Abstract 12585: The Impact of Remote Patient Monitoring and Digital Therapeutics on Major Clinical Events and Costs in Heart Failure Patients: Early Experience in the Quebec Public Healthcare System
Bibliographic record
Abstract
Introduction: There is increasing use of digital health solutions in heart failure (HF), but few studies have demonstrated its impact on public healthcare systems in North America. The Continuum project developed a mobile app for remote patient monitoring (RPM) and a digital therapeutics (DTx) solution aimed at assisting clinicians in medication optimization and preventing hospitalizations among HF outpatients. Research Question: Are RPM and DTx cost-effective for care of outpatients with HF? Methods: A 3-month randomized controlled trial for HF outpatients at risk of hospitalisation evaluated the Continuum program versus standard of care alone. This 12-week program included: 1. A self-care app via smartphone or tablet where patients entered vital signs, weight, and HF symptoms; 2. Remote monitoring of these data by clinical nurses; 3. DTx automated medication suggestions sent to the treating medical team; and 4. HF educational modules for patients. Results: A total of 171 patients were included. Preliminary results are available for the first 63 patients that completed the study, 32 intervention (INT) and 31 control (CTRL). Patients were similar in age (70±12 vs 69±13y), NYHA class (II 81 vs 87%), patients with ejection fraction <40% (56 vs 48%) and comorbidities such as diabetes (38 vs 45%; INT vs CTRL). The number of emergency room (ER) visits and/or hospitalisations (all cause) per patient was 0.19±0.47 for the INT group and 0.55±0.89 for the CTRL one (P=0.05). Survival analysis (Figure) showed a trend in favor of the INT group (95 days (CI95% 87-104) vs 78 days (CI95% 68-89); P=0.08). The total cost of healthcare consumption (hospitalizations + ER visits) in the INT group was 134,088 Canadian dollars vs 174,924 in the CTRL group (+30%). Conclusions: Our preliminary results show potential benefits of a mobile app incorporating self-care, remote monitoring, and digital therapeutics in preventing major events in HF outpatients at a reduced cost for the healthcare system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".