Sepsis is a leading cause of mortality and morbidity in hospitalised patients. The Centers for Medicare and Medicaid Services (CMS) mandated that US hospitals report sepsis bundle compliance rate as a quality process measure in October 2015. The specific aim of our study was to improve the CMS sepsis bundle compliance rate from 30% to 40% across 20 acute care hospitals in our healthcare system within 1 year. The study included all adult inpatients with sepsis sampled according to CMS specifications from October 2015 to September 2016. The CMS sepsis bundle compliance rate was tracked monthly using statistical process control charting. A baseline rate of 28.5% with 99% control limits was established. We implemented multiple interventions including computerised decision support systems (CDSSs) to increase compliance with the most commonly missing bundle elements. Compliance reached 42% (99% statistical process control limits 18.4%-38.6%) as CDSS was implemented system-wide, but this improvement was not sustained after CMS changed specifications of the outcome measure. Difficulties encountered elucidate shortcomings of our study methodology and of the CMS sepsis bundle compliance rate as a quality process measure.
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http://dx.doi.org/10.1136/bmjoq-2017-000080 | DOI Listing |
Clin Microbiol Infect
December 2024
Department of Internal Medicine, Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, NE. Electronic address:
Eur J Emerg Med
December 2024
Sorbonne Université, IMProving Emergency Care (IMPEC) FHU Paris.
NEJM AI
October 2024
Division of Biomedical Informatics, University of California, San Diego, San Diego.
Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction.
View Article and Find Full Text PDFPediatr Crit Care Med
December 2024
Division of Pediatric Critical Care, Department of Pediatrics, Riley Hospital for Children at Indiana, University Health and Indiana University School of Medicine, Indianapolis, IN.
Intern Emerg Med
December 2024
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
Adhering to bundle-based care recommendations within stringent time constraints presents a profound challenge. Elements within these bundles hold varying degrees of significance. We aimed to evaluate the Surviving Sepsis Campaign (SSC) hour-one bundle compliance patterns and their association with patient outcomes.
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