Abstract 19519: Closing the Cardiac Intensive Care Unit: Cost-Savings Associated with a “Closed” versus “Open” Model of Critical Care Delivery
Bibliographic record
Abstract
Introduction: A recent AHA scientific statement highlighted the evolving complexity of critical care delivery for cardiac patients, and the emerging need for novel staffing models. In this document, a “closed” unit structure - in which a dedicated intensive care team treats all admitted patients - was specifically advocated. However, in light of escalating critical care costs within US hospitals, there is a pressing need to better understand the financial impact of different care platforms. Methods: In July 2013, our academic cardiac intensive care unit (CICU) was transitioned from an “open” to a “closed” model of care. In a before-and-after study design, consecutive admission records were reviewed from Aug 2012-Dec 2012 (“open” unit) and from Aug 2013-Dec 2013 (“closed” unit). Routinely collected financial and demographic data were examined, and the impact of case-mix index (CMI) on cost was evaluated. Results: In the “open” and “closed” models, there were 333 patient-visits accounting for 1,891 patient-days and 397 visits accounting for 2,558 patient-days, respectively. While demographics, payor mix, and fixed vs. variable cost distribution were unchanged (Table), the total cost-per-patient and cost-per-patient-day were lower within the “closed” CICU ($8,676 vs. $10,118 and $1,346 vs. $1,782, respectively) despite a greater average CMI (4.6 vs. 3.6). Total and 30d CICU readmission rates were also lower in the “closed” unit (Table). Readmissions in the "closed" unit resulted in greater cost-per-patient-day than new admits ($1,576 vs. $1,339). Conclusions: A “closed” CICU staffing model is associated with lower health care costs. This may be partly explained by lower CICU recidivism, but likely is multifactorial. Additional study will focus on the influence of resource use, critical care delivery to key sub-populations, and the development of effective strategies for further cost containment.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".