The Impact of Checklists on Inpatient Safety Outcomes: A Systematic Review of Randomized Controlled Trials
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Systematic reviews of non-randomized controlled trials (RCTs) suggest that using a checklist results in fewer medical errors and adverse events, but these evaluations are at risk of bias. OBJECTIVE: To conduct a systematic review of RCTs of checklists to determine their effectiveness in improving patient safety outcomes in hospitalized patients. METHODS: Ovid EMBASE, Ovid MEDLINE, PubMed, and the Cochrane Central Register of Controlled Trials were searched from inception until December 8, 2016. The search was restricted to RCTs. Included studies reported patient safety outcomes of a checklist intervention. Data extracted included the study characteristics, setting, population, intervention, outcomes measures, and sample size. MEASUREMENTS AND MAIN RESULTS: 11,225 citations were identified, of which 9 (16,987 patients) satisfied the inclusion criteria. Citations reported evaluations of checklists designed to improve surgical safety, prescription of medications, heart failure management, pain control, infection control precautions, and physician handover. Studies reported significant reductions in postoperative complications and medication-related problems and improved compliance with evidence-based prescribing of medications, infection control precautions, and patient handover procedures. 30-day mortality was reported in 3 studies and was significantly lower among patients allocated to the checklist group (odds ratio 0.60, 95% confidence interval, 0.41-0.89, 𝑃 = 0.01, I² = 0.0%, 𝑃 = 0.573). Methodological quality of the studies was moderate. CONCLUSION: A small number of citations report RCT evaluations of the impact of checklists on patient safety. There is an urgent need for high-quality evaluations of the effectiveness of patient safety checklists in inpatient healthcare settings to substantiate their perceived benefits.
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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 | 0.083 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.051 | 0.009 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it