Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

Scherer, Juliane de Souza; Pereira, Jéssica Silveira; Debastiani, Mariana Severo; Bica, Cláudia Giuliano

doi:10.1590/0034-7167-2021-0586

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Most studies reported MLA evaluations at stage II (retrospective), 32 , 33 , 36 , 38–40 , 45 , 48 , 49 , 55–57 2 at stage III (silent trial), 33 , 41 4 at stage IV (pilot trial), 50 , 58–60 3 at stage V (large trial/roll-out), 33 , 46 , 61 and 11 reported post-deployment evaluations. 35 , 37 , 42–44 , 47 , 48 , 51–53 , 62 …”

Section: Resultsmentioning

confidence: 99%

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

van der Vegt,

Scott,

Dermawan

et al. 2023

Journal of the American Medical Informatics Association

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Objective To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework. Materials and Methods Systematically review studies of clinically applied AI-based sepsis prediction algorithms in regard to methodological quality, deployment and evaluation methods, and outcomes. Identify contextual factors that influence implementation and map these factors to the SALIENT implementation framework. Results The review identified 30 articles of algorithms applied in adult hospital settings, with 5 studies reporting significantly decreased mortality post-implementation. Eight groups of algorithms were identified, each sharing a common algorithm. We identified 14 barriers, 26 enablers, and 22 decision points which were able to be mapped to the 5 stages of the SALIENT implementation framework. Discussion Empirical studies of deployed sepsis prediction algorithms demonstrate their potential for improving care and reducing mortality but reveal persisting gaps in existing implementation guidance. In the examined publications, key decision points reflecting real-word implementation experience could be mapped to the SALIENT framework and, as these decision points appear to be AI-task agnostic, this framework may also be applicable to non-sepsis algorithms. The mapping clarified where and when barriers, enablers, and key decisions arise within the end-to-end AI implementation process. Conclusions A systematic review of real-world implementation studies of sepsis prediction algorithms was used to validate an end-to-end staged implementation framework that has the ability to account for key factors that warrant attention in ensuring successful deployment, and which extends on previous AI implementation frameworks.

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Section: Resultsmentioning

confidence: 99%

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

van der Vegt,

Scott,

Dermawan

et al. 2023

Journal of the American Medical Informatics Association

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“…Essas tecnologias baseadas em IA têm auxiliado na reestruturação e contribuído para melhorias significativas em todas as esferas da prestação do cuidado desde a prevenção, diagnóstico precoce, predição de prognóstico, otimização das intervenções e tratamento de suporte [91]. No Brasil, entre outras iniciativas já aplicadas na rotina dos cuidados de saúde, dispositivos baseados em IA utilizados à beira leito para monitoramento de sinais vitais e biomarcadores têm se mostrado como ferramentas promissora para manejo e detecção precoce de condições complexas e graves como a sepse [92,93].…”

Section: Serviços Inteligentes Baseados Em Aprendizado De Máquinaunclassified

Internet das Coisas de Saúde: aplicando IoT, interoperabilidade e aprendizado de máquina com foco no paciente

Bertoni

Rodrigues

Zeiser

et al. 2022

Minicursos Do XXII Simpósio Brasileiro De Computação Aplicada À Saúde

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Effectively recording, managing, and tracking clinical data securely integrated at different healthcare system levels is critical today. However, health systems are composed of several agents, and they are not always able to share and integrate the clinical data of patients. In this sense, the objective of this chapter is to analyze and present the challenges, research opportunities, and possible solutions for developing a distributed model of information interoperability focused on the patient. In addition, aspects related to using data for the development of machine learning models using a collaborative network for sharing patient data will be discussed. Finally, several aspects of implementation, legislation, and ethics involved in a collaborative network for sharing clinical data, will be presented and evaluated. ResumoO registro, gerenciamento e acompanhamento eficaz de dados clínicos integrados de maneira segura nos diferentes níveis de um sistema de saúde são fundamentais na atualidade. Contudo, os sistemas de saúde são compostos por diversos agentes e que nem sempre conseguem compartilhar e integrar os dados clínicos dos pacientes. Neste sentido, o objetivo deste capítulo é analisar e apresentar os desafios, oportunidades de

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Identification of Barriers to AI Implementation in Medical Information Systems

Pilch,

Zygała,

Gryncewicz

et al. 2024

Lecture Notes in Networks and Systems

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Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

Cited by 5 publications

References 21 publications

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework

Internet das Coisas de Saúde: aplicando IoT, interoperabilidade e aprendizado de máquina com foco no paciente

Identification of Barriers to AI Implementation in Medical Information Systems

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