Analyzing effects of providing performance feedback at ward rounds on guideline adherence - the importance of feedback usage analysis and statistical control charts.
Bibliographic record
Abstract
OBJECTIVE: Feedback to clinicians on their past performance is often aimed at increasing adherence to guidelines. We investigate how various analytical approaches influence the interpretation of adherence data. The analytical approaches vary in considering the actual or the intended use of the feedback, and whether outcomes are inspected over time. MATERIAL AND METHODS: At base line, a computerized decision support system was employed at the ICU bedside to increase adherence to a mechanical ventilation strategy. We intervened by providing feedback about adherence to the guideline at the daily ward rounds. The outcome measure was the percentage of ventilation time (VT) in excess of the guideline's recommendation. Actual usage of the feedback was logged and data analysis was carried out using two approaches: classical statistics, and statistical process control (SPC) that inspect progress of an outcome over time. DESIGN: Prospective, before/after study. RESULTS: The classical analysis stated that the percentage of ventilation time in excess of the guideline's recommendation decreased significantly due to the feedback (5% reduction, p < 0.001). When SPC analysis of the outcome was applied, the effect was deemed not significant. When the actual delivery of feedback over time was also included it showed that the experiment does not allow for conclusive results. CONCLUSIONS: The concluded effect of providing feedback on adherence to a guideline depends on whether the actual usage pattern of the feedback and the inspection of the outcome over time are considered. Future evaluative studies should report on usage patterns and progression of outcomes over time.
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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 | 0.127 | 0.400 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".