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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedAustrian JS, Jamin CT, Doty GR
Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay.
The goal of this study was to determine if an electronic health record (EHR) based sepsis alert system could improve quality of care and clinical outcomes for patients with sepsis. A patient-level, interrupted time series study of emergency department patients with severe sepsis or septic shock was conducted, with an intervention introduced at the approximate mid-point--a system of interruptive sepsis alerts triggered by abnormal vital signs or laboratory results. Mean length of stay for patients with sepsis decreased significantly following the introduction of the alert, but the alert system had no effect on mortality or other clinical or process measures. The researchers conclude that a more sophisticated algorithm for sepsis identification is needed to improve outcomes.
AHRQ-funded; HS023683.
Citation: Austrian JS, Jamin CT, Doty GR .
Impact of an emergency department electronic sepsis surveillance system on patient mortality and length of stay.
J Am Med Inform Assoc 2018 May;25(5):523-29. doi: 10.1093/jamia/ocx072..
Keywords: Electronic Health Records (EHRs), Emergency Department, Health Information Technology (HIT), Hospitals, Mortality, Outcomes, Quality Improvement, Quality of Care, Sepsis
Taylor RA, Pare JR, Venkatesh AK
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing clinical decision rules (CDRs) and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case. It concluded that this approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis.
AHRQ-funded; HS021271.
Citation: Taylor RA, Pare JR, Venkatesh AK .
Prediction of in-hospital mortality in emergency department patients with sepsis: A local big data-driven, machine learning approach.
Acad Emerg Med 2016 Mar;23(3):269-78. doi: 10.1111/acem.12876.
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Keywords: Emergency Medical Services (EMS), Mortality, Clinical Decision Support (CDS), Sepsis, Health Information Technology (HIT)