Electronic medical records for discovery research in rheumatoid arthritis.
Objective: Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately...
| Publicado en: | Arthritis Care & Research Vol. 62; no. 8; pp. 1120 - 1128 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2010 Aug
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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=105078843&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105078843 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2151464X B8M4 jtl: Arthritis Care & Research issn: 2151464X maglogo: N pubinfo: dt: 2010 Aug vid: 62 iid: 8 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105078843 105078843 NLM20235204 2010748230 10.1002/acr.20184 NLM20235204 PMC3121049 105078843 ppf: 1120 ppct: 8 formats: tig: atl: Electronic medical records for discovery research in rheumatoid arthritis. aug: au: Liao KP Cai T Gainer V Goryachev S Zeng-Treitler Q Raychaudhuri S Szolovits P Churchill S Murphy S Kohane I Karlson EW Plenge RM Liao, Katherine P Cai, Tianxi Gainer, Vivian Goryachev, Sergey Zeng-treitler, Qing Raychaudhuri, Soumya Szolovits, Peter Churchill, Susanne affil: Brigham and Women's Hospital, Boston, Massachusetts, USA sug: subj: Arthritis, Rheumatoid Diagnosis Adult Aged Algorithms Autoantibodies Blood Autoantibodies Diagnostic Use Prospective Studies Electronic Health Records Female Human International Classification of Diseases Male Middle Age Research Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Objective: Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone.Methods: Subjects with > or =1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti-cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an "RA Mart" (n = 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms.Results: A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions).Conclusion: We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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