Peer-Reviewed Publication
J Crit Care2026;97155748.September 18, 2026Journal Article
Development and usability testing of a machine learning clinical decision support tool for suggesting etiologies and treatment of clinical deterioration.
Oliver T Nguyen1, Douglas A Wiegmann1, James Haddad2, Sophia A Doerr2, Arsalan Ahmad1, Madeline Oguss2, Mary Akel3, Ann Wieben4, Joseph Reid5, Jonathan Allan5, Dana Edelson6, Matthew M Churpek7
1Department of Industrial and Systems Engineering, University of Wisconsin at Madison, Madison, WI, USA.
2Department of Medicine, University of Wisconsin at Madison, Madison, WI, USA.
3Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
4School of Nursing, University of Wisconsin at Madison, Madison, WI, USA.
5AgileMD, Inc, San Francisco, CA, USA.
6AgileMD, Inc, San Francisco, CA, USA; Department of Medicine, University of Chicago, Chicago, IL, USA.
7Department of Medicine, University of Wisconsin at Madison, Madison, WI, USA. Electronic address: mchurpek@medicine.wisc.edu.
Abstract
BACKGROUND: Early warning score clinical decision support (CDS) systems identify patients at risk of clinical deterioration, but do not provide insights on the underlying cause. Machine learning (ML) models may address this limitation, but how to convey such outputs to clinicians remains unclear. This study elicited clinicians' design requirements and assessed their impact on ML-CDS usability in a…
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