Enablers for Remote Monitoring Programs for Cardiac Conditions: Lessons From the COVID-19 Pandemic
Bibliographic record
Abstract
Throughout the COVID-19 pandemic, Canada’s health care system has experienced a dramatic shift toward virtual care. Patients with cardiac conditions are at an increased risk of adverse outcomes from COVID-19 infections, and for many, remote monitoring has been viewed as a safer form of health care delivery. This Policy Insight summarizes key facilitators and barriers to the implementation and expansion of remote monitoring programs for patients with cardiac conditions based on lessons learned during the COVID-19 pandemic. These include: Technology adoption — Health care decision-makers should be aware of barriers to technology adoption, including both patient and clinician hesitation and varying levels of health and digital literacy. Developing an effective education program to train staff and patients can help overcome technological barriers to adoption. Program adaptation — Transitioning programs to remote monitoring requires the consideration of a programs’ operational elements, such as eligibility requirements, onboarding and assessments, appointment logistics, program equipment, roles and responsibilities of clinicians and staff, health human resource planning, and data infrastructure and management. Regulatory and legislative changes — Policies that support enhanced interprovincial licensure and reimbursement for remote monitoring services are examples of policy changes that would foster a regulatory environment conducive to remote monitoring programs. While significant government investment has enabled the transition to virtual care (including remote monitoring), continued coordination between governments is needed to enhance the adoption of virtual care within Canadian health systems. Further research is needed on the cost-effectiveness and clinical appropriateness of remote monitoring across cardiac conditions. This analysis can support health system decision-makers in determining when and how to apply remote monitoring services safely and effectively.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".