Barriers to Patient Portal Adoption Among a Bilingual Patient Population by Analysis of Survey Findings from English- and Spanish-Speaking Patients: Information Needs Study
Bibliographic record
Abstract
Background: Despite legislative action, pre-existing barriers continue to prevent patients from using patient portals. Patients, especially older people, people of color, and people with limited English proficiency continue to experience difficulty in adopting patient portals. Objective: The aim of this study was to advance understanding, explore willingness to adopt an electronic portal, and examine differences between language preferences. Methods: English- and Spanish-speaking patients (N=106) were surveyed from a community clinic regarding access to electronic devices and the internet, barriers to using a patient portal, willingness to adopt such a portal, preference mode of communication with health care providers, and preferred features in the current clinic's portal. Linear and logistic regressions were performed to predict the probability that patients would adopt the patient portal. Results: Only 65% (n=69)of participants said they envisioned themselves using a patient portal. English-speaking patients were more willing to exchange electronic information with their health care providers. Spanish-speaking patients reported language as a significant barrier to portal use. A logistic regression revealed that patients with more positive attitudes and higher perceived behavioral control are more likely to sign up and use the patient portal (Nagelkerke R2=.51, classification=90.8%, efficacy B=2.38, Wald-1=5.93, P=.02 and Exp[B]=12.44, attitude B=1.87, Wald=6.45, P=.01, Exp[B]=7.49). Conclusions: Understanding language preference differences while predicting portal use based on attitudes and perceptions empowers patients to have a more meaningful experience with their physician, potentially overcoming low health literacy-related barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".