COVID–19 driven decline in emergency visits: Has it continued, is it permanent, and what does it mean for emergency physicians?
Bibliographic record
Abstract
INTRODUCTION: Hospital-based emergency departments have been a sustained source of overall hospital utilization in the United States. In 2019, an estimated 150 million hospital-based emergency department (ED) visits occurred in the United States, up from 90 million in 1993, 108 million in 2000 and 137 million in 2015. This study analyzes hospital ED visit registration data pre and post to the COVID-19 pandemic describe the impact of on hospital ED utilization and to assess long-term implications of COVID and other factors on the utilization of hospital-based emergency services. METHODS: We analyze real-time hospital ED visit registration data from a large sample of US hospitals to document changes in ED visits from January 2020 through March 2022 relative to 2019 (pre-COVID baseline) to describe the impact of the COVID-19 pandemic on EDs and assess long-term implications. RESULTS: Our data show an initial steep reduction in ED visits during the first half of 2020 (compared to 2019 levels) with rebounding occurring in 2021, but never reaching pre-pandemic levels. Overall, ED visit volumes across the study states declined in each year since 2019: 2020 declined by -18%, 2021 by -10% and the first quarter of 2022 is -12% below 2019 levels. CONCLUSIONS: There is a wide range of potential long-term implications of the observed reduction in the demand for hospital-based emergency services not only for emergency physicians, but for hospitals, health plans and consumers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".