Reducing sepsis-related unplanned 30-day readmissions at a hospital-based skilled nursing facility
Bibliographic record
Abstract
Background: Sepsis is a common and costly medical emergency, often leading to unplanned readmissions. The purpose of this quality improvement project is to integrate staff education, every 4 hours vital signs monitoring guided by sepsis screening score, and structured response via a process map to reduce unplanned 30-day readmission rate by 25% from baseline at a hospital-based skilled nursing facility (HBSNF).Methods: This project was conducted at an 18-bed HBSNF. Prior to implementing this project, all registered nurses and patient care assistants received education on sepsis. Registered nurses were also trained in the proper use of Nursing Sepsis Management Order Set and How to Respond: A Patient with Suspected Sepsis Process Map. From September 1 to November 30, 2020, the project gradually increased vital signs monitoring frequency from every 12 hours to every 4 hours based on patients’ sepsis risk stratified by sepsis screening score in 3 phases. Systemic Inflammatory Response Syndrome criteria was used to identify sepsis-related unplanned readmissions.Results: Overall, the 3-month vital signs monitoring compliance rate was 96% (5019/5223). The sepsis-related unplanned 30-day readmission rate was reduced from baseline 47% (17/36) to 21% (4/19) at the end of this project, about a 55% decrease from baseline.Conclusions: The combination of an evidence-based electronic surveillance system and change in management strategies significantly reduced sepsis-related unplanned 30-day readmissions at this HBSNF. Dissemination of these innovations could improve sepsis management in other HBSNFs and positively impact patients’ health outcomes and healthcare costs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".