Telehealth Perceptions Among US Immigrant Patients: Cross-sectional Study Within an Academic Internal Medicine Practice
Bibliographic record
Abstract
Background The use of telemedicine has increased dramatically through the COVID-19 pandemic. While data are available about patient satisfaction with health care through telemedicine, little is known about the immigrant patient experience. Objective We investigated whether immigrant patients would prefer in-person visits and have higher ratings for interpersonal communication during in-person rather than telemedicine visits. We hoped to identify the reasons behind immigrant visit preferences and consider these reasons to guide suggestions for more equitable use of and access to visit options. Methods Overall, 270 patients including 122 immigrants and 148 nonimmigrants were seen by 4 internal medicine providers in either an in-person (n=132) or telemedicine (n=138) university practice setting. Immigrants were defined as having been born outside of the United States. Patients were queried between February and April 2021 using an adaptation of a previously validated patient satisfaction survey containing standard questions developed by the Consumer Assessment of Healthcare Providers and Systems Program. Patients seen via in-person visits completed a paper copy of the survey. The same survey was administered by a follow-up phone call for telemedicine visits. Patients surveyed spoke English, Spanish, or Arabic and were surveyed in their preferred language. For televisits, the same survey was read to the patient by a certified translator. The survey comprised 9 questions on a 5-point Likert scale assessing satisfaction under the categories of access to care, interpersonal interaction, quality of care, and next visit preference. An additional write-in question assessed reasons for subsequent visit type preferences. Survey question responses were compared with paired t tests. Results Across both immigrant and nonimmigrant patient populations, satisfaction with perceived quality of care was universally high regardless of visit type (televisits: P=.80 and P=.60; in-person: P=.76 and P=.37). During televisits, immigrants were more likely than nonimmigrants to feel that providers spent sufficient time with them (P<.001). Different perceptions were noted among nonimmigrant patients. Nonimmigrants tended to perceive more provider time during in-person visits (P=.006). When asked to comment on reasons behind subsequent visit preference, nonimmigrant patients prioritized convenience, whereas immigrants noted the telemedicine advantage of not having to navigate other office logistics. Conclusions While satisfaction was quite high for both telemedicine and in-person visits across immigrant and nonimmigrant populations, significant differences in patient priorities were identified. Immigrants found televisits desirable because they felt they spent more time with their providers and were able to avoid additional office logistics that are often challenging barriers for non-English speakers. This suggests opportunities to use information technology to provide cultural and language-appropriate information throughout the in-person and telemedicine visit experience of immigrants, such as assistance with call-in scheduling, appointment reminders, and portal access. A focus on diminishing these barriers will help reduce health care inequities among immigrant patients. Conflicts of Interest None declared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".