Video Remote Interpreting Technology in Health Care: Cross-Sectional Study of Deaf Patients’ Experiences
Bibliographic record
Abstract
BACKGROUND: The advent of new rehabilitation and assistive technologies has led to the creation of video remote interpreting (VRI) as an accessible communication technology for deaf patients. Although there has been a rapid growth in the use of VRI technology by health care providers, there is scant published information on VRI users and their satisfaction. Current, timely data are needed to understand deaf patients' use and satisfaction with the quality of VRI technology in health care settings. OBJECTIVE: This study aimed to investigate the national trends of deaf patients' satisfaction with the quality of video remote interpreting (VRI) in health settings and recommend actions to improve VRI quality and deaf patients' satisfaction with VRI in health care settings. METHODS: Secondary data related to deaf adults' experiences of using VRI service in a medical setting were obtained from the Health Information National Trends Survey in American Sign Language, which was administered to a US sample of deaf adults between 2016 and 2018. RESULTS: Among our VRI users (N=555, all in the United States) who answered questions about VRI usage in health between 2016 and 2018, only 41% were satisfied with the quality of the VRI technology service. Respondents with fewer years of education or those who were male were more likely to rate the VRI quality as acceptable. After adjusting for covariates in a binary regression analysis, deaf patients' self-reported interference (ie, VRI interpreter's interference with disclosure of health information) increased patient dissatisfaction with the quality of VRI technology service by three-fold. CONCLUSIONS: To increase satisfaction with VRI technology service in health care and rehabilitation settings among deaf patients, special attention needs to be given to video technology, as the use of sign language requires high-fidelity video for optimal communication between the interpreter and patient. To promote the willingness to disclose medical information through VRI among deaf patients, the interpreter must be highly skilled in both expressive and receptive communication and have the requisite background in medicine and rehabilitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".