JMIR Publications

ABSTRACT

Background:

NYU Langone Health (NYULH) operates one of the largest remote patient monitoring (RPM) programs in the United States. Its hypertension management initiative (NYULH RPM HTN) supports approximately 4,500 patients monthly and captured over 100,000 remote blood pressure (BP) readings in 2024. Despite its benefits, the program faces real-world challenges, including patient disengagement, device usability issues, and clinician burden from high data volume. Generative AI (GenAI), particularly large language models (LLMs), offers opportunities to enhance patient engagement and streamline clinical workflows through personalized conversational interfaces such as chatbots and its data summarization capabilities.

Objective:

To explore the feasibility of using GenAI to support RPM, we developed the AI Brain, an electronic health record (EHR)–integrated GenAI layer to support RPM for hypertension management. AI Brain includes a patient-facing agent, chatbot designed to support engagement and blood pressure (BP) adherence, as well as a clinician-facing agent that generates smart content for EHR documentation and drafts patient messages. This study was conducted at NYULH, an academic medical center, providing a unique setting to evaluate the tool within a large-scale hypertension RPM program and to assess its impact on patient engagement, data interpretation, and clinical workflow efficiency.

Methods:

Our multidisciplinary team—comprising researchers, software engineers/architects, UX designers, and physicians—developed the AI Brain using a user-centered design approach and agile software development methods. We established patient and clinician advisory committees and conducted workshops during the formative phase to understand workflows and co-design solutions in collaboration with stakeholders. This was followed by a software development cycle that engaged advisory committee members at each stage to ensure the tool met user needs. Implementation considerations included usability, data privacy, clinical integration, and alignment with existing RPM processes.

Results:

The evaluation of the AI Brain demonstrated feasibility for integration into an established RPM infrastructure. Early observations suggest that the patient-facing agent showed potential to address common engagement barriers, including missed blood pressure submissions and device-related challenges. The clinician-facing agent supported care teams by summarizing key patient trends and reducing manual data review burden. Moreover, structured survey results indicated positive acceptability and perceived usefulness of GenAI-generated content. Security evaluations further demonstrated robust safeguards and reliable system performance.

Conclusions:

GenAI represents a promising approach to enhancing RPM, as demonstrated by its evaluation and adaptation within the NYULH hypertension management program. We described our development process and showed that, based on our evaluation, thoughtfully designed and integrated GenAI tools may help bridge gaps in patient workflows in terms of engagement and adherence as well as support clinical workflow to reduce data analysis and data summarization. Further evaluation is needed to assess long-term clinical outcomes, patient trust, and scalability in real-world healthcare settings.