Electronic medical records for discovery research in rheumatoid arthritis

Arthritis Care and Research

Volumn 62, Issue 8, 2010, Pages 1120-1127

  Liao, Katherine P ^a   Cai, Tianxi ^b   Gainer, Vivian ^c   Goryachev, Sergey ^c   Zeng Treitler, Qing ^d   Raychaudhuri, Soumya ^a,e   Szolovits, Peter ^f   Churchill, Susanne ^c   Murphy, Shawn ^g   Kohane, Isaac ^a   Karlson, Elizabeth W ^a   Plenge, Robert M ^a,e  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c Partners Health Care System   (United States)

^d UNIVERSITY OF UTAH   (United States)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^f MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^g MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CLINICAL RESEARCH; CONTROLLED STUDY; ELECTRONIC MEDICAL RECORD; HUMAN; MAJOR CLINICAL STUDY; MEDICAL RECORD REVIEW; NARRATIVE; PATIENT CODING; RHEUMATOID ARTHRITIS;

ADULT; AGED; ALGORITHMS; ARTHRITIS, RHEUMATOID; AUTOANTIBODIES; COHORT STUDIES; ELECTRONIC HEALTH RECORDS; FEMALE; HUMANS; INTERNATIONAL CLASSIFICATION OF DISEASES; MALE; MIDDLE AGED; RESEARCH;

EID: 77955297110     PISSN: 21514658     EISSN: 15290131     Source Type: Journal    
DOI: 10.1002/acr.20184     Document Type: Article

References (34)

1. Poon EG, Jha AK, Christino M, Honour MM, Fernandopulle R, Middleton B, et al. Assessing the level of healthcare information technology adoption in the United States: a snapshot. BMC Med Inform Decis Mak 2006;6:1.
2. Trivedi B. Biomedical science: betting the bank. Nature 2008; 452:926-9.
3. Solomon DH, Goodson NJ, Katz JN, Weinblatt ME, Avorn J, Setoguchi S, et al. Patterns of cardiovascular risk in rheumatoid arthritis. Ann Rheum Dis 2006;65:1608-12.
4. Gabriel SE. The sensitivity and specificity of computerized databases for the diagnosis of rheumatoid arthritis. Arthritis Rheum 1994;37:821-3.
5. Singh JA, Holmgren AR, Noorbaloochi S. Accuracy of Veterans Administration databases for a diagnosis of rheumatoid arthritis. Arthritis Rheum 2004;51:952-7.
6. Katz JN, Barrett J, Liang MH, Bacon AM, Kaplan H, Kieval RI, et al. Sensitivity and positive predictive value of Medicare part B physician claims for rheumatologic diagnoses and procedures. Arthritis Rheum 1997;40:1594-600.
7. Losina E, Barrett J, Baron JA, Katz JN. Accuracy of Medicare claims data for rheumatologic diagnoses in total hip replacement recipients. J Clin Epidemiol 2003;56:515-9.
8. Schneeweiss S, Setoguchi S, Weinblatt ME, Katz JN, Avorn J, Sax PE, et al. Anti-tumor necrosis factor α therapy and the risk of serious bacterial infections in elderly patients with rheumatoid arthritis. Arthritis Rheum 2007;56:1754-64.
9. Effler P, Ching-Lee M, Bogard A, Ieong MC, Nekomoto T, Jernigan D. Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. JAMA 1999;282:1845-50.
10. Klompas M, Haney G, Church D, Lazarus R, Hou X, Platt R. Automated identification of acute hepatitis B using electronic medical record data to facilitate public health surveillance. PLoS One 2008;3:e2626.
11. Lazarus R, Klompas M, Campion FX, McNabb SJ, Hou X, Daniel J, et al. Electronic support for public health: validated case finding and reporting for notifiable diseases using electronic medical data. J Am Med Inform Assoc 2009;16:18-24.
12. Meystre S, Haug P. Improving the sensitivity of the problem list in an intensive care unit by using natural language processing. AMIA Annu Symp Proc 2006:554-8.
13. Meystre S, Haug PJ. Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J Biomed Inform 2006;39:589-99.
14. Solti I, Aaronson B, Fletcher G, Solti M, Gennari JH, Cooper M, et al. Building an automated problem list based on natural language processing: lessons learned in the early phase of development. AMIA Annu Symp Proc 2008:687-91.
15. Levin MA, Krol M, Doshi AM, Reich DL. Extraction and mapping of drug names from free text to a standardized nomenclature. AMIA Annu Symp Proc 2007:438-42.
16. Turchin A, Morin L, Semere LG, Kashyap V, Palchuk MB, Shubina M, et al. Comparative evaluation of accuracy of extraction of medication information from narrative physician notes by commercial and academic natural language processing software packages. AMIA Annu Symp Proc 2006:789-93.
17. Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc 2003;10:115-28.
18. Penz JF, Wilcox AB, Hurdle JF. Automated identification of adverse events related to central venous catheters. J Biomed Inform 2007;40:174-82.
19. Zeng QT, Goryachev S, Weiss S, Sordo M, Murphy SN, Lazarus R. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006;6:30.
20. Cunningham H, Humphreys K, Gaizauskas R, Wilks Y. GATE: a TIPSTER-based general architecture for text engineering. Proceedings of the TIPSTER Text Program (Phase III) 6 Month Workshop, DARPA, California; 1997.
21. Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS, et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 1988;31:315-24.
22. Banal F, Dougados M, Combescure C, Gossec L. Sensitivity and specificity of the American College of Rheumatology 1987 criteria for the diagnosis of rheumatoid arthritis according to disease duration: a systematic literature review and meta-analysis. Ann Rheum Dis 2009;68:1184-91.
23. Zou H, Hastie T, Tibshirani R. On the "degrees of freedom" of the Lasso. Stanford (CA): Stanford University Department of Statistics; 2004.
24. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: Springer-Verlag; 2001.
25. Zou H. The adaptive lasso and its oracle properties. J Am Stat Assoc 2006;101:1418-29.
26. Greenberg JD, Reed G, Kremer JM, Tindall E, Kavanaugh A, Zheng C, et al. Association of methotrexate and TNF antagonists with risk of infection outcomes including opportunistic infections in the CORRONA registry. Ann Rheum Dis 2010; 69:380-6.
27. Forslind K, Ahlmen M, Eberhardt K, Hafstrom I, Svensson B. Prediction of radiological outcome in early rheumatoid arthritis in clinical practice: role of antibodies to citrullinated peptides (anti-CCP). Ann Rheum Dis 2004;63:1090-5.
28. Bukhari M, Thomson W, Naseem H, Bunn D, Silman A, Symmons D, et al. The performance of anti-cyclic citrullinated peptide antibodies in predicting the severity of radiologic damage in inflammatory polyarthritis: results from the Norfolk Arthritis Register. Arthritis Rheum 2007;56:2929-35.
29. Lee DM, Schur PH. Clinical utility of the anti-CCP assay in patients with rheumatic diseases. Ann Rheum Dis 2003;62: 870-4.
30. Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, et al. Use of electronic health records in US hospitals. N Engl J Med 2009;360:1628-38.
31. Jha AK, Ferris TG, Donelan K, DesRoches C, Shields A, Rosenbaum S, et al. How common are electronic health records in the United States? A summary of the evidence. Health Aff (Millwood) 2006;25:w496-507.
32. Berner ES, Detmer DE, Simborg D. Will the wave finally break? A brief view of the adoption of electronic medical records in the United States. J Am Med Inform Assoc 2005;12:3-7.
33. DesRoches CM, Campbell EG, Rao SR, Donelan K, Ferris TG, Jha A, et al. Electronic health records in ambulatory care: a national survey of physicians. N Engl J Med 2008;359:50-60.
34. Murphy S, Churchill S, Bry L, Chueh H, Weiss S, Lazarus R, et al. Instrumenting the health care enterprise for discovery research in the genomic era. Genome Res 2009;19:1675-81.