Improving Communication Between Nurses and Resident Physicians: A 3-Year Quality Improvement Project
Bibliographic record
Abstract
Breakdown in communication is a predictor of both nursing and surgical errors. In a 2013 survey at our institution, staff on the general surgery unit identified nurse-resident communication as the most important issue related to patient safety. The general surgery Comprehensive Unit-based Safety Program sought to improve nurse-resident communication through a 3-year quality improvement initiative. A multidisciplinary working group conducted a root-cause analysis and developed initiatives addressing priority issues in nurse-resident communication. Two main interventions were developed: structured face-to-face interaction at discharge rounds and notebooks to transfer nonurgent messages. Compliance was evaluated. The primary outcomes of percieved communication and collaboration were assessed using a validated survey distributed to residents and unit nurses before the intervention, 9 months after, and 2.5 years after the intervention. The interventions were associated with improvements in perceived communication and team function. Survey scores, on average, were significant higher at 9 months postintervention and remained significant compared with preintervention after 2.5 years (from 57% to 74%, P = .01, then 72%, P = .02, among residents; and from 63% to 80%, P = .01, then 77% among nurses). Our framework and initiatives addressing nurse-resident communication may be useful for other teams interested in addressing this critical patient safety issue.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".