Reinventing Inflammatory Bowel Disease (IBD) Clinical Trial Recruitment Using Novel Digital Medicine Tools
Notice bibliographique
Résumé
Background: Issues with patient recruitment and enrollment are the primary barriers for missed clinical trial timelines; 8 out of 10 clinical trials are delayed or unable to be completed because of lack of timely patient recruitment. Current patient recruitment efforts are inefficient and time-consuming, since they are typically dependent on manually screening patients during face-to-face visits to the clinic or hospital. With the rapid development of digital communication platforms within health care and the broad consumer adoption of smartphones, there are increasing opportunities to overcome some of these barriers. These platforms have particularly great potential for research and clinical care of chronic conditions, such as inflammatory bowel disease (IBD), an often debilitating disease which currently affects over three million adults in the United States. Objective: To integrate and utilize a digital medicine platform to improve patient recruitment and enrollment processes in clinical trials. Methods: Patients enrolled in the Mount Sinai Crohn’s and Colitis Registry (MSCCR) were remotely approached about enrolling in a mindfulness study for IBD patients. A text-based clinical rules engine was used to inform registry patients about the trial and to allow patients to indicate interest in participating via text message. Eligible IBD patients were bulk “prescribed” a notification through RxHealth’s digital medicine platform, RxUniverse. Characteristics of the enrolled population, characteristics of patients who responded, and timeliness of responses were analyzed. Results: Of the 1364 patients in the MSCCR with available phone numbers, 270 patients affirmatively replied they wanted to participate in to the study. Patients who opted into receiving more information about the study were more likely to have inadequate control of their IBD (25.64% vs 18.97%; P<.05) and more likely to have a recent history of depression based on a validated patient health questionnaire (15.38% vs 8.4%; P<.05) than those who opted out. Furthermore, patients who opted in tended to be younger, were more likely to be female, and less likely to have ulcerative colitis, though these trends did not reach statistical significance. Patient race did not significantly differ between those who opted in and opted out. In terms of timeliness of response among those enrolled, the majority of patients responded within 2 hours of notification. Conclusions: Digital medicine software platforms can facilitate large-scale, lower-effort recruitment of eligible patients for clinical trials. Future research should be done to explore their expanded use for recruitment, patient education, and study data collection. Additional technologies such as patient-powered networks, social media, e-recruiting bots, and other remote engagement platforms can aid clinical trials by saving time and reducing costs of patient recruitment.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».