A PICU patient safety checklist: rate of utilization and impact on patient care
Bibliographic record
Abstract
OBJECTIVE: In healthcare, checklists help to ensure patients receive evidence-based, safe care. Since 2007, we have used a bedside checklist in our PICU to facilitate daily discussion of care-related questions at each bedside. The primary objective of this study was to assess compliance with checklist use and to assess how often individual checklist elements affected patient management. A secondary objective was to determine whether patient and unit factors (severity of illness, unit census, weekday vs. weekend, admitting diagnosis group) influenced checklist use. DESIGN: This was a prospective observational study. A research assistant attended daily bedside rounds to collect data at each eligible patient encounter. SETTING: The study was conducted in the Children's Hospital of Eastern Ontario (CHEO) PICU, a 12-bed cardiac and medical-surgical unit. PARTICIPANTS: Included all patients admitted to the PICU prior to 6 am and who were not being discharged that day. INTERVENTION: A bedside rounds checklist. MAIN OUTCOME MEASURES: Included compliance and whether the checklist affected the patient's management plan. RESULTS: A total of 148 encounters were collected on 28 days between September 2013 and February 2014. Compliance with the checklist was 89.2% (132/148; 95% CI 83.2-93.2%) and was not influenced by admitting diagnosis group, patient census, severity of patient's conditions or weekday/weekend status. The checklist affected the patient management plan 52.6% of the time (69/132; 95% CI 44.2-61%). CONCLUSIONS: Our study found high rates of compliance with an established checklist that has been in use in the PICU since 2007. Checklist use frequently resulted in a change in the patient management plan.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".