Improving cardiac operating room to intensive care unit handover using a standardised handover process
Why this work is in the frame
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Bibliographic record
Abstract
Handovers from the cardiovascular operating room (CVOR) to the cardiovascular intensive care unit (CVICU) are complex processes involving the transfer of information, equipment and responsibility, at a time when the patient is most vulnerable. This transfer is typically variable in structure, content and execution. This variability can lead to the omission and miscommunication of critical information leading to patient harm. We set out to improve the quality of patient handover from the CVOR to the CVICU by introducing a standardised handover protocol. This study is an interventional time-series study over a 4-month period at an adult cardiac surgery centre. A standardised handover protocol was developed using quality improvement methodologies. The protocol included a handover content checklist and introduction of a formal 'sterile cockpit' timeout. Implementation of the protocol was refined using monthly iterative Plan-Do-Study-Act. The primary outcome was the quality of handovers, measured by a Handover Score, comprising handover content, teamwork and patient care planning indicators. Secondary outcomes included handover duration, adherence to the standardised handover protocol and handover team satisfaction surveys. 37 handovers were observed (6 pre intervention and 31 post intervention). The mean handover score increased from 6.5 to 14.0 (maximum 18 points). Specific improvements included fewer handover interruptions and more frequent postoperative patient care planning. Average handover duration increased slightly from 2:40 to 2:57 min. Caregivers noted improvements in teamwork, content received and patient care planning. The majority (>95%) agreed that the intervention was a valuable addition to the CVOR to CVICU handover process. Implementation of a standardised handover protocol for postcardiac surgery patients was associated with fewer interruptions during handover, more reliable transfer of critical content and improved patient care planning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it