Patient Satisfaction With Telehealth Visits in Rural Compared With Urban Communities: Single-Center Study
Bibliographic record
Abstract
<sec> <title>UNSTRUCTURED</title> Background & Aims: Studies performed in urban communities with access to technology suggest high patient satisfaction with telehealth. While virtual visits can increase the reach of clinical practice in rural communities, technological barriers may reduce patient satisfaction. Our aim is to compare satisfaction with telehealth visits between patients living in rural and urban communities. Approach & Results: A telephone survey was developed and administered to hepatology patients seen at outpatient clinics from 3/20-3/21. Patient characteristics and survey responses were compared by urban and rural location as defined by the census tract based on ZIP code using univariable and multivariable logistic regression. Of 400 patients, 164 (41%) completed the survey. Compared to urban patients, rural patients had twice the transportation time to clinic (59 + 35 vs 39 + 37 minutes) and were more likely to cancel due to transportation issues (46% vs 13%). Rural patients reported less proficiency with technology and more technical difficulties, including an inability to log on to the portal or access the camera/microphone (75% vs 25%) and less comfort with their devices (54% vs 9%). Overall, urban patients were more likely to prefer telehealth (aOR: 5.20, 95% CI: 2.15-13.7), and were more satisfied with telehealth vs. in person visits than rural patients (62.1% vs. 20.8%). Conclusion: Rural patients reported more technical challenges to telehealth and more transportation issues than urban patients but favored in person hepatology visits. Urban patients were more satisfied with telehealth visits compared to in person visits. Research is needed to improve telehealth delivery and satisfaction for rural patients. </sec>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".