The Impact of Halting Elective Admissions in Anticipation of a Demand Surge Due to the Coronavirus Pandemic (COVID-19)
Bibliographic record
Abstract
BACKGROUND: In anticipation of a demand surge for hospital beds attributed to the coronavirus pandemic (COVID-19) many US states have mandated that hospitals postpone elective admissions. OBJECTIVES: To estimate excess demand for hospital beds due to COVID-19, the net financial impact of eliminating elective admissions in order to meet demand, and to explore the scenario when demand remains below capacity. RESEARCH DESIGN: An economic simulation to estimate the net financial impact of halting elective admissions, combining epidemiological reports, the US Census, American Hospital Association Annual Survey, and the National Inpatient Sample. Deterministic sensitivity analyses explored the results while varying assumptions for demand and capacity. SUBJECTS: Inputs regarding disease prevalence and inpatient utilization were representative of the US population. Our base case relied on a hospital admission rate reported by the Center for Disease Control and Prevention of 137.6 per 100,000, with the highest rates in people aged 65 years and older (378.8 per 100,000) and 50-64 years (207.4 per 100,000). On average, elective admissions accounted for 20% of total hospital admissions, and the average rate of unoccupied beds across hospitals was 30%. MEASURES: Net financial impact of halting elective admissions. RESULTS: On average, hospitals COVID-19 demand for hospital bed-days fell well short of hospital capacity, resulting in a substantial financial loss. The net financial impact of a 90-day COVID surge on a hospital was only favorable under a narrow circumstance when capacity was filled by a high proportion of COVID-19 cases among hospitals with low rates of elective admissions. CONCLUSIONS: Hospitals that restricted elective care took on a substantial financial risk, potentially threatening viability. A sustainable public policy should therefore consider support to hospitals that responsibly served their communities through the crisis.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".