Routine use of the "ADE scorecards", an application for automated ADE detection in a general hospital.
Retrospective detection of Adverse Drug Events (ADEs) is challenging, notably because ADEs result from complex interactions between many factors. Data mining techniques have recently emerged in the field of automated retrospective ADE detection. The "ADE Scorecards" are a research application based on data-mining that has been built in the framework of the PSIP European Project, and potentially enables automated ADE retrospective detection. The objective of this paper is to evaluate the use of the ADE Scorecards in a real-life healthcare situation. For that purpose, the ADE Scorecards have been implemented in a French general hospital and have been used by the physicians and pharmacists for three years (corresponding to 73,000 inpatient stays). According to the results, 2% of the analyzed inpatient stays have a potential ADE with hyperkalemia, and 1% of them have a potential ADE with vitamin K antagonist overdose. In practice, the application, which was first designed to be a standalone web-based application for the physicians, has been used as a part of a more global quality improvement approach led by the pharmacists.
Public Health Department, Lille University Hospital; UDSL EA 2694; Univ Lille Nord de France; F-59000 Lille, France.
Emmanuel
E
Chazard
Michel
M
Luyckx
Jean-Baptiste
JB
Beuscart
Laurie
L
Ferret
Régis
R
Beuscart
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005602:France / Q000453:epidemiology; D006769:Hospitals, General / Q000706:statistics & numerical data*; D006801:Humans; D050316:Medical Order Entry Systems / Q000706:statistics & numerical data*; D015995:Prevalence