A36 IMPACT OF DIGITAL HEALTH MONITORING IN THE MANAGEMENT OF INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Abstract Background Inflammatory bowel disease (IBD) affects over 270,000 Canadians and costs the healthcare system $1.28 billion dollars annually. With advancements in technology, a shift from the traditional ‘reactive’ approach to IBD management to a ‘proactive’ approach that integrates self-management strategies using digital health monitoring platforms could greatly benefit patient care. Aims The purpose of this study was to investigate the effect of implementing the IBD health monitoring platform, HealthPROMISE, in clinical practice and to evaluate whether its use leads to better quality of care, improved health outcomes, and reduce resource consumption in patients with IBD. Methods IBD patients were recruited in gastroenterology clinics and asked to install the HealthPROMISE application onto their smartphones. Patient satisfaction, quality of care, quality of life, patient symptoms, and resource utilization metrics were collected throughout the study and sent directly to their healthcare teams. Patients with abnormal symptom/short inflammatory bowel disease questionnaire (SIBDQ) scores were flagged for their physicians to follow up with. After one-year, patient outcome metrics were compared to baseline values. Results Overall, out of 59 patients enrolled in the study, 32 patients (54%) logged into the application at least once during the study period. The number of IBD-related ER visits/hospitalizations in the year of use compared to the prior year demonstrated a significant decrease from 25% of patients (8/32) to 3% (1/32) (p=0.03). Patients also reported an increase in their understanding of the nature/causes of their condition after using the application (p=0.026). No significant changes were observed in the number of quality indicators met (p = 0.67) or in SIBDQ scores (p=0.48). Conclusions Given the significant burden of IBD, there is a need to develop effective management strategies. This study demonstrated that digital health monitoring platforms may aid in reducing the number of ER visits and hospitalizations in IBD patients. Future studies evaluating acceptability and costs with a larger sample size would help determine the feasibility and generalizability of widely implementing mobile health applications in the management of IBD. Funding Agencies CCC
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".