure efficacy and safety. Patients with clinic BP ≥ 140/90 mm Hg were enrolled and given a Bluetooth‐enabled BP device. Home BPs were transmitted automatically into the electronic medical record. Medication titrations were performed by phone at biweekly intervals, based upon weekly average BP, until home BP was controlled at <135/85 mm Hg. Results Eighty‐one percent of all enrolled, and 91% of those patients who regularly measured home BP achieved goal, in an average of 7 weeks. Control was reached similarly across races, genders, and ages. Conclusions A home‐based BP control program run by non‐physicians can provide efficient, effective and rapid control, suggesting an innovative paradigm for hypertension management. This program is effective, sustainable, adaptable, and scalable to fit current and emerging national systems of healthcare.

Remote Patient Monitoring Program for Hospital Discharged COVID-19 Patients

“…However, data supporting the use of RPM have so far been mixed, with variable results depending on condition and implementation. 16,[20][21][22][23][24][25][26] As COVID-19 cases began to emerge in our region, we identified a need for managing an increasing number of patients admitted to the hospital with COVID-19 in the setting of unparalleled resource constraints. We therefore rapidly designed and deployed an RPM program so that we could monitor and manage patients after discharge in their home environment using pulse oximeters, thermometers, and an app-based symptom assessment tool, monitored by a team of nurses.…”

Patient-generated health data and electronic health record integration: a scoping review

“… 32 Other studies found onboarding sessions, patient instruction manuals, and other device-support materials given to patients before participating in PGHD integration activities to be critical. Fisher et al 33 described a program with sustainable scalability that used nonlicensed patient navigators to provide patient-facing education.…”

“… 41 Setup time with patients added 45–60 min to a scheduled visit according to another article, and in others, time was needed to develop customized flowsheets, create patient registries, and identify relevant evidence for the use of PGHD in clinical care. 33 , 35 , 36 , 42 …”

Use of machine learning to predict hypertension-related complication outcomes of varying severity

“…The models can also identify patients without previous complication events. Stratification of care management resources could include identifying candidates for remote monitoring programs at home [33, 34], and designating outreach resources ranging from digital strategies to personalized contact with a population health consultant, an engagement specialist, or a clinician. In this way, leveraging these predicative models can help improve the financial and health outcomes impact of care management.…”