Comput Methods Programs Biomed - A smart medication recommendation model for the electronic prescription.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground report institut medicin err human build safer health system drew special attent toward prevent medic error patient safeti american reinvest recoveri act feder criteria meaning use stage mandat eprescrib use elig provid order access medicaid medicar incent payment inappropri prescrib identifi prevent caus least drugrel advers event studi report systemrel error offer target recommend improv enhanc eprescrib systemobject studi aim enhanc effici eprescrib system shorten medic list reduc risk inappropri select medic well reduc prescrib time physiciansmethod million prescript taiwan nation health insur claim data use comput diagnosismed associ furthermor prescript random select develop smart medic recommend model use associ rule data miningresult conclus import contribut model introduc new concept call mean prescript rank mpr prescript coverag rate cr prescript proactiv medic list pml comput use mpr cr model medic dropdown menu signific shorten therebi reduc medic select error prescript time physician will still select relev medic even case inappropri unintent select

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