Critical Care Organizations in Academic Medical Centers in North America
Bibliographic record
Abstract
OBJECTIVES: With the exception of a few single-center descriptive reports, data on critical care organizations are relatively sparse. The objectives of our study were to determine the structure, governance, and experience to date of established critical care organizations in North American academic medical centers. DESIGN: A 46-item survey questionnaire was electronically distributed using Survey Monkey to the leadership of 27 identified critical care organizations in the United States and Canada between September 2014 and February 2015. A critical care organization had to be headed by a physician and have primary governance over the majority, if not all, of the ICUs in the medical center. MEASUREMENTS AND MAIN RESULTS: We received 24 responses (89%). The majority of the critical care organizations (83%) were called departments, centers, systems, or operations committees. Approximately two thirds of respondents were from larger (> 500 beds) urban institutions, and nearly 80% were primary university medical centers. On average, there were six ICUs per academic medical center with a mean of four ICUs under critical care organization governance. In these ICUs, intensivists were present in-house 24/7 in 49%; advanced practice providers in 63%; hospitalists in 21%; and telemedicine coverage in 14%. Nearly 60% of respondents indicated that they had a separate hospital budget to support data management and reporting, oversight of their ICUs, and rapid response teams. The transition from the traditional model of ICUs within departmentally controlled services or divisions to a critical care organization was described as gradual in 50% and complete in only 25%. Nearly 90% indicated that their critical care organization governance structure was either moderately or highly effective; a similar number suggested that their critical care organizations were evolving with increasing domain and financial control of the ICUs at their respective institutions. CONCLUSIONS: Our survey of the very few critical care organizations in North American academic medical centers showed that the governance models of critical care organizations vary and continue to evolve. Additional studies are warranted to improve our understanding of the factors that can foster the growth of critical care organizations and how they can be effective.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".