Evaluation of self-measured blood pressure monitoring in a southern rural West Virginia health system.

INTRODUCTION: At the time of the 2021 Behavioral Risk Factor Surveillance System survey, an estimated 32.3% of adults in the US and nearly half (43.4%, 776 000) of adults in West Virginia (WV) had hypertension. Further, the Interactive Atlas of Heart Disease and Stroke estimates an increase in the percentage of adults with hypertension in the US from 32.3% to 47.0%, with hypertension rates in WV rising as high as 58.7%, indicating a significant public health concern in the community. Hypertension increases the risk of several negative health outcomes, including heart disease and stroke, and leads to increased economic and chronic disease burden. Although certain unmodifiable factors (sex, age, race, ethnicity, and family history) increase the risk of developing hypertension, a healthy lifestyle - including a nutritious diet, maintaining a healthy weight, avoiding nicotine products, and participating in regular moderate physical activity - can decrease the risk of developing hypertension. Self-measured blood pressure (SMBP) monitoring, or home BP monitoring, when integrated with a provider's clinical management approach, is linked to improvements in BP management and control. This study represents a mid-point assessment of a remote SMBP monitoring program implemented by Cabin Creek Health Systems (CCHS), a federally qualified health center, and its impact on BP control. METHODS: CCHS implemented SMBP programming in March 2020 as one element of a developing comprehensive program aimed at reducing uncontrolled hypertension, and therefore chronic disease burden, in its service area and patient population. The project, funded by the Health Resources and Services Administration, continued to February 2023. This report represents a mid-point analysis and was based on the retrospective analysis of de-identified data collected for 234 patients to June 2022, who were assessed for changes in BP between the date of enrollment and the most recently available BP measurement. Patients were enrolled in the SMBP program if they exhibited current or previous indicators of uncontrolled hypertension (systolic ≥140 mmHg and/or diastolic ≥90 mmHg), at the discretion of their provider, and were equipped with an iBloodPressure cellular connected home BP monitoring system, manufactured by Smart Meter. Their BP readings were documented in the integration software TimeDoc Health and electronic health record athenahealth. RESULTS: At the time of enrollment, 201 (86.0%) patients had uncontrolled hypertension, with 116 (49.6%) patients having both uncontrolled systolic (≥140 mmHg) and diastolic (≥90 mmHg) values. At follow-up, the number of patients with uncontrolled hypertension decreased from 201 to 98 (41.9%), with only 36 (15.4%) patients having both uncontrolled systolic and diastolic values. Additionally, 26 (11.1%) patients were in hypertensive crisis at the time of enrollment, and no patients remained in crisis at the time of follow-up. The number of patients with BP values in the controlled range (systolic <140 mmHg and diastolic <90 mmHg) increased from 33 (14.1%) at enrollment to 136 (58.1%) at follow-up. Overall, there was a 44.0% increase in the number of patients with BP values in the controlled range at follow-up, and a concomitant 44.1% decrease in the number of patients in the uncontrolled range. These observations were consistent across multiple demographic indicators, including clinic location, three-digit zip code, and patient sex. CONCLUSION: Systematic implementation of remote BP monitoring, when integrated into clinician workflows, was associated with a substantial reduction in the number of patients with uncontrolled hypertension in this rural federally qualified health center. Further, CCHS was successful in implementing a remote SMBP monitoring program in a community challenged with transportation insecurity, and poor cellular and broadband access, of which lessons learned are applicable to other health systems interested in pursuing comparable efforts.

Using i2b2 to Bootstrap Rural Health Analytics and Learning Networks.

We demonstrate that the open-source i2b2 (Informatics for Integrating Biology and the Bedside) data model can be used to bootstrap rural health analytics and learning networks. These networks promote communication and research initiatives by providing the infrastructure necessary for sharing data and insights across a group of healthcare and research partners. Data integration remains a crucial challenge in connecting rural healthcare sites with a common data sharing and learning network due to the lack of interoperability and standards within electronic health records. The i2b2 data model acts as a point of convergence for disparate data from multiple healthcare sites. A consistent and natural data model for healthcare data is essential for overcoming integration issues, but challenges such as those caused by weak data standardization must still be addressed. We describe our experience in the context of building the West Virginia/Kentucky Health Analytics and Learning Network, a collaborative, multi-state effort connecting rural healthcare sites.