A83 IMPACT OF TELEHEALTH ON MEDICATION ADHERENCE IN GASTROENTEROLOGY CHRONIC DISEASE MANAGEMENT
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».