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A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Implementation of an Artificial Intelligence Algorithm for sepsis detection.

Luciana Schleder Gonçalves¹, Maria Luiza de Medeiros Amaro¹, Andressa de Lima Miranda Romero²

¹Universidade Federal do Paraná. Curitiba, Paraná, Brazil.

Revista Brasileira De Enfermagem

|April 16, 2020

Summary

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Nurses

Area of Science:

Nursing Informatics
Clinical Technology
Sepsis Management

Background:

Early sepsis identification is critical for patient outcomes.
Technological tools can aid clinical decision-making.
Integrating new technology into nursing practice presents unique challenges.

Purpose of the Study:

To describe nurses' experiences with technological tools for early sepsis identification.
To report on the implementation of artificial intelligence (AI) algorithms in sepsis detection.
To analyze the impact of AI on nursing workflows.

Main Methods:

An experience report detailing the period before and after AI implementation.
Focus on a philanthropic hospital setting during the first half of 2018.

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Qualitative description of the algorithm's development and integration.

Main Results:

The study outlines the motivation behind the AI algorithm's creation and use.
Details the crucial role of nurses in developing and implementing the technology.
Examines the effects of the AI tool on the nursing work process and sepsis identification.

Conclusions:

Technological innovations must enhance healthcare professional practices.
Nurses play a vital role throughout the technology adoption lifecycle.
Nurse involvement in technology incorporation facilitates rapid, early sepsis identification and improves patient care.