Using Health Care Resources Wisely After the COVID-19 Pandemic: Recommendations to Reduce Low-Value Care
Bibliographic record
Abstract
Low-value tests, treatments, and procedures are an important health care quality problem in Canada and across the world because they provide little clinical benefit, may be harmful for patients, and waste limited resources. Due to the COVID-19 pandemic, health care systems face increased challenges of limited resources, reduced capacity, and a growing backlog of surgeries and other procedures. The pandemic has compelled health care professionals to make challenging decisions to prioritize health care services while coping with increased demand. As Canada emerges from the pandemic and health care systems rebuild and begin to address the backlog of delayed or cancelled services, there is an imperative to introduce lasting changes to reduce low-value care and ensure high-quality care is available to everyone. To help inform efforts for using health care resources wisely and to support decision-making, CADTH and Choosing Wisely Canada convened a 10-member multi-disciplinary panel of clinicians, patient representatives, and health policy experts to review areas of low-value care that can be reduced or limited. This panel reviewed, deliberated, and prioritized 19 recommendations of the more than 400 Choosing Wisely Canada recommendations, the implementation of which can help ensure high-value care after the pandemic. Examples of the 19 recommendations include: Avoiding unnecessary transfers for patients in long-term care to hospitals unless there is an urgent medical need. Limiting blood tests and imaging unless required to answer a specific clinical question or guide treatment. Not transfusing red blood cells for hemodynamically stable patients in the intensive care unit. Not delaying palliative care for patients with serious illness because they are pursuing disease-directed treatment. Moreover, the panel’s discussion highlighted how the selected recommendations can advance key priorities, including improving health equity and access to care, appropriately using limited resources, emphasizing patient-focused care, and addressing challenges the pandemic has presented for long-term care.
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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 | 0.019 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".