Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts

OBJECTIVES: While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether...

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Detalles Bibliográficos
Autores principales: Baron, Jason M, Huang, Richard, McEvoy, Dustin, Dighe, Anand S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935497/
https://www.ncbi.nlm.nih.gov/pubmed/33709062
http://dx.doi.org/10.1093/jamiaopen/ooab006

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935497/
https://www.ncbi.nlm.nih.gov/pubmed/33709062
http://dx.doi.org/10.1093/jamiaopen/ooab006

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