Literature DB >> 21724635

Impact of protocol watch on compliance with the surviving sepsis campaign.

Karen K Giuliano1, Michele Lecardo, LuAnn Staul.   

Abstract

PURPOSE: Clinical decision support systems are intended to improve patients' care and outcomes, particularly when such systems are present at the point of care. Protocol Watch was developed as a bedside clinical decision support system to improve clinicians' adherence to the Surviving Sepsis Campaign guidelines. This pre/post-intervention pilot study was done to evaluate the effect of Protocol Watch on compliance with 5 guidelines from the Surviving Sepsis Campaign.
METHODS: Preintervention data on rates and time to complete the resuscitation and management bundles from the Surviving Sepsis Campaign and time to administer antibiotics were collected from intensive care units at 2 large teaching hospitals in the United States. Training on the Protocol Watch application was then provided to clinical staff in the units, and Protocol Watch was installed at all critical care beds in both hospitals. Data were collected on rates and time to completion for 5 Surviving Sepsis Campaign guidelines after installation of Protocol Watch, and univariate analyses were done to evaluate the effect of Protocol Watch on compliance with the guidelines.
RESULTS: Implementation of Protocol Watch was associated with significant improvements in compliance with the resuscitation bundle (P = .01) and decreased time to administer antibiotics (P = .006). No significant changes were achieved for compliance with the management bundle or time to complete the resuscitation or management bundles.
CONCLUSIONS: Clinical decision support systems such as Protocol Watch may improve adherence to the Surviving Sepsis Campaign guidelines, which potentially may contribute to reduced morbidity and mortality for critically ill patients with sepsis.

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Year:  2011        PMID: 21724635     DOI: 10.4037/ajcc2011421

Source DB:  PubMed          Journal:  Am J Crit Care        ISSN: 1062-3264            Impact factor:   2.228


  7 in total

1.  Secondary Analysis of an Electronic Surveillance System Combined with Multi-focal Interventions for Early Detection of Sepsis.

Authors:  Bonnie L Westra; Sean Landman; Pranjul Yadav; Michael Steinbach
Journal:  Appl Clin Inform       Date:  2017-01-18       Impact factor: 2.342

Review 2.  What are effective strategies for the implementation of care bundles on ICUs: a systematic review.

Authors:  Marjon J Borgert; Astrid Goossens; Dave A Dongelmans
Journal:  Implement Sci       Date:  2015-08-15       Impact factor: 7.327

Review 3.  Effect of performance improvement programs on compliance with sepsis bundles and mortality: a systematic review and meta-analysis of observational studies.

Authors:  Elisa Damiani; Abele Donati; Giulia Serafini; Laura Rinaldi; Erica Adrario; Paolo Pelaia; Stefano Busani; Massimo Girardis
Journal:  PLoS One       Date:  2015-05-06       Impact factor: 3.240

Review 4.  The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review.

Authors:  Christopher E Curtis; Fares Al Bahar; John F Marriott
Journal:  PLoS One       Date:  2017-08-24       Impact factor: 3.240

Review 5.  Systematic review of clinical decision support interventions with potential for inpatient cost reduction.

Authors:  Christopher L Fillmore; Bruce E Bray; Kensaku Kawamoto
Journal:  BMC Med Inform Decis Mak       Date:  2013-12-17       Impact factor: 2.796

6.  The impact of a multifaceted intervention including sepsis electronic alert system and sepsis response team on the outcomes of patients with sepsis and septic shock.

Authors:  Yaseen M Arabi; Hasan M Al-Dorzi; Ahmed Alamry; Ra'ed Hijazi; Sami Alsolamy; Majid Al Salamah; Hani M Tamim; Saad Al-Qahtani; Abdulaziz Al-Dawood; Abdellatif M Marini; Fatimah H Al Ehnidi; Shihab Mundekkadan; Amal Matroud; Mohamed S Mohamed; Saadi Taher
Journal:  Ann Intensive Care       Date:  2017-05-30       Impact factor: 6.925

7.  Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Authors:  Eli Bloch; Tammy Rotem; Jonathan Cohen; Pierre Singer; Yehudit Aperstein
Journal:  J Healthc Eng       Date:  2019-11-03       Impact factor: 2.682

  7 in total

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