摘要

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. Methods and Finding(s): 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.
原文英語
文章編號e82401
期刊PLoS One
8
發行號12
DOIs
出版狀態已發佈 - 十二月 3 2013

指紋

probabilistic models
Medication Errors
Statistical Models
drug therapy
Prescriptions
Inappropriate Prescribing
Quality of Health Care
Health insurance
National Health Programs
Patient Safety
Taiwan
Association rules
Information technology
Databases
Technology
Physicians
Identification (control systems)
Sensitivity and Specificity
health insurance
information technology

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

引用此文

A probabilistic model for reducing medication errors. / Nguyen, Phung Anh; Syed-Abdul, Shabbir; Iqbal, Usman; Hsu, Min-Huei; Huang, Chen-Ling; Li, Hsien-Chang; Clinciu, Daniel Livius; Jian, Wen Shan; Li, Yu Chuan Jack.

於: PLoS One, 卷 8, 編號 12, e82401, 03.12.2013.

研究成果: 雜誌貢獻文章

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