Intelligent use and clinical benefits of electronic health records in rheumatoid arthritis

References

• Greenes RA, Pappalardo AN, Marble CW, Barnett GO. Design and implementation of a clinical data management system. Comput Biomed Res 1969;2(5):469-85
   Google Scholar
• McDonald CJ. Protocol-based computer reminders, the quality of care and the non-perfectibility of man. N Engl J Med 1976;295(24):1351-5
   PubMed Web of Science ®Google Scholar
• Shortliffe EH, Buchanan BG. A model of inexact reasoning in medicine. Math Biosci 1975;23(3-4):351-79
   Google Scholar
• Nuckols TK, Smith-Spangler C, Morton SC, et al. The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Syst Rev 2014;3(1):56
   PubMedGoogle Scholar
• King J, Patel V, Jamoom EW, Furukawa MF. Clinical benefits of electronic health record use: national findings. Health Serv Res 2014;49(1 Pt 2):392-404
   Google Scholar
• Wang SJ, Middleton B, Prosser LA, et al. A cost-benefit analysis of electronic medical records in primary care. Am J Med 2003;114(5):397-403
   PubMed Web of Science ®Google Scholar
• Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005;116(6):1506-12
   PubMed Web of Science ®Google Scholar
• Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005;293(10):1197-203
   PubMed Web of Science ®Google Scholar
• Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc JAMIA 2009;16(3):328-37
   PubMed Web of Science ®Google Scholar
• Köpcke F, Lubgan D, Fietkau R, et al. Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data. BMC Med Inform Decis Mak 2013;13:134
   Google Scholar
• McCarty CA, Chisholm RL, Chute CG, et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics 2011;4:13
   PubMed Web of Science ®Google Scholar
• Kohane IS. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 2011;12(6):417-28
   PubMed Web of Science ®Google Scholar
• Jha AK, DesRoches CM, Campbell EG, et al. Use of electronic health records in U.S. hospitals. N Engl J Med 2009;360(16):1628-38
   PubMed Web of Science ®Google Scholar
• Pulley JM, Denny JC, Peterson JF, et al. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther 2012;92(1):87-95
   PubMed Web of Science ®Google Scholar
• Roberts GW, Farmer CJ, Cheney PC, et al. Clinical decision support implemented with academic detailing improves prescribing of key renally cleared drugs in the hospital setting. J Am Med Inform Assoc JAMIA 2010;17(3):308-12
   Google Scholar
• Phansalkar S, Desai AA, Bell D, et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc JAMIA 2012;19(5):735-43
   Google Scholar
• Leung AA, Keohane C, Amato M, et al. Impact of vendor computerized physician order entry in community hospitals. J Gen Intern Med 2012;27(7):801-7
   PubMed Web of Science ®Google Scholar
• Rha B, Burrer S, Park S, et al. Emergency department visit data for rapid detection and monitoring of norovirus activity, United States. Emerg Infect Dis 2013;19(8):1214-21
   Google Scholar
• de Vries-Bouwstra J, Cessie SL, Allaart C, et al. Using predicted disease outcome to provide differentiated treatment of early rheumatoid arthritis. J Rheumatol 2006;33(9):1747-53
   PubMed Web of Science ®Google Scholar
• Fricton J, Rindal DB, Rush W, et al. The effect of electronic health records on the use of clinical care guidelines for patients with medically complex conditions. J Am Dent Assoc 1939;142(10):1133-42
   Google Scholar
• Matsui T, Tohma S. Support system for medical examination and treatment and for clinical research in rheumatic diseases. Nihon Rinsho Jpn J Clin Med 2005;63(Suppl 1):699-705
   Google Scholar
• Kolarz G, Adlassnig KP, Bögl K. RHEUMexpert: a documentation and expert system for rheumatic diseases. Wien Med Wochenschr 1946;149(19-20):572-4
   Google Scholar
• Leeb BF. Hotel-based rheumatology, and more. J Rheumatol 2009;36(2):455-5
   Google Scholar
• Palmer D, El Miedany Y. EROMIA in inflammatory arthritis: the next step in standard practice. Br J Nurs Mark Allen Publ 2010;19(1):42-6
   Google Scholar
• Fransen J, Twisk JWR, Creemers MCW, Van Riel PLCM. Design and analysis of a randomized controlled trial testing the effects of clinical decision support on the management of rheumatoid arthritis. Arthritis Care Res 2004;51(1):124-7
   Google Scholar
• Van der Wilt GJ, Groenewoud H, van Riel P. Bridging the gap between aggregate data and individual patient management: a Bayesian approach. Int J Technol Assess Health Care 2011;27(2):133-8
   Google Scholar
• Dahlström O, Timpka T, Hass U, et al. A simple method for heuristic modeling of expert knowledge in chronic disease: identification of prognostic subgroups in rheumatology. Stud Health Technol Inform 2008;136:157-62
   Google Scholar
• Williams CA, Mosley-Williams AD, Overhage JM. Arthritis quality indicators for the Veterans administration: implications for electronic data collection, storage format, quality assessment, and clinical decision support. AMIA Annu Symp Proc 2007;806-10
   Google Scholar
• Adhikesavan LG, Newman ED, Diehl MP, et al. American College of Rheumatology quality indicators for rheumatoid arthritis: benchmarking, variability, and opportunities to improve quality of care using the electronic health record. Arthritis Rheum 2008;59(12):1705-12
   Google Scholar
• Agnew-Blais J, Coblyn JS, Katz JN, et al. Measuring quality of care for rheumatic diseases using an electronic medical record. Ann Rheum Dis 2009;68(5):680-4
   Google Scholar
• Prevoo ML, van ’t Hof MA, Kuper HH, et al. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum 1995;38(1):44-8
   PubMed Web of Science ®Google Scholar
• Collier DS, Kay J, Estey G, et al. A rheumatology-specific informatics-based application with a disease activity calculator. Arthritis Rheum 2009;61(4):488-94
   Google Scholar
• Collier DS, Grant RW, Estey G, et al. Physician ability to assess rheumatoid arthritis disease activity using an electronic medical record-based disease activity calculator. Arthritis Rheum 2009;61(4):495-500
   Google Scholar
• El Miedany Y, El Gaafary M, Palmer D. Assessment of the utility of visual feedback in the treatment of early rheumatoid arthritis patients: a pilot study. Rheumatol Int 2012;32(10):3061-8
   Web of Science ®Google Scholar
• Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: data quality issues and informatics opportunities. AMIA Summits Transl Sci Proc 2010;2010:1-5
   Google Scholar
• Pakhomov S, Weston SA, Jacobsen SJ, et al. Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care 2007;13(6 Part 1):281-8
   Google Scholar
• Roden D, Xu H, Denny J, Wilke R. Electronic medical records as a tool in clinical pharmacology: opportunities and challenges. Clin Pharmacol Ther 2012;91(6):1083-6
   PubMed Web of Science ®Google Scholar
• Chute CG, Pathak J, Savova GK, et al. The SHARPn project on secondary use of electronic medical record data: progress, plans, and possibilities. AMIA Annu Symp Proc 2011;2011:248-56
   Google Scholar
• Wei W-Q, Leibson CL, Ransom JE, et al. Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus. J Am Med Inform Assoc JAMIA 2012;19(2):219-24
   Google Scholar
• Denny JC, Bastarache L, Ritchie MD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol 2013;31(12):1102-10
   PubMed Web of Science ®Google Scholar
• Web. PharmacoGenomic discovery and replication in very large patient POpulations (PGPop). Available from: http://pgpop.mc.vanderbilt.edu/labkey/project/PGPop/begin.view?
   Google Scholar
• Daugherty SE, Wahba S, Fleurence R. Patient-powered research networks: building capacity for conducting patient-centered clinical outcomes research. J Am Med Inform Assoc JAMIA 2014;21(4):583-6
   PubMed Web of Science ®Google Scholar
• Coorevits P, Sundgren M, Klein GO, et al. Electronic health records: new opportunities for clinical research. J Intern Med 2013;274(6):547-60
   PubMed Web of Science ®Google Scholar
• Carroll RJ, Thompson WK, Eyler AE, et al. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc JAMIA 2012;19(e1):e162-9
   Google Scholar
• Ng B, Aslam F, Petersen NJ, et al. Identification of rheumatoid arthritis patients using an administrative database: a Veterans Affairs study. Arthritis Care Res 2012;64(10):1490-6
   Google Scholar
• Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res 2010;62(8):1120-7
   Google Scholar
• Ritchie MD, Denny JC, Crawford DC, et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet 2010;86(4):560-72
   PubMed Web of Science ®Google Scholar
• Carroll RJ, Eyler AE, Denny JC. Naïve electronic health record phenotype identification for rheumatoid arthritis. AMIA Annu Symp Proc AMIA Symp AMIA Symp 2011;2011:189-96
   Google Scholar
• Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273-97
   Web of Science ®Google Scholar
• Nicholson A, Ford E, Davies KA, et al. Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code lists. PLoS One 2013;8(2):e54878
   Google Scholar
• Ford E, Nicholson A, Koeling R, et al. Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text? BMC Med Res Methodol 2013;13(1):105
   Google Scholar
• Liao KP, Kurreeman F, Li G, et al. Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non–rheumatoid arthritis controls. Arthritis Rheum 2013;65(3):571-81
   PubMedGoogle Scholar
• Lin C, Karlson EW, Canhao H, et al. Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records. PLoS One 2013;8(8):e69932
   PubMed Web of Science ®Google Scholar
• Tymms K, Zochling J, Scott J, et al. Barriers to optimal disease control for rheumatoid arthritis patients with moderate and high disease activity. Arthritis Care Res 2014;66(2):190-6
   PubMed Web of Science ®Google Scholar
• Schmajuk G, Miao Y, Yazdany J, et al. Identification of risk factors for elevated transaminases in methotrexate users through an electronic health record. Arthritis Care Res 2014;66(8):1159-66
   Google Scholar
• Denny JC, Ritchie MD, Basford MA, et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 2010;26(9):1205-10
   PubMed Web of Science ®Google Scholar
• Roden D, Pulley J, Basford M, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 2008;84(3):362-9
   PubMed Web of Science ®Google Scholar
• Kullo IJ, Ding K, Jouni H, et al. A genome-wide association study of red blood cell traits using the electronic medical record. PLoS One 2010;5:9
   Google Scholar
• Kullo IJ, Fan J, Pathak J, et al. Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. J Am Med Inform Assoc JAMIA 2010;17(5):568-74
   Google Scholar
• Web. Kaiser Permanente, UCSF Scientists Complete NIH-Funded Genomics Project Involving 100,000 People. Internet Available from: www.dor.kaiser.org/external/news/press_releases/Kaiser_Permanente,_UCSF_Scientists_Complete_NIH-Funded_Genomics_Project_Involving_100,000_People/
   Google Scholar
• Kurreeman F, Liao K, Chibnik L, et al. Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. Am J Hum Genet 2011;88(1):57-69
   PubMed Web of Science ®Google Scholar
• Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 2014;506(7488):376-81
   PubMed Web of Science ®Google Scholar
• Wilke R, Xu H, Denny J, et al. The emerging role of electronic medical records in pharmacogenomics. Clin Pharmacol Ther 2011;89(3):379-86
   PubMed Web of Science ®Google Scholar
• Peterson JF, Bowton E, Field JR, et al. Electronic health record design and implementation for pharmacogenomics: a local perspective. Genet Med Off J Am Coll Med Genet 2013;15(10):833-41
   Google Scholar
• Van Driest S, Shi Y, Bowton EA, et al. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. Clin Pharmacol Ther 2014;95(4):423-31
   PubMed Web of Science ®Google Scholar
• Hicks JK, Crews KR, Hoffman JM, et al. A clinician-driven automated system for integration of pharmacogenetic consults into an electronic medical record. Clin Pharmacol Ther 2012;92(5):563-6
   PubMed Web of Science ®Google Scholar
• Lee WM. Drug-induced hepatotoxicity. N Engl J Med 2003;349(5):474-85
   PubMed Web of Science ®Google Scholar
• Njoku DB. Drug-induced hepatotoxicity: metabolic, genetic and immunological basis. Int J Mol Sci 2014;15(4):6990-7003
   PubMed Web of Science ®Google Scholar
• Marquez A, Ferreiro-Iglesias A, Davila-Fajardo CL, et al. Lack of validation of genetic variants associated with anti-tumor necrosis factor therapy response in rheumatoid arthritis: a genome-wide association study replication and meta-analysis. Arthritis Res Ther 2014;16(2):R66
   Web of Science ®Google Scholar
• Lin C, Canhao H, Miller T, et al. Feature Engineering and Selection for Rheumatoid Arthritis Disease Activity Classification Using Electronic Medical Records. In: Proceedings of International Conference of Machine Learning (ICML) Workshop on Machine Learning for Clinical Data. 2012
   Google Scholar
• Mandl KD, Mandel JC, Murphy SN, et al. The SMART Platform: early experience enabling substitutable applications for electronic health records. J Am Med Inform Assoc JAMIA 2012;19(4):597-603
   PubMed Web of Science ®Google Scholar
• Swanton C. My cancer genome: a unified genomics and clinical trial portal. Lancet Oncol 2012;13(7):668-9
   Google Scholar
• Sciencedirect. Available from: www.sciencedirect.com/science/journal/00029297
   Google Scholar