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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/103455
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/103455


    Title: A Smart Medication Recommendation Model for The Electronic Prescription
    Authors: 黃鼎鈞
    Contributors: 資管博三
    Keywords: NHI database;Medications;Inappropriate prescription;Diagnosis-Medication association;Smart medication recommendation model
    Date: 2014-11
    Issue Date: 2016-11-07 15:18:10 (UTC+8)
    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.
    Relation: Computer Methods and Programs in Biomedicine · November 2014, Vol.117, No.2, pp.218-224
    Data Type: article
    DOI: http://dx.doi.org/10.1016/j.cmpb.2014.06.019
    Appears in Collections:[資訊管理學系] 期刊論文

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