Leveraging Clinical Informatics and Data Science to Improve Care and Facilitate Research in Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference

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art_SANCHEZ-PINTO_Leveraging_Clinical_Informatics_and_Data_Science_to_Improve_2023.PDF (681.04 KB)
Citações na Scopus
4
Tipo de produção
article
Data de publicação
2023
DOI
Scopus
Web of Science
PubMed
Título da Revista
ISSN da Revista
Título do Volume
Editora
LIPPINCOTT WILLIAMS & WILKINS
Autores
SANCHEZ-PINTO, L. Nelson
SAUTHIER, Michael
RAJAPREYAR, Prakadeshwari
JOUVET, Philippe
NEWTH, Christopher
Citação
PEDIATRIC CRITICAL CARE MEDICINE, v.24, n.2, suppl.1, p.S1-S11, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
OBJECTIVES: The use of electronic algorithms, clinical decision support systems, and other clinical informatics interventions is increasing in critical care. Pediatric acute respiratory distress syndrome (PARDS) is a complex, dynamic condition associated with large amounts of clinical data and frequent decisions at the bedside. Novel data-driven technologies that can help screen, prompt, and support clinician decision-making could have a significant impact on patient outcomes. We sought to identify and summarize relevant evidence related to clinical informatics interventions in both PARDS and adult respiratory distress syndrome (ARDS), for the second Pediatric Acute Lung Injury Consensus Conference. DATA SOURCES: MEDLINE (Ovid), Embase (Elsevier), and CINAHL Complete (EBSCOhost). STUDY SELECTION: We included studies of pediatric or adult critically ill patients with or at risk of ARDS that examined automated screening tools, electronic algorithms, or clinical decision support systems. DATA EXTRACTION: Title/abstract review, full text review, and data extraction using a standardized data extraction form. DATA SYNTHESIS: The Grading of Recommendations Assessment, Development and Evaluation approach was used to identify and summarize evidence and develop recommendations. Twenty-six studies were identified for full text extraction to address the Patient/Intervention/Comparator/Outcome questions, and 14 were used for the recommendations/statements. Two clinical recommendations were generated, related to the use of electronic screening tools and automated monitoring of compliance with best practice guidelines. Two research statements were generated, related to the development of multicenter data collaborations and the design of generalizable algorithms and electronic tools. One policy statement was generated, related to the provision of material and human resources by healthcare organizations to empower clinicians to develop clinical informatics interventions to improve the care of patients with PARDS. CONCLUSIONS: We present two clinical recommendations and three statements (two research one policy) for the use of electronic algorithms and clinical informatics tools for patients with PARDS based on a systematic review of the literature and expert consensus.
Palavras-chave
clinical decision support, clinical informatics, data science, machine learning, pediatric acute respiratory distress syndrome
URI
https://observatorio.fm.usp.br/handle/OPI/54754
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Coleções
Artigos e Materiais de Revistas Científicas - FM/MPE
Artigos e Materiais de Revistas Científicas - LIM/36