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Record W4285739665 · doi:10.2196/36069

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

2022· article· en· W4285739665 on OpenAlexvenueno aff
Susan Levine, Richa Gupta, Kenda Alkwatli, Allaa Almoushref, Saira Cherian, Dominique Feterman Jimenez, Greishka Nicole Cordero Baez, Angela Hart, Clara Weinstock

Bibliographic record

VenueJMIR Human Factors · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthLikert scaleTelemedicineMedicineFamily medicineHealth careImmigrationPatient satisfactionCross-sectional studyCertificationSocial distancePhoneNursingPsychologyCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

BACKGROUND: The use of telemedicine has increased dramatically through the COVID-19 pandemic. Although data are available about patient satisfaction with telemedicine, little is known about immigrant patients' experience. OBJECTIVE: We sought to investigate patients' experiences with telehealth compared to in- person visits between immigrants and nonimmigrants. We wanted to identify and describe next visit preferences within the Farmington University of Connecticut Internal Medicine practice to ultimately guide suggestions for more equitable use and accessibility of visit options. METHODS: A total of 270 patients including 122 immigrants and 148 nonimmigrants were seen by 4 Internal Medicine providers in an in-person (n=132) or telemedicine (n=138) university practice setting. Patients were queried between February and April 2021, using an adaptation of a previously validated patient satisfaction survey that contained 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 consisted of 10 questions on a Likert scale of 1-5. Of them, 9 questions assessed patient satisfaction under the categories of access to care, interpersonal interaction, and quality of care. An additional question asked patients to describe and explain the reasons behind next visit preferences. Survey question responses were compared by paired t tests. RESULTS: Across both immigrant and nonimmigrant patient populations, satisfaction with perceived quality of care was high, regardless of visit type (P=.80, P=.60 for televisits and P=.76, P=.37 for in-person visits). During televisits, immigrants were more likely to feel 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 next televisit preference, nonimmigrant patients prioritized convenience, whereas immigrants noted not having to navigate office logistics. For those who chose in-person visits, both groups prioritized the need for a physical exam. CONCLUSIONS: Although satisfaction was 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 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 immigrants' in-person and telemedicine visit experience. A focus on diminishing these barriers will help reduce health care inequities among immigrant patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.057
GPT teacher head0.448
Teacher spread0.391 · 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 teacher head, not a consensus.

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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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