Introduction: The Centers for Medicare & Medicaid Services (CMS) designed Hospital Quality Initiatives (HQI) to assure delivery of quality health care for institutions receiving Medicare payments. Like many teaching institutions, the SEP-1 compliance rates at McLaren Oakland in Pontiac fluctuated monthly and were not achieving institutional target expectations.
Methods: The project team designed a Sepsis Macro and a Sepsis Order Set in the electronic medical record system. The project team also implemented an educational initiative targeted at emergency medicine resident and attending physicians. The educational initiative instructed emergency medicine resident and attending physicians in the metrics measured in the SEP-1 bundle as well as how to properly use the newly designed Sepsis Macro and Sepsis Order Set.
Results: After implementation of the Sepsis Macro and Sepsis Order Set, the overall compliance with the SEP-1 bundle improved from 57% to 62%, above national averages and at the institutional target expectations. However, there were not statistically significant differences (p = 0.562) between the compliance rate before and after program implementation (Pre = 57% (SD = 0.27); 95% CI: 0.29 - 0.85); Post= 62% (SD = 0.11); 95% CI: 0.55 - 0.70). After program implementation the SEP-1 compliance rate was met in 82% of the months in comparison with 50% of the months in the pre-intervention (p = 0.28).
Conclusions: Although not achieving statistical significance, this intervention demonstrated that simple, cost-effective measures of education and standardization in documentation and order entry in EMR's can improve clinically significant compliance to CMS HQI metrics in community-based teaching institutions.
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http://dx.doi.org/10.51894/001c.37707 | DOI Listing |
Zhonghua Jie He He Hu Xi Za Zhi
October 2024
Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Air Force Medical University, Xi'an 710032, China.
Microvasc Res
November 2024
Université de Lyon, APCSe Agressions Pulmonaires et Circulatoires dans le Sepsis-, UP 2021.A101, VetAgro Sup, 1 Avenue Bourgelat, 69280 Marcy-l'Étoile, France. Electronic address:
Systemic inflammation and hemodynamic or microvascular alterations are a hallmark of sepsis and play a role in organs hypoperfusion and dysfunction. Pimobendan, an inodilator agent, could be an interesting option for inotropic support and microcirculation preservation during shock. The objectives of this study were to evaluate effect of pimobendan on cytokine and nitric oxide (NO) release and investigate whether changes of macro and microcirculation parameters are associated with the release of cytokines and NO in pigs sepsis model.
View Article and Find Full Text PDFAsian J Surg
July 2024
Department of Intensive Care Unit, Affiliated Hospital of Hebei University, Baoding City, 071000, China. Electronic address:
Minerva Anestesiol
September 2024
Department of Intensive Care, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles (ULB), Brussels, Belgium.
Brain dysfunction is a frequent complication of sepsis. Most likely, sepsis-associated brain dysfunction (SABD) results from the interaction between multiple factors: neurodegeneration due to microglial activation, altered neurotransmission, neuroinflammation and impairment of cerebral macro- and microcirculation. Altered brain perfusion might results from several mechanism: global or regional alterations in cerebral blood flow (CBF); reduced cerebral perfusion pressure - which is the driving force propelling blood through cerebral blood vessels - due to systemic hypotension; global or regional vasoconstriction; dysfunction of the intrinsic regulatory mechanisms of CBF, such as cerebral autoregulation and cerebrovascular reactivity; endothelial and blood-brain barrier dysfunction; autonomic nervous system dysfunction and metabolic uncoupling.
View Article and Find Full Text PDFJMIR AI
January 2024
Vital Software, Inc, Claymont, DE, United States.
Background: Despite its high lethality, sepsis can be difficult to detect on initial presentation to the emergency department (ED). Machine learning-based tools may provide avenues for earlier detection and lifesaving intervention.
Objective: The study aimed to predict sepsis at the time of ED triage using natural language processing of nursing triage notes and available clinical data.
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