摘要

Purpose: 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 14.5million 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. Result: 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. Conclusion: 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.
原文英語
期刊Pharmacoepidemiology and Drug Safety
DOIs
出版狀態接受/付印 - 2016

指紋

Chinese Traditional Medicine
Prescriptions
Inappropriate Prescribing
Medication Errors
Clinical Decision Support Systems
Value of Life
Quality of Health Care
National Health Programs
Drug-Related Side Effects and Adverse Reactions
Taiwan
Medicine
Databases
Pharmacology
Technology
Physicians
Safety

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Epidemiology

引用此文

@article{749e3d368f884092b054ad66e93d8399,
title = "An automated technique to identify potential inappropriate traditional Chinese medicine (TCM) prescriptions",
abstract = "Purpose: 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 14.5million 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. Result: 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. Conclusion: 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.",
keywords = "Association rule mining, Data mining, Pharmacoepidemiology, Potential appropriate prescription, Traditional Chinese medicine",
author = "Yang, {Hsuan Chia} and Iqbal Usman and Nguyen, {Phung Anh} and Lin, {Shen Hsien} and Huang, {Chih Wei} and Jian, {Wen Shan} and Li, {Yu Chuan}",
year = "2016",
doi = "10.1002/pds.3976",
language = "English",
journal = "Pharmacoepidemiology and Drug Safety",
issn = "1053-8569",
publisher = "John Wiley and Sons Ltd",

}

TY - JOUR

T1 - An automated technique to identify potential inappropriate traditional Chinese medicine (TCM) prescriptions

AU - Yang, Hsuan Chia

AU - Usman, Iqbal

AU - Nguyen, Phung Anh

AU - Lin, Shen Hsien

AU - Huang, Chih Wei

AU - Jian, Wen Shan

AU - Li, Yu Chuan

PY - 2016

Y1 - 2016

N2 - Purpose: 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 14.5million 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. Result: 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. Conclusion: 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.

AB - Purpose: 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 14.5million 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. Result: 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. Conclusion: 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.

KW - Association rule mining

KW - Data mining

KW - Pharmacoepidemiology

KW - Potential appropriate prescription

KW - Traditional Chinese medicine

UR - http://www.scopus.com/inward/record.url?scp=84959431451&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959431451&partnerID=8YFLogxK

U2 - 10.1002/pds.3976

DO - 10.1002/pds.3976

M3 - Article

C2 - 26910512

AN - SCOPUS:84959431451

JO - Pharmacoepidemiology and Drug Safety

JF - Pharmacoepidemiology and Drug Safety

SN - 1053-8569

ER -