The impact of the COVID-19 pandemic on transfers between long-term care and emergency departments across Alberta
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) was overwhelmingly impacted by COVID-19 and unnecessary transfer to emergency departments (ED) can have negative health outcomes. This study aimed to explore how the COVID-19 pandemic impacted LTC to ED transfers and hospitalizations, utilization of community paramedics and facilitated conversations between LTC and ED physicians during the first four waves of the pandemic in Alberta, Canada. METHODS: In this retrospective population-based study, administrative databases were linked to identify episodes of care for LTC residents who resided in facilities in Alberta, Canada. This study included data from January 1, 2018 to December 31, 2021 to capture outcomes prior to the onset of the pandemic and across the first four waves. Individuals were included if they visited an emergency department, received care from a community paramedic or whose care involved a facilitated conversation between LTC and ED physicians during this time period. RESULTS: Transfers to ED and hospitalizations from LTC have been gradually declining since 2018 with a sharp decline seen during wave 1 of the pandemic that was greatest in the lowest-priority triage classification (CTAS 5). Community paramedic visits were highest during the first two waves of the pandemic before declining in subsequent waves; facilitated calls between LTC and ED physicians increased during the waves. CONCLUSIONS: There was a reduction in number of transfers from LTC to EDs and in hospitalizations during the first four waves of the pandemic. This was supported by increased conversations between LTC and ED physicians, but was not associated with increased community paramedic visits. Additional work is needed to explore how programs such as community paramedics and facilitated conversations between LTC and ED providers can help to reduce unnecessary transfers to hospital.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".