@Article{info:doi/10.2196/64933,
author="Wu, Jennifer J
and Graham, Ross
and {\c{C}}elebi, Julie
and Fraser, Kevin
and Gin, Geneen T
and Dang, Laurel
and Hatamy, Esmatullah
and Walker, Amanda
and Barbato, Courtney
and Lunde, Ottar
and Coles, Lisa
and Agnihotri, Parag
and Morn, Cassandra
and Tai-Seale, Ming",
title="Factors Influencing Primary Care Physicians' Intent to Refer Patients With Hypertension to a Digital Remote Blood Pressure Monitoring Program: Mixed Methods Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="24",
volume="27",
pages="e64933",
keywords="digital health; primary care; electronic health records; referral; hypertension; remote monitoring; remote blood pressure; digital technology; mobile phone; mixed method; quantitative analysis; linear regression; clinical information",
abstract="Background: Primary care physicians' (PCP) referral rates to digital health programs are highly variable. This study explores whether knowledge of the digital remote blood pressure monitoring (RBPM) program and information on referral patterns influence PCPs' intention to refer patients. Objective: This study aims to examine the relationship between PCPs' knowledge of the digital RBPM program and information on their own prior referral rates versus their own with their peers' referral rates and their likelihood to refer patients to the digital RBPM program. Methods: This is a mixed methods study integrating quantitative analysis of electronic health record data regarding the frequency of PCPs' referrals of patients with hypertension to a digital health program and quantitative and qualitative analyses of survey data about PCPs' knowledge of the program and their intention to refer patients. PCPs responded to a clinical vignette featuring an eligible patient. They were randomized to either receive their own referral rate or their own plus their peers' referral rate. They were assessed on their intent to refer eligible future patients. Descriptive and multivariable linear regression analyses examined participant characteristics and the factors associated with their intent to refer patients. Narrative reasons for their intention to refer were thematically analyzed. Results: Of the 242 eligible PCPs invited to participate, 31{\%} (n=70) responded to the survey. From electronic health record data, the mean referral rate of patients per PCP was 11.80{\%} (SD 13.30{\%}). The mean self-reported knowledge of the digital health program was 6.47 (SD 1.81). The mean likelihood of referring an eligible patient (on a scale of 0 to 10, with 0 being not at all, and 10 being definitely) based on a vignette was 8.54 (SD 2.12). The own referral data group's mean likelihood to refer was 8.91 (SD 1.28), whereas the own plus peer prior referral data group was 8.35 (SD 2.19). Regression analyses suggested the intention to refer the vignette patient was significantly associated with their knowledge (coefficient 0.46, 95{\%} CI 0.20-0.73; P<.001), whereas the intention to refer future patients was significantly associated with their intent to refer the patient in the vignette (coefficient 0.62, 95{\%} CI 0.46-0.78; P<.001). No evidence of association was found on receiving own plus peer referral data compared with own referral data and intent to refer future patients (coefficient 0.23, 95{\%} CI --0.43 to 0.89; P=.48). Conclusions: Physicians' intention to refer patients to a novel digital health program can be extrapolated by examining their intention to refer an eligible patient portrayed in a vignette, which was found to be significantly influenced by their knowledge of the program. Future efforts should engage PCPs to better inform them so that more patients can benefit from the digital health program. ",
issn="1438-8871",
doi="10.2196/64933",
url="https://www.jmir.org/2025/1/e64933",
url="https://doi.org/10.2196/64933",
url="http://www.ncbi.nlm.nih.gov/pubmed/40126550"
}