@inproceedings{8ef5f94b7fbe472585bb5481b42b03c6,
title = "Methods for detecting malfunctions in clinical decision support systems",
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.",
keywords = "Electronic health records, Expert systems, Safety management",
author = "Adam Wright and Hickman, {Trang T.} and Dustin McEvoy and Skye Aaron and Angela Ai and Ash, {Joan S.} and Andersen, {Jan Marie} and Rachel Ramoni and Milos Hauskrecht and Peter Embi and Richard Schreiber and Sittig, {Dean F.} and Bates, {David W.}",
note = "Publisher Copyright: {\textcopyright} 2017 International Medical Informatics Association (IMIA) and IOS Press.; 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 ; Conference date: 21-08-2017 Through 25-08-2017",
year = "2017",
doi = "10.3233/978-1-61499-830-3-1385",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "1385",
editor = "Gundlapalli, {Adi V.} and Jaulent Marie-Christine and Zhao Dongsheng",
booktitle = "MEDINFO 2017",
address = "Netherlands",
}