Electronic health records and adverse drug events after patient transfer

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چکیده

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منابع مشابه

Electronic health records and adverse drug events after patient transfer.

BACKGROUND Our objective was to examine the frequencies of medication error and adverse drug events (ADEs) at the time of patient transfer in a system with an electronic health record (EHR) as compared with a system without an EHR. It was hypothesised that the frequencies of these events would be lower in the EHR system because of better information exchange across sites of care. METHODS 469 ...

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Text Mining Electronic Health Records to Identify Hospital Adverse Events

Manual reviews of health records to identify possible adverse events are time consuming. We are developing a method based on natural language processing to quickly search electronic health records for common triggers and adverse events. Our results agree fairly well with those obtained using manual reviews, and we therefore believe that it is possible to develop automatic tools for monitoring a...

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Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

OBJECTIVES Electronic healthcare records (EHRs) are a rich source of information, with huge potential for secondary research use. The aim of this study was to develop an application to identify instances of Adverse Drug Events (ADEs) from free text psychiatric EHRs. METHODS We used the GATE Natural Language Processing (NLP) software to mine instances of ADEs from free text content within the ...

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ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) ...

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Learning Signals of Adverse Drug-Drug Interactions from the Unstructured Text of Electronic Health Records

Drug-drug interactions (DDI) account for 30% of all adverse drug reactions, which are the fourth leading cause of death in the US. Current methods for post marketing surveillance primarily use spontaneous reporting systems for learning DDI signals and validate their signals using the structured portions of Electronic Health Records (EHRs). We demonstrate a fast, annotation-based approach, which...

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ژورنال

عنوان ژورنال: BMJ Quality & Safety

سال: 2010

ISSN: 2044-5415,2044-5423

DOI: 10.1136/qshc.2009.033050