Assessing Disparities in Video-Telehealth Use and eHealth Literacy Among Hospitalized Patients: Cross-sectional Observational Study
Bibliographic record
Abstract
BACKGROUND: Medicare coverage for audio-only telehealth is slated to end this year after the public health emergency concludes. When the time comes, many patients may be unable to make the transition from audio-only to video telehealth due to digital inexperience. This study explores the second digital divide within video telehealth use, which is primarily characterized by skills and capabilities rather than access, by measuring eHealth literacy (eHL) and video capabilities in hospitalized patients. OBJECTIVE: The aim of this study is to evaluate video capabilities, eHealth literacy, and engagement with video telehealth among hospitalized patients. METHODS: The study design is a cross-sectional observational study of adult inpatients at the University of Chicago Medical Center. We assessed self-reported rates of audio versus video telehealth usage as well as the participants' self-reported willingness to use video telehealth for future health care visits. We used a multivariable binary logistic regression to determine the odds ratio for being unwilling to use video telehealth, adjusted for age, sex, race or ethnicity, educational level, eHL literacy scale (eHEALS), health literacy (brief health literacy screen), technology access, internet access, and video capability. RESULTS: Of the 297 enrolled participants, median age was 58 years, most (n=185, 62%) identified as Black, half (n=149, 50%) were female, one-quarter (n=66, 22%) lacked home internet access, and one-third (n=102, 34%) had inadequate eHL. CONCLUSIONS: Patients with low eHL reported greater participation in audio-only telehealth over video telehealth, of which the former may lose its flexible pandemic reimbursement policy. This may widen the existing health disparities as older adults and patients with low eHL face challenges in accessing video telehealth services. Low eHL is associated with lack of web-based skills, lower rates of video telehealth usage, and lower willingness to use video technology. The study results raise the question of how to improve video capability among patients who, despite having access to smartphones and laptops, face challenges in using telehealth optimally.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".