Toward a Learning Health-care System -- Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection requir...
| Publicado en: | Biomedical Informatics Insights Vol. 8; pp. 13 - 23 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2016 Supp1
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118546799&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118546799 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2016 Supp1 vid: 8 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 118546799 118546799 118546799 10.4137/Bii.s37977 118546799 ppf: 13 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Toward a Learning Health-care System -- Knowledge Delivery at the Point of Care Empowered by Big Data and NLP. aug: au: Kaggal, Vinod C. Komandur Elayavilli, Ravikumar Mehrabi, Saeed Pankratz, Joshua J. Sunghwan Sohn Yanshan Wang Dingcheng Li Rastegar, Majid Mojarad Murphy, Sean P. Ross, Jason L. Chaudhry, Rajeev Buntrock, James D. Hongfang Liu affil: Division of Information Management and Analytics, Mayo Clinic, Rochester, MN, USA sug: subj: Natural Language Processing Data Analytics Electronic Health Records Time Health Data Collection Sensitivity and Specificity Data Mining Study Design Record Review Data Analysis Case Studies Narratives Scales ab: The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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