I-PASS Handoff Program: Use of a Campaign to Effect Transformational Change
Bibliographic record
Abstract
BACKGROUND: Behavior change is notoriously difficult to achieve within health care systems. Successful implementation of the I-PASS handoff bundle with subsequent decreases in medical errors and preventable adverse events represents an example of successful transformational change within academic medical centers. OBJECTIVE: We designed a campaign to support and enhance uptake of the I-PASS handoff bundle at 9 study sites from 2011 to 2013. METHODS: Following Kotter's model of transformational change, we established urgency using local data and institutional mandates, and site leaders built local guiding coalitions with institutional leaders, key faculty, and Chief Residents. We created and communicated our vision using a branded campaign and empowered others to act by soliciting and acting on feedback and supporting systems changes. Site leaders planned for and created short-term wins by recognizing residents who engaged with I-PASS, consolidated improvements, and institutionalized new approaches. RESULTS: Implementation of I-PASS was successful, with achievement of substantial improvements in rates of medical errors and preventable adverse events. Data from the initial I-PASS study have continued to drive a national campaign that has included national recognition by leaders in the field of patient safety and pediatrics. Momentum has increased significantly to support mentored implementation of the I-PASS handoff program at over 35 academic medical centers across North America. CONCLUSIONS: I-PASS provides an example of transformational change achieved through a combination of educational interventions and change management to address resistance/barriers, supported by a robust campaign. We encourage others in academic medicine to consider using change models, including campaigns, to support health care improvement programs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".