Impact of Telehealth on Medication Adherence in Chronic Gastrointestinal Diseases
Bibliographic record
Abstract
Abstract Background With the COVID-19 pandemic, the demand and availability of telehealth in outpatient care has had exponential growth. Although use of telehealth has been studied and validated for various medical specialties, relatively few studies have looked at its role in gastroenterology. Aim To assess effectiveness of telehealth medicine in gastroenterology by comparing medication adherence rate for patients seen with telehealth and traditional in-person appointment for various gastrointestinal conditions. Methods Retrospective chart review of patients seen in outpatient gastroenterology clinic was performed to identify patients who were given prescription to fill either through telehealth or in-person appointment. By using provincial pharmacy database, we determined the prescription fill rate. Results A total of 206 patients were identified who were provided new prescriptions or prescription renewal at their gastroenterology clinic visit. One hundred and three patients were seen through in-person visit during pre-pandemic period, and 103 patients were seen through telehealth appointment during COVID-19 pandemic. The mean age of patients was 49.2 years (55% female). On average, patients had 4.7 previous visits with their gastroenterologists before their visit. IBD management was the most common reason for visits (37.9% and 46.6% in telehealth and in-person groups, respectively). Prescription fill rate for patients seen through telehealth was 92.2% compared to 81.6% for the in-person group (OR: 2.69, 95% CI: 1.12–6.45; P = 0.023). Conclusions Medication adherence rate for telehealth visits was higher than for in-patient visits. These findings suggest that telehealth can be an effective method of care delivery, especially for patients with chronic gastrointestinal conditions like IBD.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".