Patient Satisfaction With Telehealth Visits in Rural Compared With Urban Communities: Single-Center Study
Bibliographic record
Abstract
Abstract Background 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. Objective This study aimed to compare satisfaction with telehealth visits between patients living in rural and urban communities. Methods A telephone survey was developed and administered to hepatology patients seen at outpatient clinics from March 2020 through March 2021. 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. Results Of 400 patients, 164 (41%) completed the survey. Compared with urban patients, rural patients had twice the transportation time to clinic (mean 59, SD 35 vs mean 30, SD 15 min) and were more likely to cancel due to transportation issues (21/48, 46% vs 15/116, 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 or microphone (35/48, 75% vs 29/116, 25%) and less comfort with their devices (26/48, 54% vs 10/116, 9%). Overall, urban patients were more likely to prefer telehealth (adjusted odds ratio 5.20, 95% CI 2.15‐13.7) and were more satisfied with telehealth vs in-person visits than rural patients (72/116, 62.1% vs 10/48, 20.8%). Conclusions Rural patients reported more technical challenges with telehealth and more transportation issues than urban patients but favored in-person hepatology visits. Urban patients were more satisfied with telehealth visits compared with in-person visits. Research is needed to improve telehealth delivery and satisfaction for rural 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".