A83 IMPACT OF TELEHEALTH ON MEDICATION ADHERENCE IN GASTROENTEROLOGY CHRONIC DISEASE MANAGEMENT
Bibliographic record
Abstract
Abstract Background With the COVID-19 pandemic, the demand and availability of telehealth in outpatient care has increased. Although use of telehealth has been studied and validated for various medical specialties, relatively few studies have looked at its role in gastroenterology despite burden of chronic diseases such as inflammatory bowel disease (IBD). Aims 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 GI conditions. Methods Retrospective chart analysis 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 241 patients were identified who were provided prescriptions during visit with their gastroenterologists. 128 patients were seen through in-person visit during pre-pandemic period. 113 patients were seen through telehealth appointment during COVID pandemic. The mean age of patients in telehealth cohort was 42 years (57% male). On average patients had 10 prior visits with their gastroenterologists before index appointment, used for adherence assessment. 92% of patients were seen in follow-up, while 8% were seen in initial consultation. The majority of the patients in the telehealth cohort had IBD (89%), while the remaining 11% had various diagnoses, including functional GI disorder, gastroesophageal reflux disease, viral hepatitis, or hepatobiliary disorders. Biologic therapy was the most commonly prescribed medication (66.4%). 45 patients were provided either new medication or dose change, and 68 patients had prescription refill to continue their current medications. It took a mean of 18 days (SD = 16.2) for patients to fill their prescriptions. Prescription fill rate for patients seen through telehealth and in-person visit were 98.2% and 89.1% (P = 0.004) respectively. Patients seen through telehealth were 6.8 times more likely to fill their prescriptions compared to the in-person counterparts (OR 6.82, CI 1.51 – 30.68, P = 0.004). When we compared adherence rate while excluding biologic therapies, the prescription fill rate was 94.7% in telehealth group and 81.4% in in-person group (OR 4.11, CI 0.88 – 19.27, P = 0.056). Due to high level of adherence, statistical analysis comparing adherent and non-adherent groups was performed but yielded insignificant results. Conclusions Medication adherence rate for patients seen through telehealth was higher compared to patients seen through in-patient visit in this study. Telehealth is a viable alternative for outpatient care especially for patients with chronic GI conditions such as IBD. Funding Agencies None
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".