Automated methods for the summarization of electronic health records.
Objectives: This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation.Target Audien...
| Published in: | Journal of the American Medical Informatics Association Vol. 22; no. 5; pp. 938 - 948 |
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| Main Authors: | , |
| Format: | review Journal Article |
| Published: |
Oxford University Press / USA
Sep2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109639995&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109639995 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Sep2015 vid: 22 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 109639995 NLM25882031 2013153145 10.1093/jamia/ocv032 NLM25882031 PMC4986665 109639995 ppf: 938 ppct: 10 formats: tig: atl: Automated methods for the summarization of electronic health records. aug: au: Pivovarov, Rimma Elhadad, Noémie sug: ab: Objectives: This review examines work on automated summarization of electronic health record (EHR) data and in particular, individual patient record summarization. We organize the published research and highlight methodological challenges in the area of EHR summarization implementation.Target Audience: The target audience for this review includes researchers, designers, and informaticians who are concerned about the problem of information overload in the clinical setting as well as both users and developers of clinical summarization systems.Scope: Automated summarization has been a long-studied subject in the fields of natural language processing and human-computer interaction, but the translation of summarization and visualization methods to the complexity of the clinical workflow is slow moving. We assess work in aggregating and visualizing patient information with a particular focus on methods for detecting and removing redundancy, describing temporality, determining salience, accounting for missing data, and taking advantage of encoded clinical knowledge. We identify and discuss open challenges critical to the implementation and use of robust EHR summarization systems. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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