Predicting Inpatient Medication Orders From Electronic Health Record Data
نویسندگان
چکیده
منابع مشابه
The relative benefits of claims and electronic health record data for predicting medication adherence trajectory.
BACKGROUND Healthcare providers are increasingly encouraged to improve their patients' adherence to chronic disease medications. Prediction of adherence can identify patients in need of intervention, but most prediction efforts have focused on claims data, which may be unavailable to providers. Electronic health records (EHR) are readily available and may provide richer information with which t...
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Employing a machine learning approach we predict, up to 24 hours prior, a diagnosis of severe sepsis. Strongly predictive models are possible that use only text reports from the Electronic Health Record (EHR), and omit structured numerical data. Unstructured text alone gives slightly better performance than structured data alone, and the combination further improves performance. We also discuss...
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The Stanford Translational Research Integrated Database Environment (STRIDE) clinical data warehouse integrates medication information from two Stanford hospitals that use different drug representation systems. To merge this pharmacy data into a single, standards-based model supporting research we developed an algorithm to map HL7 pharmacy orders to RxNorm concepts. A formal evaluation of this ...
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Technological progress and changes have affected the field of medicine and health. Also, the electronic health record is used for convenience of treatment, prevention and diagnosis, but it has advantages and disadvantages that we have studied them in this research. Method: Qualitative research methods and Grounded theory were used for the sociological study of electronic health record, and in...
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ژورنال
عنوان ژورنال: Clinical Pharmacology & Therapeutics
سال: 2020
ISSN: 0009-9236,1532-6535
DOI: 10.1002/cpt.1826