| Literature DB >> 29295464 |
Adam Wright1, Trang T Hickman1, Dustin McEvoy1, Skye Aaron1, Angela Ai1, Joan S Ash2, Jan Marie Andersen1, Rachel Ramoni3, Milos Hauskrecht4, Peter Embi5, Richard Schreiber6, Dean F Sittig7, David W Bates1.
Abstract
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.Entities:
Keywords: Electronic Health Records; Expert Systems; Safety Management
Mesh：
Year: 2017 PMID： 29295464
Source DB: PubMed Journal: Stud Health Technol Inform ISSN： 0926-9630