Expanding the scope of Critical Care Rapid Response Teams: a feasible approach to identify adverse events. A prospective observational cohort
Bibliographic record
Abstract
INTRODUCTION: Adverse events (AEs) affect 3-12% of hospitalised patients. These are estimates from a labour-intensive chart review process,which is not feasible outside research. Clinical deterioration on the wards triggers a rapid response teams (RRTs) consult and can be used to identify an AE prospectively. OBJECTIVES: To demonstrate the feasibility of using RRT to detect AEs and compare this methodology to the rates reported using an electronic safety reporting system. METHODS: Prospective observational cohort of RRT consults. Three independent physicians reviewed all cases for the occurrence of an AE and its preventability. We summarise AEs as rates per 1000 patient-days, and compared the rates between RRT and the safety reporting system using a Poisson model. RESULTS: There were 8713 hospital admissions, with 531 RRT consults and 247 (2.8%) cases included. Forty-four (17.8%) and 35 cases (14.2%) were judged as AEs and preventable AEs, respectively. RRT identified 0.52 AE/1000 patient-days, compared with 0.21 AE/1000 patient-days detected through the electronic safety reporting system (rate ratio 2.4, 95% CI 1.4 to 4.2, p=0.0014). Patients in surgical wards had more AEs (0.83/1000 vs 0.36/1000, p<0.01) and preventable AEs (0.70 vs 0.21, p<0.01) than patients in medical wards. Agreement for AE (κ 0.46, 95% CI 0.39 to 0.53) and preventable AE (κ 0.47, 95% CI 0.40 to 0.53) was moderate among reviewers. CONCLUSIONS: Reviewing RRT consults identified a high proportion of AEs and preventable AEs. This methodology detected twice as many AEs as the hospital's safety reporting system. RRT clinicians provide a complementary and more sensitive mechanism than traditional safety reporting systems to identify possible AEs in hospitals.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".