Bringing Care Close to Home: Remote Management of Heart Failure In Partnership with Indigenous Communities In Northern Ontario, Canada
Bibliographic record
Abstract
Background: The Weeneebayko Area Health Authority (WAHA) is a regional, community-based Indigenous health authority in Northern Ontario, Canada. From September 2022 to March 2023, the WAHA and University Health Network engaged in a partnership that designed a collaborative model of care to address inequities in cardiology specialist access in Northern Ontario. This model implemented a digital therapeutic for heart failure, (the Medly program) and in-person cardiology clinics in the region. Methods: A WAHA-based Medly program clinical coordinator worked closely with the University Health Network team to deliver care and support patient self-management of HF. The use and effectiveness of the Medly program were tracked through app usage and rules-based algorithm alerts, based on patient self-reported data. Distribution of relevant equipment (a scale, a blood pressure cuff, and a mobile device) for the Medly program was recorded. Surveys to assess patient and provider satisfaction with the Medly program also were administered. A retrospective chart audit of electronic medical records and administrative databases was conducted. Results: A total of 33 patients in the WAHA were enrolled in the Medly program during a 7-month period, surpassing the enrollment goal of 25 patients. A total of 93% of eligible patients were on optimized guideline-directed medical therapy or were being titrated for it. Of 15 surveyed patients, 100% agreed or strongly agreed that the Medly program facilitated delivery of care close to home, and 86% of surveyed clinicians (n = 7) agreed or strongly agreed that the Medly program addresses a gap in available care in the region. Conclusions: The implementation of the Medly program in partnership with the WAHA has demonstrated success in terms of the volume of referrals, the quality of care, adherence to evidence-based best-practice guidelines, and satisfaction with the program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".