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Accelerated Chest Pain Treatment With Artificial Intelligence-Informed, Risk-Driven Triage.
Hinson, Jeremiah S; Taylor, R Andrew; Venkatesh, Arjun; Steinhart, Benjamin D; Chmura, Christopher; Sangal, Rohit B; Levin, Scott R.
Affiliation
  • Hinson JS; Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Taylor RA; Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland.
  • Venkatesh A; Beckman Coulter Diagnostics, Brea, California.
  • Steinhart BD; Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut.
  • Chmura C; Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut.
  • Sangal RB; Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut.
  • Levin SR; Beckman Coulter Diagnostics, Brea, California.
JAMA Intern Med ; 2024 Jul 22.
Article in En | MEDLINE | ID: mdl-39037785
This quality improvement study evaluates the use of artificial intelligence to accelerate triage of patients presenting to the emergency department with chest pain.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: JAMA Intern Med Year: 2024 Document type: Article
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: JAMA Intern Med Year: 2024 Document type: Article