Literature DB >> 24048073

Developing an early sepsis alert program.

Kristen M Buck1.   

Abstract

Severe sepsis and septic shock are major health care problems affecting millions of people around the world each year. To aid in early identification and treatment of patients with sepsis, one Midwestern health care system has developed and implemented a computer-assisted sepsis alert system. Despite some limitations, the program has been moderately successful in identifying patients whose condition is declining, and it is having an overall positive effect on patient care. Program modifications continue with experience.

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Year:  2014        PMID: 24048073     DOI: 10.1097/NCQ.0b013e3182a98182

Source DB:  PubMed          Journal:  J Nurs Care Qual        ISSN: 1057-3631            Impact factor:   1.597


  9 in total

1.  Impact of Electronic Physician Order-Set on Antibiotic Ordering Time in Septic Patients in the Emergency Department.

Authors:  Emily L Fargo; Frank D'Amico; Aaron Pickering; Kathleen Fowler; Ronald Campbell; Megan Baumgartner
Journal:  Appl Clin Inform       Date:  2018-12-05       Impact factor: 2.342

2.  Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Authors:  Jennifer C Ginestra; Heather M Giannini; William D Schweickert; Laurie Meadows; Michael J Lynch; Kimberly Pavan; Corey J Chivers; Michael Draugelis; Patrick J Donnelly; Barry D Fuchs; Craig A Umscheid
Journal:  Crit Care Med       Date:  2019-11       Impact factor: 7.598

3.  In reference to "Development, implementation, and impact of an automated early warning and response system for sepsis".

Authors:  Poushali Bhattacharjee; Dana P Edelson
Journal:  J Hosp Med       Date:  2015-04-13       Impact factor: 2.960

Review 4.  Identifying Patients With Sepsis on the Hospital Wards.

Authors:  Poushali Bhattacharjee; Dana P Edelson; Matthew M Churpek
Journal:  Chest       Date:  2016-07-01       Impact factor: 9.410

Review 5.  Computerized Clinical Decision Support Systems for the Early Detection of Sepsis Among Adult Inpatients: Scoping Review.

Authors:  Khalia Ackermann; Jannah Baker; Malcolm Green; Mary Fullick; Hilal Varinli; Johanna Westbrook; Ling Li
Journal:  J Med Internet Res       Date:  2022-02-23       Impact factor: 7.076

6.  Evaluation of antibiotic escalation in response to nurse-driven inpatient sepsis screen.

Authors:  Daisuke Furukawa; Thomas D Dieringer; Mitchell D Wong; Julia T Tong; Isa A Cader; Lauren E Wisk; Maria A Han; Summer M Gupta; Russell B Kerbel; Daniel Z Uslan; Christopher J Graber
Journal:  Antimicrob Steward Healthc Epidemiol       Date:  2021-12-03

Review 7.  Automated monitoring compared to standard care for the early detection of sepsis in critically ill patients.

Authors:  Sheryl Warttig; Phil Alderson; David Jw Evans; Sharon R Lewis; Irene S Kourbeti; Andrew F Smith
Journal:  Cochrane Database Syst Rev       Date:  2018-06-25

8.  Evaluating the impact of a computerized surveillance algorithm and decision support system on sepsis mortality.

Authors:  Sharad Manaktala; Stephen R Claypool
Journal:  J Am Med Inform Assoc       Date:  2016-05-25       Impact factor: 4.497

9.  Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders.

Authors:  Kenrick D Cato; Kathleen McGrow; Sarah Collins Rossetti
Journal:  Nurs Manage       Date:  2020-11
  9 in total

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