Quality Improvement in Critical Care: Selection and Development of Quality Indicators
Bibliographic record
Abstract
Background. Caring for critically ill patients is complex and resource intensive. An approach to monitor and compare the function of different intensive care units (ICUs) is needed to optimize outcomes for patients and the health system as a whole. Objective. To develop and implement quality indicators for comparing ICU characteristics and performance within and between ICUs and regions over time. Methods. Canadian jurisdictions with established ICU clinical databases were invited to participate in an iterative series of face-to-face meetings, teleconferences, and web conferences. Eighteen adult intensive care units across 14 hospitals and 5 provinces participated in the process. Results. Six domains of ICU function were identified: safe, timely, efficient, effective, patient/family satisfaction, and staff work life. Detailed operational definitions were developed for 22 quality indicators. The feasibility was demonstrated with the collection of 3.5 years of data. Statistical process control charts and graphs of composite measures were used for data display and comparisons. Medical and nursing leaders as well as administrators found the system to be an improvement over prior methods. Conclusions. Our process resulted in the selection and development of 22 indicators representing 6 domains of ICU function. We have demonstrated the feasibility of such a reporting system. This type of reporting system will demonstrate variation between units and jurisdictions to help identify and prioritize improvement efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.179 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".