Use of Structured Handoff Protocols for Intrahospital Within-Unit Transitions
Bibliographic record
Abstract
Objectives. To review the evidence from the past 10 years on the effectiveness of structured protocols for the handoff between clinicians of responsibility for a patient’s care on clinical safety outcomes. Methods. We searched PubMed, EMBASE, PSNet, CINAHL, and a narrowly focused search for unpublished reports from January 2013 to June/July 2024 for systematic reviews and primary studies of structured protocols for handoffs within the same inpatient unit (i.e., not transferring to a different level of care or a different institution) that reported patient clinical outcomes, such as medical errors, adverse events, medication errors, mortality, length of stay. Risk of bias was assessed with the Cochrane Risk of Bias Tool or a modification of the National Institutes of Health pre-post study tool, a narrative synthesis was performed, and certainty of evidence was assessed using criteria used by Making Healthcare Safer II and the National Academy of Medicine. Findings. We retrieved 789 citations, of which 16 articles were eligible for review (2 systematic reviews and 14 articles of 13 primary studies). Four studies were randomized controlled trials and the remainder were pre-post studies. Two studies were performed in Argentina, one each was performed in Taiwan, Canada and Germany, and the rest were performed in the United States. Six studies were single site studies, and the remainder were multisite. Almost all studies were conducted in academic teaching hospitals and assessed physician-to-physician handoffs. Two systematic reviews and two new original research studies (one a randomized controlled trial) provided low-certainty evidence that use of the SBAR tool (situation, background, assessment, recommendation) can improve patient safety clinical outcomes. Ten studies (of 9 implementations; 2 studies were randomized controlled trials) provided moderate-certainty evidence that the I-PASS tool (illness severity, patient summary, action list, situation awareness and contingency plans, and synthesis to receiver) can improve patient safety clinical outcomes. Many co-interventions and implementation strategies are used in conjunction with the I-PASS mnemonic. No multisite evidence was found for any other structured handoff tool. Conclusions. Use of the structured handoff tool I-PASS probably improves patient clinical outcomes and use of the SBAR tool may improve patient clinical outcomes, with I-PASS having a stronger certainty of evidence. Data come primarily from academic teaching hospitals, and the usefulness of any tool in nonacademic teaching settings is understudied.
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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 | 0.024 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".