Trends in telehealth use among a cohort of rural patients during the COVID-19 pandemic
Bibliographic record
Abstract
Objective: Rural populations faced unique challenges to healthcare access during the COVID-19 pandemic. This analysis assesses trends in digital health technology use at the onset of the pandemic and describes digital health behaviors among a cohort of patients within a rural integrated healthcare network throughout the first 3 years of the pandemic. Methods: We used data from both the electronic health record (EHR) and a patient survey. EHR data was used to longitudinally assess change over time in patient portal use and telehealth visits. Survey responses were used to provide additional context. Results: Telehealth appointments peaked in the first quarter of 2020 at 28% of all office visits, before leveling off to 8-10% in 2022. Women and those younger than 65 were more likely to have participated in telehealth appointments. Active patient portal users increased from 34.1% in January 2019 to 63.7% in January 2022. There were no differences noted in portal use trends based on rurality. Conclusions: Our findings corroborate previous research, as well as add context regarding digital health technology use throughout the COVID pandemic in a rural patient population. Future research must focus on understanding constraints to digital health expansion in order to continue providing safe, equitable care.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".