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Evaluation of Automated Reminders to Reduce Sepsis Mortality Rates

Sepsis is still a leading cause of death in the United States despite extensive research and modern advancement in technology. Early recognition of sepsis and timely management strategies are important for effective reduction of sepsis-related morbidity and mortality. Guided by the logic model, the purpose of this project was to evaluate the effectiveness of electronic reminders in enhancing clinical decision-making among 30 nurses in 3 medical-surgical units. The practice-focused question addressed the effectiveness of electronic reminders for early recognition and initiation of goal-directed treatment of sepsis in hospitalized patients on medical-surgical units in an effort to reduce sepsis mortality rates. Data were collected from a randomized convenience sample using a self-constructed questionnaire and through observation. The observations were aimed at assessing whether the nurses adhered to the sepsis protocol, while the questionnaire captured the participants' perceptions regarding the use of automated alerts measured on a 5-point Likert scale. Statistical analysis involved the use of frequencies and percentages, positive predictive value (PPV), and negative predictive value (NPV). The results indicated that all the nurses adhered to sepsis protocol. The sepsis-related mortality rate, mean response time, and rate of severe sepsis at the hospital were reduced by 17.2%, 14 minutes, and 11.1%, respectively. It was concluded that automatic alert systems improve nurses' ability to recognize early symptoms of sepsis and their ability to initiate Code Sepsis. However, replication of this study using a large sample size could provide findings that are more generalizable. Electronic reminders may promote positive social change because earlier recognition of sepsis by nurses may lead to a reduction of healthcare costs through improved management of sepsis patients in acute care settings.

Identiferoai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-4504
Date01 January 2017
CreatorsLindo, Maria M
PublisherScholarWorks
Source SetsWalden University
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceWalden Dissertations and Doctoral Studies

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