摘要

Background: The report from the Institute of Medicine, To Err Is Human: Building a Safer Health System in 1999 drew a special attention towards preventable medical errors and patient safety. The American Reinvestment and Recovery Act of 2009 and federal criteria of 'Meaningful use' stage 1 mandated e-prescribing to be used by eligible providers in order to access Medicaid and Medicare incentive payments. Inappropriate prescribing has been identified as a preventable cause of at least 20% of drug-related adverse events. A few studies reported system-related errors and have offered targeted recommendations on improving and enhancing e-prescribing system. Objective: This study aims to enhance efficiency of the e-prescribing system by shortening the medication list, reducing the risk of inappropriate selection of medication, as well as in reducing the prescribing time of physicians. Method: 103.48 million prescriptions from Taiwan's national health insurance claim data were used to compute Diagnosis-Medication association. Furthermore, 100,000 prescriptions were randomly selected to develop a smart medication recommendation model by using association rules of data mining. Results and conclusion: The important contribution of this model is to introduce a new concept called Mean Prescription Rank (MPR) of prescriptions and Coverage Rate (CR) of prescriptions. A proactive medication list (PML) was computed using MPR and CR. With this model the medication drop-down menu is significantly shortened, thereby reducing medication selection errors and prescription times. The physicians will still select relevant medications even in the case of inappropriate (unintentional) selection.
原文英語
頁(從 - 到)218-224
頁數7
期刊Computer Methods and Programs in Biomedicine
117
發行號2
DOIs
出版狀態已發佈 - 十一月 1 2014

指紋

Electronic Prescribing
Prescriptions
Health insurance
Association rules
Medicine
Data mining
American Recovery and Reinvestment Act
Health
Recovery
Medication Systems
Inappropriate Prescribing
Physicians
Medical Errors
Medication Errors
National Academies of Science, Engineering, and Medicine (U.S.) Health and Medicine Division
Data Mining
Medicaid
National Health Programs
Patient Safety
Medicare

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics

引用此文

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title = "A smart medication recommendation model for the electronic prescription",
abstract = "Background: The report from the Institute of Medicine, To Err Is Human: Building a Safer Health System in 1999 drew a special attention towards preventable medical errors and patient safety. The American Reinvestment and Recovery Act of 2009 and federal criteria of 'Meaningful use' stage 1 mandated e-prescribing to be used by eligible providers in order to access Medicaid and Medicare incentive payments. Inappropriate prescribing has been identified as a preventable cause of at least 20{\%} of drug-related adverse events. A few studies reported system-related errors and have offered targeted recommendations on improving and enhancing e-prescribing system. Objective: This study aims to enhance efficiency of the e-prescribing system by shortening the medication list, reducing the risk of inappropriate selection of medication, as well as in reducing the prescribing time of physicians. Method: 103.48 million prescriptions from Taiwan's national health insurance claim data were used to compute Diagnosis-Medication association. Furthermore, 100,000 prescriptions were randomly selected to develop a smart medication recommendation model by using association rules of data mining. Results and conclusion: The important contribution of this model is to introduce a new concept called Mean Prescription Rank (MPR) of prescriptions and Coverage Rate (CR) of prescriptions. A proactive medication list (PML) was computed using MPR and CR. With this model the medication drop-down menu is significantly shortened, thereby reducing medication selection errors and prescription times. The physicians will still select relevant medications even in the case of inappropriate (unintentional) selection.",
keywords = "Diagnosis-Medication association, Inappropriate prescription, Medications, NHI database, Smart medication recommendation model",
author = "Shabbir Syed-Abdul and Alex Nguyen and Frank Huang and Jian, {Wen Shan} and Usman Iqbal and Vivian Yang and Min-Huei Hsu and Li, {Yu Chuan}",
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AU - Yang, Vivian

AU - Hsu, Min-Huei

AU - Li, Yu Chuan

PY - 2014/11/1

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