Effects of increasing compliance with minimal sedation on duration of mechanical ventilation: a quality improvement intervention
Bibliographic record
Abstract
Abstract Introduction In the past two decades, healthcare adopted industrial strategies for process measurement and control. In the industry model, care is taken to avoid minimal deviations from a standard. In healthcare there is scarce data to support that a similar strategy can lead to better outcomes. Briefly, when compliance is high, further attempts to improve uptake of a process are seldom made. Our intensive care unit (ICU) improved the compliance with minimizing sedation from a high baseline of 80.4% (95% CI: 66.9 to 90.2) to 96.2% (95% CI: 95.2 to 97.0) 12 months after a quality improvement initiative. We sought to measure whether this minute improvement in compliance led to a reduction in duration of mechanical ventilation. Methods We collected data on compliance with the process during 12 months. A trained data collector abstracted data from charts every other day. Our database contains data for length of mechanical ventilation, mortality, type of admission, and acute physiology and chronic health evaluation (APACHE) II scores for the 12 months before and after the process improvement. To control for secular trends we used an interrupted-time series with adjustment for auto-correlation. We calculated the expected length of mechanical ventilation on each month by the end of the intervention period, and calculated the fitted value for the post-intervention months. Results We included 1556 patients. There was an immediate effect of the intervention (regression coefficient = -0.129, P value < 0.001) and the secular trend was a determinant of length of mechanical ventilation (regression coefficient = 0.010, P value = 0.004). The trend post-intervention was not significant (regression coefficient = 0.004, P value = 0.380). The relative change in the length of mechanical ventilation was 14.5% (IQR 13.8% to 15.8%) and the total expected decrease in mechanical ventilation days was 502.7 days (95% CI 300.9 to 729.1) over one year. Conclusions In a system already working at high levels of compliance, outcomes can still be improved. Our intervention was successful in reducing the length of mechanical ventilation. ICUs should have a process of quality assurance in place to provide constant monitoring of key quality of care processes and correct deviations from the proposed standard.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".