Chinese Americans’ Use of Patient Portal Systems: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Electronic patient portals are increasingly used in health care systems as communication and information-sharing tools and show promise in addressing health care access, quality, and outcomes. However, limited research exists on portal use patterns and practices among diverse patient populations, resulting in the lack of culturally and contextually tailored portal systems for these patients. OBJECTIVE: This study aimed to summarize existing evidence on the access and use patterns, barriers, and facilitators of patient portals among Chinese Americans, who represent a growing patient population in the United States with unique health care and health technology needs. METHODS: The authors conducted a literature search using the PRISMA Protocol for Scoping Reviews (Preferred Reporting Items for Systematic Reviews and Meta-Analyses-ScR) for extracting articles published in major databases (MEDLINE, Embase, and PsycINFO) on patient portals and Chinese Americans. Authors independently reviewed the papers during initial screening and full-text review. The studies were analyzed and coded for the study method type, sample population, and main outcomes of interest. RESULTS: In total, 17 articles were selected for inclusion in the review. The included articles were heterogenous and varied in their study aims, methodologies, sample populations, and outcomes. Major findings identified from the articles include variable patterns of portal access and use among Chinese Americans compared to other racial or ethnic groups, with limited evidence on the specific barriers and facilitators for this group; a preference for cross-sectional quantitative tools such as patient surveys and electronic health record-based data over qualitative or other methodologies; and a pattern of aggregating Chinese American-related data into a larger Asian or Asian American designation. CONCLUSIONS: There is limited research evaluating the use patterns, experiences, and needs of Chinese Americans who access and use patient portal systems. Existing research is heterogeneous, largely cross-sectional, and does not disaggregate Chinese Americans from larger Asian demographics. Future research should be devoted to the specific portal use patterns, preferences, and needs of Chinese Americans to help ensure contextually appropriate and acceptable design and implementation of these digital health tools.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".