Improving Unadjusted and Adjusted Mortality With an Early Warning Sepsis System in the Emergency Department and Inpatient Wards
Bibliographic record
Abstract
BACKGROUND: Mortality reduction has been a major focus of improvement for health care systems. Although several studies have noted improved sepsis-related mortality with the use of electronic health record (EHR) systems, there are no known published early warning sepsis systems using the Veterans Health Administration (VHA) EHR system. METHODS: The Malcom Randall Veterans Affairs Medical Center (MRVAMC), a large academic 1a VHA facility within the North Florida/South Georgia Veterans Health System (NF/SGVHS), was identified as having opportunities for improvement related to inpatient mortality outcomes. Sepsis was discovered as the primary contributor to inpatient mortality for MRVAMC's acute level of care (LOC). Education along with implementation of an early warning sepsis system (EWSS) was subsequently integrated in the VHA EHR known as the Veterans Information Systems and Technology Architecture/ Computerized Patient Record System (VistA/CPRS) at NF/SGVHS, which applied a combination of informatics solutions within a Lean Six Sigma quality improvement framework. RESULTS: At MRVAMC, there was an observed decrease in the number of inpatient deaths for the acute LOC from a high of 48 in fiscal year (FY) 2017, quarter 3 to a low of 27 in FY 2019, quarter 4. This resulted in as large of an improvement as a 44% reduction in unadjusted mortality with education and implementation of an EWSS from FYs 2017 to 2019. Additionally, the MRVAMC acute LOC risk-adjusted mortality (standardized mortality ratio) improved from > 1.0 to < 1.0, demonstrating fewer inpatient mortalities than predicted from FYs 2017 to 2019. CONCLUSIONS: Education along with the possible implementation of an EWSS within the VHA EHR was associated with improvement in unadjusted and adjusted inpatient mortality at MRVAMC. This may be an effective approach for patients with sepsis.
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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 | 0.000 | 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".