Scalable Model for Delivery of Inpatient Palliative Care During a Pandemic
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, hospitals worldwide have reported large volumes of patients with refractory symptoms and a large number of deaths attributable to COVID-19. This has led to an increase in the demand for palliative care beyond what can be provided by most existing programs. We developed a scalable model to enable continued provision of high-quality palliative care during a pandemic for hospitals without a palliative care unit or existing dedicated palliative care beds. METHODS: A COVID-19 consultation service working group (CWG) was convened with stakeholders from palliative care, emergency medicine, critical care, and general internal medicine. The CWG connected with local palliative care teams to ensure a coordinated response, and developed a model to ensure high-quality palliative care provision. RESULTS: Our 3-step scalable model included: (1) consultant model enhanced by virtual care; (2) embedded model; and (3) cohorted end-of-life unit for COVID-19 positive patients. This approach was enabled through tools and resources to ensure specialist palliative care capacity and rapid upskilling of all clinicians to deliver basic palliative care. Enabling tools and resources included a triage tool for in-person versus virtual care, new medication order sets and guidelines to facilitate prescribing for common symptoms, and lead advance care planning and goals of care discussions. A redeployment plan of generalist physicians and psychiatrists was created to ensure seamless provision of serious illness care. CONCLUSION: This 3-step, scalable approach enables rapid upscaling of palliative care in collaboration with generalist physicians, and may be adapted for future pandemics or natural disasters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".