title: A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database.
abstract: OBJECTIVE
  Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool.
  
  
  METHODS
  We developed an algorithm referred to as the Comparison on Extreme Laboratory Test results, which takes an extreme representative value pair according to the types of laboratory abnormalities on the basis of each patient's medication point. We used 10 years' EMR data from a tertiary teaching hospital, containing 32,033,710 prescriptions and 115,241,147 laboratory tests for 530,829 individual patients. Ten drugs were selected randomly for analysis, and 51 laboratory values were matched. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated.
  
  
  RESULTS
  The mean number of detected laboratory abnormality signals for each drug was 27 (±7.5). The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 64-100%, 22-76%, 22-75%, and 54-100%, respectively.
  
  
  CONCLUSIONS
  The results of this study demonstrated that the Comparison on Extreme Laboratory Test results algorithm described herein was extremely effective in detecting the signals characteristic of adverse drug reactions. This algorithm can be regarded as a useful signal detection tool, which can be routinely applied to EMR data.
authors.affiliation: Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea. l.harmark@lareb.nl
authors.forename: Man Young
authors.initials: MY
authors.lastname: Park
authors.forename: Dukyong
authors.initials: D
authors.lastname: Yoon
authors.forename: Kiyoung
authors.initials: K
authors.lastname: Lee
authors.forename: Seok Yun
authors.initials: SY
authors.lastname: Kang
authors.forename: Inwhee
authors.initials: I
authors.lastname: Park
authors.forename: Suk-Hyang
authors.initials: SH
authors.lastname: Lee
authors.forename: Woojae
authors.initials: W
authors.lastname: Kim
authors.forename: Hye Jin
authors.initials: HJ
authors.lastname: Kam
authors.forename: Young-Ho
authors.initials: YH
authors.lastname: Lee
authors.forename: Ju Han
authors.initials: JH
authors.lastname: Kim
authors.forename: Rae Woong
authors.initials: RW
authors.lastname: Park
mesh_terms: D000293:Adolescent; D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000368:Aged; D000465:Algorithms; D002648:Child; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011358:Product Surveillance, Postmarketing / Q000379:methods; D012680:Sensitivity and Specificity; D055815:Young Adult