Inpatient Telehealth Experience of Patients With Limited English Proficiency: Cross-sectional Survey and Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: Patients with limited English proficiency (LEP) are at a higher risk of poor health outcomes and are less likely to use telehealth than English-speaking patients. To date, there is no formal evaluation of inpatient (IP) telehealth user experience of patients and their families by language preference during visits with their clinicians. OBJECTIVE: This study aims to compare the experiences of English- and Spanish-speaking patients and their families using IP telehealth, as well as to evaluate the experience of Spanish interpreters providing services through IP telehealth. METHODS: We prospectively administered a survey to English- and Spanish-speaking patients and their families who used IP telehealth from October 1, 2020, to March 31, 2021. We performed semistructured phone interviews of hospital-based Spanish interpreters who provided services through IP telehealth. RESULTS: A total of 661 surveys were administered, with completion rates of 18% (112/621) in English and 62% (25/40) in Spanish. On a 10-point scale, the overall satisfaction of Spanish speakers (median 10, IQR 10-10) was higher than that of English speakers (median 9, IQR 8-10; P=.001). Both English- and Spanish-speaking patients used IP telehealth for visits with their primary IP care team, subspecialty consultants, and other clinicians. Hospital tablets were used more often than personal devices, and only English-speaking patients used personal laptops. Patients and their families encountered challenges with log-in, team coordination with multiple users, and equipment availability. Interpreters encountered challenges with audio and video quality, communication, safety, and Wi-Fi access. CONCLUSIONS: Both English- and Spanish-speaking patients reported high satisfaction using IP telehealth across multiple disciplines despite the workflow challenges identified by interpreters. Significant investment is needed to provide robust infrastructure to support use by all patients, especially the integration of multiple users to provide interpreter services for patients with LEP.
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 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".