title: A probabilistic model for reducing medication errors.
abstract: BACKGROUND
  Medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases.
  
  
  RESULTS
  Association rules of mining techniques are utilized for 103.5 million prescriptions from Taiwan's National Health Insurance database. The dataset included 204.5 million diagnoses with ICD9-CM codes and 347.7 million medications by using ATC codes. Disease-Medication (DM) and Medication-Medication (MM) associations were computed by their co-occurrence and associations' strength were measured by the interestingness or lift values which were being referred as Q values. The DMQs and MMQs were used to develop the AOP model to predict the appropriateness of a given prescription. Validation of this model was done by comparing the results of evaluation performed by the AOP model and verified by human experts. The results showed 96% accuracy for appropriate and 45% accuracy for inappropriate prescriptions, with a sensitivity and specificity of 75.9% and 89.5%, respectively.
  
  
  CONCLUSIONS
  We successfully developed the AOP model as an efficient tool for automatic identification of uncommon or rare associations between disease-medication and medication-medication in prescriptions. The AOP model helps to reduce medication errors by alerting physicians, improving the patients' safety and the overall quality of care.
authors: Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan ; College of Medicine Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
authors: Phung Anh
authors: PA
authors: Nguyen
authors: Shabbir
authors: S
authors: Syed-Abdul
authors: Usman
authors: U
authors: Iqbal
authors: Min-Huei
authors: MH
authors: Hsu
authors: Chen-Ling
authors: CL
authors: Huang
authors: Hsien-Chang
authors: HC
authors: Li
authors: Daniel Livius
authors: DL
authors: Clinciu
authors: Wen-Shan
authors: WS
authors: Jian
authors: Yu-Chuan Jack
authors: YC
authors: Li
mesh_terms: D008508:Medication Errors; D015233:Models, Statistical