Trend of mortality and length of stay in the emergency department following implementation of a centralized sepsis alert system
Bibliographic record
Abstract
Introduction Sepsis alerts based on laboratory and vital sign criteria were found insufficient to improve patient outcomes. While most early sepsis alerts were implemented into smaller scale operating systems, a centralized new approach may provide more benefits, overcoming alert fatigue, improving deployment of staff and resources, and optimizing the overall management of sepsis. The objective of the study was to assess mortality and length of stay (LOS) trends in emergency department (ED) patients, following the implementation of a centralized and automated sepsis alert system. Methods The automated sepsis alert system was implemented in 2021 as part of a hospital-wide command and control center. Administrative data from the years 2018 to 2021 were collected. Data included ED visits, in-hospital mortality, triage levels, LOS, and the Canadian Triage and Acuity Scale (CTAS). Results Mortality rate for patients classified as CTAS I triage level was the lowest in 2021, after the implementation of the automated sepsis alert system, compared to 2020, 2019, and 2018 ( p < 0.001). The Kaplan–Meier survival curve revealed that for patients classified as CTAS I triage level, the probability of survival was the highest in 2021, after implementation of the sepsis alert algorithm, compared to previous years (Log Rank, Mantel–Cox, χ²=29.742, p < 0.001). No significant differences in survival rate were observed for other triage levels. Conclusion Implementing an automated sepsis alert system as part of a command center operation significantly improves mortality rate associated with LOS in the ED for patients in the highest triage level. These findings suggest that a centralized early sepsis alert system has the potential to improve patient outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".