Patient Portal Adoption Barriers in a Safety-Net Clinic: A Mixed-Methods Study With National Comparison
Year: 2026
Type: article
Study design: Observational Study
Abstract: INTRODUCTION: Patient portals are central to health care communication, yet disparities in adoption persist among underserved populations. Most implementation strategies draw on general population research, but adoption mechanisms may differ substantially in safety-net settings. METHODS: This mixed-methods cross-sectional study examined portal adoption barriers at a student-run free clinic serving predominantly uninsured, Spanish-speaking adults (n = 112 patients, N = 42 practitioners), with comparison to the Health Information National Trends Survey 6 (n = 5232). Exploratory cluster analysis identified patient engagement patterns. Semistructured interviews (n = 11) contextualized quantitative findings. Logistic regression models compared behavioral predictors across settings. RESULTS: Portal adoption was 22.3% locally vs 66.1% nationally. Cluster analysis identified 4 engagement patterns reflecting distinct barriers, including patients with high health confidence but low information access. Qualitative interviews revealed system usability and trust barriers rather than health literacy deficits. A logistic regression model using behavioral predictors performed well nationally (area under the curve = 0.78) but poorly locally (area under the curve = 0.37), suggesting different adoption mechanisms in safety-net settings. Only 44.3% of practitioners found the portal easy to use. CONCLUSIONS: Equitable digital health implementation requires community-specific barrier assessment rather than reliance on generalized national models. System-level usability and trust barriers, rather than individual behavioral deficits, may drive low portal adoption in underserved populations.
Source: The Permanente Journal
DOI: 10.7812/tpp/26.098
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7170650854
