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      Desiderata for computable representations of electronic health records-driven phenotype algorithms

      research-article
      1 , 2 , 3 , 4 , 5 , 5 , 6 , 7 , 7 , 8 , 9 , 10 , 9 , 3 , 11 , 12 , 8 , 13 , 5 , 14 , 1 , 15 , 1 , 7 , 16 , 17 , 1 , 18 , 19 , 11 , 15 , 20 , 1
      Journal of the American Medical Informatics Association : JAMIA
      Oxford University Press
      electronic health records, phenotype algorithms, computable representation, phenotype standardization, data models

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          Abstract

          Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).

          Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.

          Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.

          Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

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          Most cited references74

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          2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults

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            The "meaningful use" regulation for electronic health records.

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              A simple algorithm for identifying negated findings and diseases in discharge summaries.

              Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                jaminfo
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                November 2015
                05 September 2015
                : 22
                : 6
                : 1220-1230
                Affiliations
                1Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
                2Center for Biomedical Research Informatics, NorthShore University HealthSystem, Evanston, IL, USA
                3Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
                4Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
                5Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
                6Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA
                7Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
                8Group Health Research Institute, Seattle, WA, USA
                9Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH, USA
                10Center for Applied Genomics, the Children's Hospital of Philadelphia, Philadelphia, PA, USA
                11Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI, USA
                12Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa
                13Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
                14Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
                15Department of Medicine, Vanderbilt University, Nashville, TN, USA
                16Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, USA
                17Department of Genome Sciences, University of Washington, Seattle, WA, USA
                18Genomic Medicine Institute, Geisinger Health System, Danville, PA, USA
                19The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, USA
                20Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
                Author notes
                Correspondence to Joshua C Denny, MD, MS 2525 West End Ave, Suite 672 Nashville, TN 37232, USA; josh.denny@ 123456vanderbilt.edu ; Tel: 615-936-5034
                Article
                ocv112
                10.1093/jamia/ocv112
                4639716
                26342218
                1d4a931b-33ae-42be-b9de-10606b84e97e
                © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 9 February 2015
                : 20 June 2015
                : 24 June 2015
                Page count
                Pages: 11
                Categories
                Research and Applications

                Bioinformatics & Computational biology
                electronic health records,phenotype algorithms,computable representation,phenotype standardization,data models

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