Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial

Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificia...

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Veröffentlicht in:	PloS one 2021-08, Vol.16 (8), p.e0255261
Hauptverfasser:	Seol, Hee Yun, Shrestha, Pragya, Muth, Joy Fladager, Wi, Chung-Il, Sohn, Sunghwan, Ryu, Euijung, Park, Miguel, Ihrke, Kathy, Moon, Sungrim, King, Katherine, Wheeler, Philip, Borah, Bijan, Moriarty, James, Rosedahl, Jordan, Liu, Hongfang, McWilliams, Deborah B, Juhn, Young J
Format:	Artikel
Sprache:	eng
Schlagworte:	Adolescent Algorithms Artificial Intelligence Asthma Asthma - therapy Asthma in children Care and treatment Child Child, Preschool Childhood Children Childrens health Clinical trials Computer and Information Sciences Decision making Decision support systems Decision Support Systems, Clinical Electronic Health Records Electronic medical records Female Health care delivery Health risks Humans Informatics Information management Intervention Learning algorithms Machine learning Male Management Medical practice software Medical records Medicine Medicine and Health Sciences Pediatric research Pediatrics Physical Sciences Population Primary care Randomization Research and Analysis Methods Testing
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Zusammenfassung:	Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0255261