Physician and administrator experience of preparing to implement Ontario’s intensive care unit Triage Emergency Standard of Care during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
BACKGROUND: As the COVID-19 pandemic created a surge in demand for critical care resources, the province of Ontario, Canada, released the Adult Critical Care Clinical Emergency Standard of Care for Major Surge (Emergency Standard of Care [ESoC]), a triage framework to guide the allocation of critical care resources in the expectation that intensive care units would be overwhelmed. Our aim was to understand physicians' and administrators' experiences and perceptions of planning to implement the ESoC, and to identify ways to improve critical care triage processes for future pandemics. METHODS: We conducted semistructured qualitative interviews with critical care, emergency and internal medicine physicians, and hospital administrators from various Ontario health regions who were involved in their hospital's or region's ESoC implementation planning. Interviews were conducted virtually between April and October 2021. We analyzed the data using thematic analysis. RESULTS: We conducted interviews with 11 physicians and 10 hospital administrators representing 9 health regions. We identified 4 themes regarding participants' preparation to implement the ESoC: infrastructure to enable effective triage implementation; social, medical and political supports to enable effective triage implementation; moral dimensions of triage implementation; and communication of triage results. Participants outlined administrative and implementation-related improvements that could be provided at the provincial level, such as billing codes for ESoC. They also suggested improving ethical supports for the usability and quality of the ESoC (e.g., designating an ethicist in each region), and ways to improve the efficiency and usability of the tools for assessing short-term mortality risk (e.g., create information technology solutions such as a dashboard). INTERPRETATION: The implementation of a jurisdiction-level triage framework poses moral challenges for a health care system, but it also requires dedicated infrastructure, as well as institutional supports. Lessons learned from Ontario's process to prepare for ESoC implementation, as well as participants' suggestions, can be used for planning for current and future pandemics.
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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 | 0.001 | 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 | 0.002 |
| 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".