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Methods for Detecting Malfunctions in Clinical Decision Support Systems
Adam Wright, Trang T. Hickman, Dustin McEvoy, Skye Aaron, Angela Ai, Joan S. Ash, Jan Marie Andersen, Rachel Ramoni, Milos Hauskrecht, Peter Embi, Richard Schreiber, Dean F. Sittig, David W. Bates
Clinical decision support systems, when used effectively, can improve the quality of care. However, such systems can malfunction, and these malfunctions can be difficult to detect. In this poster, we describe four methods of detecting and resolving issues with clinical decision support: 1) statistical anomaly detection, 2) visual analytics and dashboards, 3) user feedback analysis, 4) taxonomization of failure modes/effects.
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