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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".