Quality and performance measures of strain on intensive care capacity: a protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: The matching of critical care service supply with demand is fundamental for the efficient delivery of advanced life support to patients in urgent need. Mismatch in this supply/demand relationship contributes to "intensive care unit (ICU) capacity strain," defined as a time-varying disruption in the ability of an ICU to provide well-timed and high-quality intensive care support to any and all patients who are or may become critically ill. ICU capacity strain leads to suboptimal quality of care and may directly contribute to heightened risk of adverse events, premature discharges, unplanned readmissions, and avoidable death. Unrelenting strain on ICU capacity contributes to inefficient health resource utilization and may negatively impact the satisfaction of patients, their families, and frontline providers. It is unknown how to optimally quantify the instantaneous and temporal "stress" an ICU experiences due to capacity strain. METHODS: We will perform a systematic review to identify, appraise, and evaluate quality and performance measures of strain on ICU capacity and their association with relevant patient-centered, ICU-level, and health system-level outcomes. Electronic databases (i.e., MEDLINE, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Web of Science, and the Agency of Healthcare Research and Quality (AHRQ) - National Quality Measures Clearinghouse (NQMC)) will be searched for original studies of measures of ICU capacity strain. Selected gray literature sources will be searched. Search themes will focus on intensive care, quality, operations management, and capacity. Analysis will be primarily narrative. Each identified measure will be defined, characterized, and evaluated using the criteria proposed by the US Strategic Framework Board for a National Quality Measurement and Reporting System (i.e., importance, scientific acceptability, usability, feasibility). DISCUSSION: Our systematic review will comprehensively identify, define, and evaluate quality and performance measures of ICU capacity strain. This is a necessary step towards understanding the impact of capacity strain on quality and performance in intensive care and to develop innovative interventions aimed to improve efficiency, avoid waste, and better anticipate impending capacity shortfalls. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42015017931.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".