Preliminary Performance on the New CMS Sepsis-1 National Quality Measure: Early Insights From the Emergency Quality Network (E-QUAL)
Abstract
Study objective We describe current hospital-level performance for the Centers for Medicare & Medicaid Services' Severe Sepsis/Septic Shock Early Management Bundle (SEP-1) quality measure and qualitatively assess emergency department (ED) sepsis quality improvement best practice implementation. Methods Using a standardized Web-based submission portal, we surveyed quality improvement data from volunteer hospital-based EDs participating in the Emergency Quality Network Sepsis Initiative. Each hospital submitted preliminary SEP-1 local chart review data, using existing Centers for Medicare & Medicaid Services definitions. We report descriptive statistics of SEP-1 data availability and performance. The primary outcome for this study was SEP-1 bundle compliance, defined as the proportion of all severe sepsis and septic shock cases receiving all required bundle elements, and secondary outcomes included conditional compliance on reported SEP-1 numerator components and ED implementation of sepsis quality improvement best practices. Results A total of 50 EDs participated in the survey; 74% were nonteaching sites and 26% were affiliated with academic centers. Of all participating EDs, 80% were in regions with relatively high population density. The mean hospital SEP-1 bundle compliance was 54% (interquartile range 30% to 75%). Bundle compliance improved during fiscal year 2016 from 39% to 57%. Broad variation existed for each bundle component, with intravenous fluid resuscitation and r…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In an emergency department network, mean SEP-1 compliance was approximately 54%, with substantial variation across bundle components, particularly intravenous fluid administration and repeat lactate measurement.
“In an emergency department network, mean compliance was approximately 54% with substantial variation across bundle components, particularly intravenous fluid administration, and repeat lactate measurement [16].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [16]
Individual SEP-1 elements can vary in feasibility, obscuring true performance.
“However, individual elements can vary in feasibility obscuring true performance [15,16].”
From Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis (2026) · cites this paper as [16]
Cites
Damiani, E., Donati, A., Serafini, G., Rinaldi, L., Adrario, E., Pelaia, P., Busani, S., & Girardis, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0125827 · Cited by 275 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Rhee, C., Wang, R., Zhang, Z., Fram, D., Kadri, S. S., & Klompas, M. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000003817 · Cited by 122 · see citing works
Evaluation of sepsis prediction models before onset of treatment
Kamran, F., Tjandra, D., Heiler, A., Virzi, J., Singh, K., King, J. E., Valley, T. S., & Wiens, J. (2024) · NEJM AI · doi:10.1056/aioa2300032 · Cited by 74 · see citing works
Does early follow-up improve the outcomes of sepsis survivors discharged to home health care?
Deb, P., Murtaugh, C. M., Bowles, K. H., Mikkelsen, M. E., Khajavi, H. N., Moore, S., Barrón, Y., & Feldman, P. H. (2019) · Medical Care · doi:10.1097/mlr.0000000000001152 · Cited by 55 · see citing works
Rhee, C., Brown, S. R., Jones, T. M., O’Brien, C., Pande, A., Hamad, Y., Bulger, A. L., Tobin, K. A., Massaro, A. F., Anderson, D. J., et al. (2018) · Infection Control & Hospital Epidemiology · doi:10.1017/ice.2018.134 · Cited by 51 · see citing works
Driving blind: Instituting SEP-1 without high quality outcomes data
Wang, J., Strich, J. R., Applefeld, W. N., Sun, J., Cui, X., Natanson, C., & Eichacker, P. Q. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd.2019.12.100 · Cited by 34 · see citing works
Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis
Nguyen, H. B., Krishtopaytis, E., Lopez, E., Farnoudi, N., Van, T., Kharalampova, V., & Coz Yataco, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15093477
“However, individual elements can vary in feasibility obscuring true performance [15,16].”
- Authors
- Venkatesh, A. K., Slesinger, T., Whittle, J., Osborn, T., Aaronson, E., Rothenberg, C., Tarrant, N., Goyal, P., Yealy, D. M., & Schuur, J. D.
- Affiliations
- Yale New Haven Hospital (US)
- Florida International University (US)
- Washington University in St. Louis (US)
- Massachusetts General Hospital (US)
- Yale University (US)
- American College of Emergency Physicians (US)
- University of Pittsburgh (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Venkatesh, Arjun K.
- ORCID
- Venue
- Annals of Emergency Medicine
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 71
- Issue
- 1
- Pages
- 10-15.e1
- ISSN
- 0196-0644 (ISSN-L)
- OpenAlex
- W2745025470
- Cited by
- 58
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 8 · 2021: 5 · 2020: 9 · 2019: 10 · 2018: 11 · 2017: 2
- FWCI
- 6.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Emergency Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (KL2TR001862)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Health Professions › Health Information Management › Healthcare Quality and Management
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Keywords
- sepsis, septic shock, Centers for Medicare and Medicaid Services, quality of care, emergency department, bundle compliance, intravenous fluid resuscitation, lactate monitoring, hospital-level variation
- MeSH terms
- Cross-Sectional Studies, Emergency Service, Hospital, Humans, Quality Assurance, Health Care, United States, Centers for Medicare and Medicaid Services, U.S., Practice Guidelines as Topic, Sepsis, Guideline Adherence, Quality Improvement, Patient Care Bundles
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
