Changes in Medical Errors after Implementation of a Handoff Program
Bibliographic record
Abstract
BACKGROUND: Miscommunications are a leading cause of serious medical errors. Data from multicenter studies assessing programs designed to improve handoff of information about patient care are lacking. METHODS: We conducted a prospective intervention study of a resident handoff-improvement program in nine hospitals, measuring rates of medical errors, preventable adverse events, and miscommunications, as well as resident workflow. The intervention included a mnemonic to standardize oral and written handoffs, handoff and communication training, a faculty development and observation program, and a sustainability campaign. Error rates were measured through active surveillance. Handoffs were assessed by means of evaluation of printed handoff documents and audio recordings. Workflow was assessed through time-motion observations. The primary outcome had two components: medical errors and preventable adverse events. RESULTS: In 10,740 patient admissions, the medical-error rate decreased by 23% from the preintervention period to the postintervention period (24.5 vs. 18.8 per 100 admissions, P<0.001), and the rate of preventable adverse events decreased by 30% (4.7 vs. 3.3 events per 100 admissions, P<0.001). The rate of nonpreventable adverse events did not change significantly (3.0 and 2.8 events per 100 admissions, P=0.79). Site-level analyses showed significant error reductions at six of nine sites. Across sites, significant increases were observed in the inclusion of all prespecified key elements in written documents and oral communication during handoff (nine written and five oral elements; P<0.001 for all 14 comparisons). There were no significant changes from the preintervention period to the postintervention period in the duration of oral handoffs (2.4 and 2.5 minutes per patient, respectively; P=0.55) or in resident workflow, including patient-family contact and computer time. CONCLUSIONS: Implementation of the handoff program was associated with reductions in medical errors and in preventable adverse events and with improvements in communication, without a negative effect on workflow. (Funded by the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services, and others.).
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".