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Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation
  1. Norman Lance Downing1,2,
  2. Joshua Rolnick3,4,
  3. Sarah F Poole5,
  4. Evan Hall6,
  5. Alexander J Wessels7,
  6. Paul Heidenreich8,
  7. Lisa Shieh7
  1. 1 Department of Medicine - Biomedical Informatics Research, Hospital Medicine, and Primary Care and Population Health, Stanford University, Stanford, California, USA
  2. 2 Clinical Excellence Research Center, Stanford University, Stanford, California, USA
  3. 3 Division of General Internal Medicine, Department of Medicine and the National Clinician Scholars Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  4. 4 Corporal Michael J. Crescenz VA Medical Center, Pennsylvania, PA, United States
  5. 5 Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
  6. 6 Medicine, Hematology and Oncology, Stanford University, Stanford, California, USA
  7. 7 Medicine, Stanford School of Medicine, Stanford, California, USA
  8. 8 Department of Medicine, Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA
  1. Correspondence to Dr Norman Lance Downing, Medicine, Biomedical Informatics Research, Hospital Medicine, and Primary Care and Population Health, Stanford University, Stanford, CA 94025, USA; ldowning{at}stanford.edu

Abstract

Background Sepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions.

Objectives To determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis.

Design Patient-level randomisation, single blinded.

Setting Medical and surgical inpatient units of an academic, tertiary care medical centre.

Patients 1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015.

Interventions Patients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders.

Measurements and main results There was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids.

Conclusions An EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.

  • sepsis
  • electronic health record
  • clinical decision support
  • alert
  • protocol

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0

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Footnotes

  • NLD and JR are joint first authors.

  • Contributors NLD and JR contributed equally to this manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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