Evidence map›Paper›PMID 35136335›Full record

ArticleFederal practitioner : for the health care professionals of the VA, DoD, and PHS2021

Improving Unadjusted and Adjusted Mortality With an Early Warning Sepsis System in the Emergency Department and Inpatient Wards.

Justin Iannello, Nicole Maltese

Abstract read
In one paragraph

Article in Federal practitioner : for the health care professionals of the VA, DoD, and PHS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Justin IannelloVeterans Health Administration Sierra Pacific Network (VISN 21).
Nicole MalteseNorth Florida/South Georgia Veterans Health System.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMortality 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.

methodsThe 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.

resultsAt 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.

conclusionsEducation 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.

Identifiers

PMID35136335
PMCPMC8815614

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.