title: An automated technique to identify potential inappropriate traditional Chinese medicine (TCM) prescriptions.
abstract: OBJECTIVE
  Medication errors such as potential inappropriate prescriptions would induce serious adverse drug events to patients. Information technology has the ability to prevent medication errors; however, the pharmacology of traditional Chinese medicine (TCM) is not as clear as in western medicine. The aim of this study was to apply the appropriateness of prescription (AOP) model to identify potential inappropriate TCM prescriptions.
  
  
  METHODS
  We used the association rule of mining techniques to analyze 14.5 million prescriptions from the Taiwan National Health Insurance Research Database. The disease and TCM (DTCM) and traditional Chinese medicine-traditional Chinese medicine (TCMM) associations are computed by their co-occurrence, and the associations' strength was measured as Q-values, which often referred to as interestingness or life values. By considering the number of Q-values, the AOP model was applied to identify the inappropriate prescriptions. Afterwards, three traditional Chinese physicians evaluated 1920 prescriptions and validated the detected outcomes from the AOP model.
  
  
  RESULTS
  Out of 1920 prescriptions, 97.1% of positive predictive value and 19.5% of negative predictive value were shown by the system as compared with those by experts. The sensitivity analysis indicated that the negative predictive value could improve up to 27.5% when the model's threshold changed to 0.4.
  
  
  CONCLUSIONS
  We successfully applied the AOP model to automatically identify potential inappropriate TCM prescriptions. This model could be a potential TCM clinical decision support system in order to improve drug safety and quality of care.
authors.affiliation: Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan.|Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
authors.forename: Hsuan-Chia
authors.initials: HC
authors.lastname: Yang
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
authors.forename: Usman
authors.initials: U
authors.lastname: Iqbal
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
authors.forename: Phung Anh
authors.initials: PA
authors.lastname: Nguyen
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
authors.forename: Shen-Hsien
authors.initials: SH
authors.lastname: Lin
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.
authors.forename: Chih-Wei
authors.initials: CW
authors.lastname: Huang
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.|School of Health Care Administration, Taipei Medical University, Taipei, Taiwan.|College of Management, Taipei Medical University, Taipei, Taiwan.|Faculty of Health Sciences, Macau University of Science and Technology, Macau, China.
authors.forename: Wen-Shan
authors.initials: WS
authors.lastname: Jian
authors.affiliation: Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan.|International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.|Department of Dermatology, Wan Fang Hospital, Taipei, Taiwan.
authors.forename: Yu-Chuan
authors.initials: YC
authors.lastname: Li
mesh_terms: D001331:Automation; D057225:Data Mining; D016208:Databases, Factual / Q000706:statistics & numerical data*; D020000:Decision Support Systems, Clinical; D006801:Humans; D057970:Inappropriate Prescribing / Q000517:prevention & control*; D008508:Medication Errors / Q000517:prevention & control*; D008516:Medicine, Chinese Traditional / Q000009:adverse effects / Q000379:methods*; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D013624:Taiwan
keywords: association rule mining; data mining; pharmacoepidemiology; potential appropriate prescription; traditional Chinese medicine