For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
80
views
32
references
 Top references
cited by
108
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      445
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Next-generation phenotyping of electronic health records

      research-article
      Author(s): ,
      Publication date (Electronic):
      Journal: Journal of the American Medical Informatics Association : JAMIA
      Publisher: BMJ Group

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.

          Related collections

          Most cited references32

          • Record: found
          • Abstract: not found
          • Article: not found

          The "meaningful use" regulation for electronic health records.

          David Blumenthal, Marilyn Tavenner (2010)
          Article 0 comments Cited 429 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

            Nicole Weiskopf, Chunhua Weng (2013)
            Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.
            Article 0 comments Cited 321 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels.

              N Tatonetti, J C Denny, S Murphy … (2011)
              The lipid-lowering agent pravastatin and the antidepressant paroxetine are among the most widely prescribed drugs in the world. Unexpected interactions between them could have important public health implications. We mined the US Food and Drug Administration's (FDA's) Adverse Event Reporting System (AERS) for side-effect profiles involving glucose homeostasis and found a surprisingly strong signal for comedication with pravastatin and paroxetine. We retrospectively evaluated changes in blood glucose in 104 patients with diabetes and 135 without diabetes who had received comedication with these two drugs, using data in electronic medical record (EMR) systems of three geographically distinct sites. We assessed the mean random blood glucose levels before and after treatment with the drugs. We found that pravastatin and paroxetine, when administered together, had a synergistic effect on blood glucose. The average increase was 19 mg/dl (1.0 mmol/l) overall, and in those with diabetes it was 48 mg/dl (2.7 mmol/l). In contrast, neither drug administered singly was associated with such changes in glucose levels. An increase in glucose levels is not a general effect of combined therapy with selective serotonin reuptake inhibitors (SSRIs) and statins.
              Article 0 comments Cited 64 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): J Am Med Inform Assoc
                Journal ID (iso-abbrev): J Am Med Inform Assoc
                Journal ID (publisher-id): jamia
                Journal ID (hwp): amiajnl
                Title: Journal of the American Medical Informatics Association : JAMIA
                Publisher: BMJ Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Print): 1067-5027
                ISSN (Electronic): 1527-974X
                Publication date (Print): Jan-Feb 2013
                Publication date (Electronic): 6 September 2012
                Publication date PMC-release: 6 September 2012
                Volume: 20
                Issue: 1
                Pages: 117-121
                Affiliations
                Biomedical Informatics, Columbia University, New York, NY, USA
                Author notes
                [Correspondence to ] Dr George Hripcsak, Department of Biomedical Informatics, Columbia University Medical Center, 622 West 168th Street, VC5, New York, NY 10027, USA; hripcsak@ 123456columbia.edu
                Article
                Publisher ID: amiajnl-2012-001145
                DOI: 10.1136/amiajnl-2012-001145
                PMC ID: 3555337
                PubMed ID: 22955496
                SO-VID: 076a0be3-2288-4a9b-8607-cf3a41b68b0a
                Copyright © Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/3.0/ and http://creativecommons.org/licenses/by-nc/3.0/legalcode

                History
                Date received : 5 June 2012
                Date accepted : 11 August 2012
                Categories
                Subject: Focus on Data Sharing
                Subject: 1506
                Custom metadata
                special-feature unlocked

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

                Comments

                Comment on this article

                scite_

                Similar content445

                See all similar

                Cited by226

                See all cited by

                Most referenced authors468

                See all reference authors