Abstract 012: Pilot Testing of Hypertension Management Support with EHR-Integrated Telemonitoring and Automated Patient Messaging for Patients at High Risk from Hypertension

Introduction: Remote patient monitoring (RPM) of blood pressure (BP) can help support hypertension management. To improve the timely interpretation and action based on home BP values, automated messages providing clinical guidance could be delivered to patients based on the results of their home BP monitoring and used to encourage monitoring when additional information is needed for decision making. We hypothesized that among patients with elevated office BP at increased risk from hypertension due to coexisting cardiovascular disease, renal disease or diabetes mellitus, RPM with automated patient messages delivered through an EHR portal would lead to antihypertensive medication adjustments and improved home BP values. Methods: This was a pilot test of RPM with automated messages to patients sent in response to home BP values. Seven clinicians (5 primary care physicians and two nephrologists) reviewed lists of their patients with qualifying comorbidities whose most recent two office BP were ≥140/90 mm Hg (n = 285). Standardized home BP readings supervised by study staff were taken at baseline and 3 months, with follow up at 9 months planned. Antihypertensive medication changes, number of messages sent, and number read by the patient were assessed from EHR data. Results: Clinicians gave approval for 82% (n = 234) of eligible patients to be contacted, of whom 30% (n=70) enrolled. Of enrollees, 31% were non-Hispanic White and 60% were non-Hispanic Black, 69% were female. Baseline mean (SD) office BP was 143 (16) / 79 (11) mm Hg and their baseline mean home BP was 141 (17) / 83 (14) mm Hg. At the 3-month follow up, mean home BP had declined to 130 (15) / 78 (12) mm Hg; p<0.001/<0.001. After a median follow up of 5 months, patients had received a median (IQR) of 6 (5 to 8) messages, and 75% were read. Blood pressure medication changes were common with 50 (71%) having at least one medication change (36% had antihypertensive therapy increased, 9% decreased, and 27% had both types of changes); median (IQR) number of medication changes was 1 (0-3). Conclusions: Among high-risk patients with uncontrolled office BP who received RPM and automated patient portal messaging, medication adjustments were common and home BP was lower at 3 months. Patient and clinician feedback will be used to inform improvements. Future work should consider whether RPM with automated messaging is an effective solution to maintain and improve BP while not over-burdening the health system.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-03-11
DOI
https://doi.org/10.1161/cir.151.suppl_1.012
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 012: Pilot Testing of Hypertension Management Support with EHR-Integrated Telemonitoring and Automated Patient Messaging for Patients at High Risk from Hypertension

Hironori Sato, Yaw Peprah, Stephen D. Persell, Lucia C. Petito et al.
Circulation
Artificial Intelligence in Healthcare
article

Abstract 012: Pilot Testing of Hypertension Management Support with EHR-Integrated Telemonitoring and Automated Patient Messaging for Patients at High Risk from Hypertension

Hironori Sato, Yaw Peprah, Stephen D. Persell, Lucia C. Petito, Ji Young Lee
article en

Abstract

Introduction: Remote patient monitoring (RPM) of blood pressure (BP) can help support hypertension management. To improve the timely interpretation and action based on home BP values, automated messages providing clinical guidance could be delivered to patients based on the results of their home BP monitoring and used to encourage monitoring when additional information is needed for decision making. We hypothesized that among patients with elevated office BP at increased risk from hypertension due to coexisting cardiovascular disease, renal disease or diabetes mellitus, RPM with automated patient messages delivered through an EHR portal would lead to antihypertensive medication adjustments and improved home BP values. Methods: This was a pilot test of RPM with automated messages to patients sent in response to home BP values. Seven clinicians (5 primary care physicians and two nephrologists) reviewed lists of their patients with qualifying comorbidities whose most recent two office BP were ≥140/90 mm Hg (n = 285). Standardized home BP readings supervised by study staff were taken at baseline and 3 months, with follow up at 9 months planned. Antihypertensive medication changes, number of messages sent, and number read by the patient were assessed from EHR data. Results: Clinicians gave approval for 82% (n = 234) of eligible patients to be contacted, of whom 30% (n=70) enrolled. Of enrollees, 31% were non-Hispanic White and 60% were non-Hispanic Black, 69% were female. Baseline mean (SD) office BP was 143 (16) / 79 (11) mm Hg and their baseline mean home BP was 141 (17) / 83 (14) mm Hg. At the 3-month follow up, mean home BP had declined to 130 (15) / 78 (12) mm Hg; p<0.001/<0.001. After a median follow up of 5 months, patients had received a median (IQR) of 6 (5 to 8) messages, and 75% were read. Blood pressure medication changes were common with 50 (71%) having at least one medication change (36% had antihypertensive therapy increased, 9% decreased, and 27% had both types of changes); median (IQR) number of medication changes was 1 (0-3). Conclusions: Among high-risk patients with uncontrolled office BP who received RPM and automated patient portal messaging, medication adjustments were common and home BP was lower at 3 months. Patient and clinician feedback will be used to inform improvements. Future work should consider whether RPM with automated messaging is an effective solution to maintain and improve BP while not over-burdening the health system.

CirculationVol. 151(Suppl_1)
Northwestern University (US), Omron (Japan) (JP)
Openalex Percentile: Top 91%
Artificial Intelligence in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.