Adverse drug events prevention rules: multi-site evaluation of rules from various sources.

Adverse drug events are a public health issue (98,000 deaths in the USA every year). Some computerized physician order entry (CPOEs) coupled with clinical decision support systems (CDSS) allow to prevent ADEs thanks to decision rules. Those rules can come from many sources: academic knowledge, record reviews, and data mining. Whatever their origin, the rules may induce too numerous alerts of poor accuracy when identically applied in different places. In this work we formalized rules from various sources in XML and enforced their execution on several medical departments to evaluate their local confidence. The article details the process and shows examples of evaluated rules from various sources. Several needs are enlightened to improve confidences: segmentation, contextualization, and evaluation of the rules over time.

[1]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[2]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[3]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[4]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[5]  Nada Lavrac,et al.  Selected techniques for data mining in medicine , 1999, Artif. Intell. Medicine.

[6]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[7]  George Hripcsak,et al.  Detecting adverse events for patient safety research: a review of current methodologies , 2003, J. Biomed. Informatics.

[8]  D. Bates,et al.  Incidence and preventability of adverse drug events among older persons in the ambulatory setting. , 2003, JAMA.

[9]  D. Bates,et al.  Adverse drug events and medication errors: detection and classification methods , 2004, Quality and Safety in Health Care.

[10]  Lemuel R. Waitman,et al.  Improving Computerized Provider Order Entry (CPOE) Usability by Data Mining Users' Queries from Access Logs , 2006, AMIA.

[11]  Lior Rokach,et al.  An Introduction to Decision Trees , 2007 .

[12]  David W. Bates,et al.  Can surveillance systems identify and avert adverse drug events? A prospective evaluation of a commercial application. , 2008, Journal of the American Medical Informatics Association : JAMIA.

[13]  Régis Beuscart,et al.  Data-Mining-Based Detection of Adverse Drug Events , 2009, MIE.