Hospital Ward Adaptation During the COVID-19 Pandemic: A National Survey of Academic Medical Centers
Bibliographic record
Abstract
IMPORTANCE: Although intensive care unit (ICU) adaptations to the coronavirus disease of 2019 (COVID-19) pandemic have received substantial attention , most patients hospitalized with COVID-19 have been in general medical units. OBJECTIVE: To characterize inpatient adaptations to care for non-ICU COVID-19 patients. DESIGN: Cross-sectional survey. SETTING: A network of 72 hospital medicine groups at US academic centers. MAIN OUTCOME MEASURES: COVID-19 testing, approaches to personal protective equipment (PPE), and features of respiratory isolation units (RIUs). RESULTS: Fifty-one of 72 sites responded (71%) between April 3 and April 5, 2020. At the time of our survey, only 15 (30%) reported COVID-19 test results being available in less than 6 hours. Half of sites with PPE data available reported PPE stockpiles of 2 weeks or less. Nearly all sites (90%) reported implementation of RIUs. RIUs primarily utilized attending physicians, with few incorporating residents and none incorporating students. Isolation and room-entry policies focused on grouping care activities and utilizing technology (such as video visits) to communicate with and evaluate patients. The vast majority of sites reported decreases in frequency of in-room encounters across provider or team types. Forty-six percent of respondents reported initially unrecognized non-COVID-19 diagnoses in patients admitted for COVID-19 evaluation; a similar number reported delayed identification of COVID-19 in patients admitted for other reasons. CONCLUSION: The COVID-19 pandemic has required medical wards to rapidly adapt with expanding use of RIUs and use of technology emerging as critical approaches. Reports of unrecognized or delayed diagnoses highlight how such adaptations may produce potential adverse effects on care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".