Developing and Evaluating a Bundled Digital Tool to Improve Complex Care and Self-Management of Patients With Inflammatory Bowel Disease: Protocol for a Hybrid Effectiveness-Implementation Study
Bibliographic record
Abstract
Background: Individuals with inflammatory bowel disease (IBD) require comprehensive care to address the physical and psychosocial burden of their disease. The demand for IBD care often exceeds availability, resulting in delayed access and suboptimal management. As a result, patients with IBD are required to self-manage significant aspects of their disease between appointments with their medical team. Digital self-management tools may help address this gap by empowering patients to be more engaged in managing their disease, potentially improving outcomes and reducing the strain on the health care system. Objective: This study aimed to design, implement, and evaluate a bundled digital health tool, MyIBDToolkit, with the overarching goal of improving the quality of care and self-management for patients with IBD in Alberta, Canada. Methods: A bundled digital health tool, MyIBDToolkit, will be integrated into our provincial electronic health record system to ensure broad accessibility and continuity of care. We will use a type 2 hybrid effectiveness-implementation design to evaluate both the clinical impact and real-world integration of the toolkit. We will assess effectiveness through changes in key outcomes such as health care utilization (eg, emergency visits, hospitalizations), disease burden on patients (eg, quality of life, symptom control), and burden on the health care system. These outcomes will be measured using comprehensive health care administrative data. A dual-comparison approach will be used: a within-subject comparison of health care utilization and disease burden before and after implementation of the MyIBDToolkit, and a between-group comparison of outcomes among toolkit users versus nonusers. To evaluate implementation success, we will examine reach (ie, number of patients and providers using the tool), fidelity to the planned timeline, sustained use over time, and factors influencing adoption and maintenance. Our goal is to reach 10,000 patients across Alberta, Canada, within three years. Results: We received funding for this project in January 2023. In preparation for the pilot launch, we have identified key stakeholders, including patients, health care providers and, administrators, and developed strategies to assess their readiness for MyIBDToolkit. We are also collecting mixed-methods data from patients to explore potential barriers and facilitators to using MyIBDToolkit. The first phase of MyIBDToolkit was launched in October 2024. Conclusions: MyIBDToolkit represents a scalable and patient-centered approach to the self-management of IBD care. By empowering patients to self-manage their disease between health care visits, we aim to reduce the burden of IBD on patients, providers, and the health care system. By evaluating the effectiveness and the implementation of the MyIBDToolkit, we aim to generate actionable and sustainable improvements to IBD care in Alberta.
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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 | 0.082 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".