Reinventing Inflammatory Bowel Disease (IBD) Clinical Trial Recruitment Using Novel Digital Medicine Tools
Bibliographic record
Abstract
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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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 | 0.034 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".