The Mid-South clinical Data Research Network

S Trent Rosenbloom, Paul Harris, Jill Pulley, Melissa Basford, Jason Grant, Allison DuBuisson, Russell L Rothman, S Trent Rosenbloom, Paul Harris, Jill Pulley, Melissa Basford, Jason Grant, Allison DuBuisson, Russell L Rothman

Abstract

The Mid-South Clinical Data Research Network (CDRN) encompasses three large health systems: (1) Vanderbilt Health System (VU) with electronic medical records for over 2 million patients, (2) the Vanderbilt Healthcare Affiliated Network (VHAN) which currently includes over 40 hospitals, hundreds of ambulatory practices, and over 3 million patients in the Mid-South, and (3) Greenway Medical Technologies, with access to 24 million patients nationally. Initial goals of the Mid-South CDRN include: (1) expansion of our VU data network to include the VHAN and Greenway systems, (2) developing data integration/interoperability across the three systems, (3) improving our current tools for extracting clinical data, (4) optimization of tools for collection of patient-reported data, and (5) expansion of clinical decision support. By 18 months, we anticipate our CDRN will robustly support projects in comparative effectiveness research, pragmatic clinical trials, and other key research areas and have the capacity to share data and health information technology tools nationally.

Keywords: Clinical Research; Data Standards; Health Information Exchange; Interoperability; Research Network.

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.

Figures

Figure 1
Figure 1
Mid-South Clinical Data Research Network (CDRN) systems and resources. VU, Vanderbilt University; IMPH, Vanderbilt Institute for Medicine and Public Health; CHSSR, Vanderbilt Center for Health Services Research; VICTR, Vanderbilt Institute for Clinical and Translational Research; REDCap, Research Electronic Data Capture; IRB, Institutional Research Board; CTSA, Clinical and Translational Science Award Program; eMERGE, Electronic Medical Records and Genomics Network; VHAN, Vanderbilt Health Affiliate Network; VA, Veterans Administration; CMS, Centers for Medicare & Medicaid Services.
Figure 2
Figure 2
Mid-South Clinical Data Research Network (CDRN) reach. The top map shows the location of Greenway PrimeRESEARCH sites participating in their research network. The bottom map shows Vanderbilt Health Affiliate Network (VHAN) partner hospital sites.
Figure 3
Figure 3
The Mid-South CDRN's research data warehousing model includes integrating data from multiple clinical systems into a common architecture supporting both de-identified and identified use cases. Beneficiaries include informatics methods development research teams (e.g. natural language processing, de-id and re-id) whose work translates into improving both research and clinical practice. Scientific investigators across the research enterprise also benefit through tools and services built on data warehousing platforms.

References

    1. Guise JM, O'Haire C, McPheeters M, et al. A practice-based tool for engaging stakeholders in future research: a synthesis of current practices. J Clin Epidemiol 2013;66:666–74
    1. Frisse ME, King JK, Rice WB, et al. A regional health information exchange: architecture and implementation. AMIA Annu Symp Proc 2008:212–16
    1. Stead WW, Miller RA, Musen MA, et al. Integration and beyond: linking information from disparate sources and into workflow. J Am Med Inform Assoc 2000;7:135–45
    1. Vadakin AKR. CDISC standards and innovations. Clin Eval 2012; 40(Suppl XXXI):217–28
    1. Danciu I, Cowan JD, Basford M, et al. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform 2014. pii: S1532-0464(14)00039-2.
    1. Delaney JT, Ramirez AH, Bowton E, et al. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin Pharmacol Ther 2012;91:257–63
    1. Xu H, Jiang M, Oetjens M, et al. Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin. J Am Med Inform Assoc 2011;18:387–91
    1. Denny JC, Ritchie MD, Crawford DC, et al. Identification of genomic predictors of atrioventricular conduction: using electronic medical records as a tool for genome science. Circulation 2010;122:2016–21
    1. Denny JC, Ritchie MD, Basford MA, et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 2010;26:1205–10
    1. Denny JC, Choma NN, Peterson JF, et al. Natural language processing improves identification of colorectal cancer testing in the electronic medical record. Med Decis Making 2012;32:188–97
    1. Denny JC, Miller RA, Waitman LR, et al. Identifying QT prolongation from ECG impressions using a general-purpose Natural Language Processor. Int J Med Inform 2009;78(Suppl 1)S34–42
    1. Denny JC, Peterson JF, Choma NN, et al. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Am Med Inform Assoc 2010;17:383–8
    1. Xu H, Doan S, Birdwell KA, et al. An automated approach to calculating the daily dose of tacrolimus in electronic health records. AMIA Summits on Translational Science Proceedings; 2010;vol. 2010:71–5
    1. Wilke RA, Xu H, Denny JC, et al. The emerging role of electronic medical records in pharmacogenomics. Clin Pharmacol Ther 2011;89:379–86
    1. Pulley J, Clayton E, Bernard GR, et al. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin Transl Sci 2010;3:42–8
    1. 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;86:560–72
    1. Roden DM, Pulley JM, Basford MA, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther 2008;84:362–9
    1. Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377–81
    1. Obeid JS, McGraw CA, Minor BL, et al. Procurement of shared data instruments for Research Electronic Data Capture (REDCap). J Biomed Inform 2013;46:259–65
    1. Antman EM, Harrington RA. Transforming clinical trials in cardiovascular disease: mission critical for health and economic well-being. JAMA 2012;308:1743–4
    1. Research Match.
    1. Harris PA, Scott KW, Lebo L, et al. ResearchMatch: a national registry to recruit volunteers for clinical research. Acad Med 2012;87:66–73
    1. Pulley J, Hassan NN, Bernard GR, et al. Identifying unpredicted drug benefit through query of patient experiential knowledge: a proof of concept web-based system. Clin Transl Sci 2010;3:98–103
    1. Horsky J, Schiff GD, Johnston D, et al. Interface design principles for usable decision support: a targeted review of best practices for clinical prescribing interventions. J Biomed Inform 2012;45:1202–16

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다