Optimizing ward rounds: systematic review and meta-analysis of interventions to enhance patient safety
Bibliographic record
Abstract
BACKGROUND: Poor quality ward rounds contribute to a large proportion of patient complications, delayed discharge, and increased hospital cost. This systematic review investigated all interventions aiming to improve patient and process-based outcomes in ward rounds. METHODS: This systematic review was prospectively registered in PROSPERO, the international prospective register of systematic reviews (CRD42023394325). MEDLINE, Embase, Emcare, and PsycInfo were searched for studies with interventions aiming to improve ward round processes or patient outcomes in hospital settings. Studies were excluded if there was no baseline comparator or they were not in the ward round setting. Interventions were coded as checklist interventions (that is electronic or paper-based pro formas, templates, and checklists), structure interventions (that is defined rules or protocol to guide or standardize conduct), or other interventions. Outcomes were assessed via meta-analyses using the I2 statistic, Cochran's Q P value, and random-effects models. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool for RCTs and the Newcastle-Ottawa scale for non-randomized studies. RESULTS: This review included 84 studies, from 18 countries, in 23 specialties, involving 43 570 patients. Checklist interventions significantly reduced ICU length of stay, improved overall documentation, and did not increase ward round duration. Structure interventions did not increase the time spent per patient or impact 30-day readmission rates or patient length of stay. CONCLUSION: This is the first systematic review with meta-analyses synthesizing the evidence of all ward round interventions targeted at improving patient and process outcomes. Results from this review should be used to inform guidelines for the 'ideal ward round'.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".