Effect of implementation of a rapid response system on protocol adherence in a surgical ward
Bibliographic record
Abstract
Objective: To describe the implementation of a rapid response system and adherence to its afferent limb in order to identify key elements for improvement. Implementation: We developed a multifaceted implementation strategy to introduce the Rapid Response System (RRS) on a 60-bed surgical ward of a university hospital. The strategy included the use of clear objectives, key leaders, an early warning score (EWS) observation protocol and a two-tiered medical emergency team (MET) warning protocol, a 1-day training program including a before-after knowledge test, mandatory for nurses and optional for ward physicians, reminders and feedback. Study design and methods: We retrospectively analyzed a sample of 10,653 patient days and 101 medical records of patients with a serious adverse event (SAE). Outcome measures were EWS recording rates, the nurse to ward physician and the ward physician to the MET calling rates following abnormal EWS recordings, and the indicators triggering these calls. Results: EWS recordings were present in 90% of the day shifts, 88% of the evening shifts and 80% of the night shifts. EWSs were recorded at least once in 92/101 medical records in the three days before an SAE; in 91/101 records EWSs were abnormal at least once. In case of an abnormal score, the nurse called the ward physician once or more in 87% (79/91). After being called by the nurse, the ward physician called the MET once or more in 75% (59/79). However, in 18% (15/79) there was a delay of one or two days before the ward physician/MET was called. Overall, medical emergency team calls were absent or delayed in over 50%. Conclusions: After RRS implementation, recording of the EWS was high. Adequate warning in case of abnormal scores was suboptimal in nurses as well as ward physicians. Future implementation strategies should therefore be aimed at the interdisciplinary team.
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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".