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Record W4318822082 · doi:10.2196/39449

Telehealth Perceptions Among US Immigrant Patients: Cross-sectional Study Within an Academic Internal Medicine Practice

2023· article· en· W4318822082 on OpenAlexvenueno aff
Richa Gupta, Susan Levine, Kenda Alkwatli, Clara Weinstock, Alla Almoushref, Saira Cherian, Dominique Feterman Jimenez, Greishka Nicole Cordero Baez, Angela Hart

Bibliographic record

VenueIproceedings · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsTelemedicineTelehealthLikert scaleMedicineImmigrationSocial distanceFamily medicinePatient satisfactionHealth careInterpersonal communicationCertificationPreferencePhoneNursingPsychologyCoronavirus disease 2019 (COVID-19)Social psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.104
GPT teacher head0.492
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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