Promoting early identification of sepsis in hospitalized patients with nurse-led protocols

Kleinpell, Ruth M.

doi:10.1186/s13054-016-1590-0

Cited by 37 publications

(33 citation statements)

References 13 publications

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“…The authors of the study concluded an improvement in the number of sepsis protocols that were started on patients, increasing from 29% to 69%. The study proved that having a nursing staff that is more educated on sepsis identification and treatment can result in better sepsis identification and treatment (Kleinpell, 2017).…”

Section: Nursing Education On Sepsismentioning

confidence: 86%

“…Integrating sepsis screenings and protocols as part of the nurse's routine assessment can help identify at risk patients and begin interventions earlier. A study conducted in New Zealand was used to raise awareness amongst staff and improve the management of sepsis by integrating sepsis screenings into the facilities nursing assessment (Kleinpell, 2017). Educational seminars were implemented for the nurses.…”

Section: Nursing Education On Sepsismentioning

confidence: 99%

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Increasing the Knowledge of Critical Care Nursing Staff Related to Sepsis: A Program Development Project

Willard¹

2018

Preprint

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Sepsis can lead the body into organ failure, tissue damage, amputations, and in severe cases, even death. Yale New Haven Health-Westerly Hospital has several new RN’s that are at a novice level of knowledge about how to treat and care for the septic patient. Knowledge of the signs and symptoms of sepsis can lead to a quicker diagnosis and improve patient outcomes. The purpose of this program development project was to improve the knowledge of sepsis identification and treatment by providing education to the critical care nursing staff at Yale Westerly Hospital. A non-probability convenience sampling plan was utilized. Fifteen nurses out of a potential twenty nurses completed the pre-test and post-test component. Each participant’s test score from the pre-test in comparison to the post-test improved after the education component of the project was implemented. Pre-test scores encompassing the entire exam, ranged from 35% to 60% and post-testing scores ranged from 65% to 85%. The mean score of the pre-test was 52% and the mean score of the post-test was 78.6%. The mean scores from the pre-test compared to the post-test improved by 26.6%. More experience in the critical care setting did influence the average test scores in both the pre and post-test. APRN’s are in a unique position to enable educational programs and assess educational needs of a certain department. The APRN can deliver education not only for the novice nurses, but for all nurses throughout the experience spectrum.

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Section: Nursing Education On Sepsismentioning

confidence: 86%

Section: Nursing Education On Sepsismentioning

confidence: 99%

Increasing the Knowledge of Critical Care Nursing Staff Related to Sepsis: A Program Development Project

Willard¹

2018

Preprint

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“…In addition, nurses are at the bedside of critically ill patients in the ICU; they have a unique position in observing patients. Other studies on nurse-driven sepsis protocols (including simple screening tools) have been shown to be highly effective in early identification and treatment of patients with sepsis, and a nursedriven care bundleebased sepsis protocol recommendations resulted in increased survival [11,12]. Both IFD and sepsis are diseases of high mortality in ICU patients.…”

Section: The Widely Used Criteria For Ifd Diagnosis Based On Revised mentioning

confidence: 99%

A Risk Prediction Model for Invasive Fungal Disease in Critically Ill Patients in the Intensive Care Unit

Zhou

Zou

et al. 2018

Asian Nursing Research

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Developing a risk prediction model for invasive fungal disease based on an analysis of the disease-related risk factors in critically ill patients in the intensive care unit (ICU) to diagnose the invasive fungal disease in the early stages and determine the time of initiating early antifungal treatment. Methods: Data were collected retrospectively from 141 critically ill adult patients with at least 4 days of general ICU stay at Sun Yat-sen Memorial Hospital, Sun Yat-sen University during the period from February 2015 to February 2016. Logistic regression was used to develop the risk prediction model. Discriminative power was evaluated by the area under the receiver operating characteristics (ROC) curve (AUC). Results: Sequential organ failure assessment (SOFA) score, antibiotic treatment period, and positive culture of Candida albicans other than normally sterile sites are the three predictors of invasive fungal disease in critically ill patients in the ICU. The model performs well with an ROC-AUC of .73. Conclusion: The risk prediction model performs well to discriminate between critically ill patients with or without invasive fungal disease. Physicians could use this prediction model for early diagnosis of invasive fungal disease and determination of the time to start early antifungal treatment of critically ill patients in the ICU.

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“…In recent years, there has been great interest in the development of machine learning algorithms (3)(4)(5) that can enhance health professional's capacity for early diagnosis, taking into consideration the results of sepsis treatment, even when an institutional protocol already exists. In the scenario of Artificial Intelligence (AI), Machine Learning (ML) stands out.…”

Section: Introductionmentioning

confidence: 99%

“…In health, this technology assists decision making in a precise way. Using algorithms and based on the analyzes of the ML, the machine presents accurate statistical predictions (5) In the context of early identification of sepsis, the role of nurses in the evaluation of signs and symptoms is already well recognized in worldwide literature (3)(4)(5)(6) . Considering a broader context, nursing scientific research on the topics of human resources management, patient safety and quality of hospital care show a connection between these factors, as well as with organizational culture and management (7) , in the early identification of signs that may put at risk patient safety.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence Algorithm for sepsis detection

Gonçalves

Amaro

Romero³

et al. 2020

Rev. Bras. Enferm.

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Objectives: to present the nurses’ experience with technological tools to support the early identification of sepsis. Methods: experience report before and after the implementation of artificial intelligence algorithms in the clinical practice of a philanthropic hospital, in the first half of 2018. Results: describe the motivation for the creation and use of the algorithm; the role of the nurse in the development and implementation of this technology and its effects on the nursing work process. Final Considerations: technological innovations need to contribute to the improvement of professional practices in health. Thus, nurses must recognize their role in all stages of this process, in order to guarantee safe, effective and patient-centered care. In the case presented, the participation of the nurses in the technology incorporation process enables a rapid decision-making in the early identification of sepsis.

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Promoting early identification of sepsis in hospitalized patients with nurse-led protocols

Cited by 37 publications

References 13 publications

Increasing the Knowledge of Critical Care Nursing Staff Related to Sepsis: A Program Development Project

Increasing the Knowledge of Critical Care Nursing Staff Related to Sepsis: A Program Development Project

A Risk Prediction Model for Invasive Fungal Disease in Critically Ill Patients in the Intensive Care Unit

Implementation of an Artificial Intelligence Algorithm for sepsis detection

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