The design and implementation of an open-source, data-driven cohort recruitment system: The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN)

Journal of the American Medical Informatics Association

Volumn 19, Issue E1, 2012, Pages

  Ferranti, Jeffrey M ^a,b   Gilbert, William ^b   McCall, Jonathan ^c   Shang, Howard ^b   Barros, Tanya ^b   Horvath, Monica M ^b  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b DUKE UNIVERSITY HEALTH SYSTEM   (United States)

^c DUKE CLINICAL RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLINICAL ASSESSMENT TOOL; DATA ANALYSIS; DUKE INTEGRATED SUBJECT COHORT AND ENROLLMENT RESEARCH NETWORK; HUMAN; MEDICAL INFORMATION SYSTEM; MEDICAL RESEARCH; MEDICAL TECHNOLOGY; PATIENT IDENTIFICATION; PERSONNEL MANAGEMENT; RESEARCH SUBJECT;

ASTHMA; CLINICAL TRIALS AS TOPIC; COMPUTER COMMUNICATION NETWORKS; DELIVERY OF HEALTH CARE, INTEGRATED; HEALTH LEVEL SEVEN; HUMANS; INTERNET; NORTH CAROLINA; PAPILLOMAVIRUS VACCINES; PATIENT SELECTION;

EID: 84863546948     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2011-000115     Document Type: Article

References (45)

1. Lovato LC, Hill K, Hertert S, et al. Recruitment for controlled clinical trials: literature summary and annotated bibliography. Control Clin Trials 1997;18:328-52.
2. Watson JM, Torgerson DJ. Increasing recruitment to randomised trials: a review of randomised controlled trials. BMC Med Res Methodol 2006;6:34.
3. Section A, Title XIII, of the American Recovery and Reinvestment Act of 2009(HITECH Act), 2009. frwebgate.access.gpo.gov/cgi-bin/getdoc.cgi?dbname1/4111_cong_ bills&docid1/4f:h1enr.pdf (accessed 22 Dec 2010).
4. Hersh WR. Adding value to the electronic health record through secondary use of data for quality assurance, research, and surveillance. Am J Manag Care 2007;13:277-278.
5. Institute of Medicine. Committee on Quality of Health Care in America. Chapter 7: Using information technology. In: Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press, 2001.
6. Safran C, Bloomrosen M, Hammond EW, et al. Towards a National Framework for the Secondary use of Health Data: A Report of the Working Conference of the American Medical Informatics Association (2006). https://www.amia.org/files/ workforthesecondaryuseofhealthdata_09_08_06_.pdf (accessed 22 Dec 2010).
7. Gren L, Broski K, Childs J, et al. Recruitment methods employed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Clin Trials 2009;6:52-59.
8. Marks L, Power E. Using technology to address recruitment issues in the clinical trial process. Trends Biotechnol 2002;20:105-109.
9. Haidich AB, Ioannidis JP. Effect of early patient enrollment on the time to completion and publication of randomized controlled trials. Am J Epidemiol 2001;154:873-880.
10. Friedman LM, Furberg CD, DeMets DL. Fundamentals of Clinical Trials. 3rd edn. New York: Springer Scientific Publishing, 1998.
11. Sung NS, Crowley WF, Genel M, et al. Central challenges facing the national clinical research enterprise. JAMA 2003;289:1278-1287.
12. Carter RE, Sonne SC, Brady KT. Practical considerations for estimating clinical trial accrual periods: application to a multi-center effectiveness study. BMC Med Res Methodol 2005;5:11.
13. Campbell MK, Snowdon C, Francis D, et al. Recruitment to randomised trials: strategies for trial enrollment and participation study. The STEPS study. Health Technol Assess 2007;11:iii, ix-105.
14. Barnard KD, Dent L, Cook A. A systematic review of models to predict recruitment to multicentre clinical trials. BMC Med Res Methodol 2010;10:63.
15. Butte AJ, Weinstein DA, Kohane IS. Enrolling patients into clinical trials faster using RealTime Recuiting. Proc AMIA Symp 2000:111-115.
16. Turchin A, Pendergrass ML, Kohane IS. DITTOda tool for identification of patient cohorts from the text of physician notes in the electronic medical record. AMIA Annu Symp Proc 2005:744-748.
17. Weiner DL, Butte AJ, Hibberd PL, et al. Computerized recruiting for clinical trials in real time. Ann Emerg Med 2003;41:242-246.
18. Embi PJ, Jain A, Clark J, et al. Effect of a clinical trial alert system on physician participation in trial recruitment. Arch Intern Med 2005;165:2272-2277.
19. Embi PJ, Jain A, Clark J, et al. Development of an electronic health record-based Clinical Trial Alert system to enhance recruitment at the point of care. AMIA Annu Symp Proc 2005:231-235.
20. Embi PJ, Payne PR, Kaufman SE, et al. Identifying challenges and opportunities in clinical research informatics: analysis of a facilitated discussion at the 2006 AMIA Annual Symposium. AMIA Annu Symp Proc 2007:221-225.
21. Afrin LB, Oates JC, Boyd CK, et al. Leveraging of open EMR architecture for clinical trial accrual. AMIA Annu Symp Proc 2003:16-20.
22. Cardozo E, Meurer WJ, Smith BL, et al. Utility of an automated notification system for recruitment of research subjects. Emerg Med J 2010;27:786-787.
23. Grundmeier RW, Swietlik M, Bell LM. Research subject enrollment by primary care pediatricians using an electronic health record. AMIA Annu Symp Proc 2007:289-293.
24. Kamal J, Pasuparthi K, Rogers P, et al. Using an information warehouse to screen patients for clinical trials: a prototype. AMIA Annu Symp Proc 2005:1004.
25. MacLean CD, Littenberg B, Gagnon M, et al. The Vermont Diabetes Information System (VDIS): study design and subject recruitment for a cluster randomized trial of a decision support system in a regional sample of primary care practices. Clin Trials 2004;1:532-544.
26. Oberg R, Rasmussen L, Melski J, et al. Evaluation of the google search appliance for patient cohort discovery. AMIA Annu Symp Proc 2008:1104.
27. Horvath MM, Winfield S, Evans S, et al. The DEDUCE Guided Query tool: Providing simplified access to clinical data for research and quality improvement. J Biomed Inform 2011;44:266-276.
28. Verma R, Harper J. Life cycle of a data warehousing project in healthcare. J Healthc Inf Manag 2001;15:107-117.
29. Kuperman GJ, Teich JM, Tanasijevic MJ, et al. Improving response to critical laboratory results with automation: results of a randomized controlled trial. J Am Med Inform Assoc 1999;6:512-522.
30. Tsui FC, Espino JU, Dato VM, et al. Technical description of RODS: a real-time public health surveillance system. J Am Med Inform Assoc 2003;10:399-408.
31. Reis BY, Kirby C, Hadden LE, et al. AEGIS: a robust and scalable real-time public health surveillance system. J Am Med Inform Assoc 2007;14:581-588.
32. Mandl KD, Overhage JM, Wagner MM, et al. Implementing syndromic surveillance: a practical guide informed by the early experience. J Am Med Inform Assoc 2004;11:141-150.
33. Bortis G. Experience with Mirth: An open Source Health Care Integration Engine. 2008 ICSE Experience Track on Software Engineering in Health Care, 2008. http:// www.ics.uci.edu/wgbortis/papers/gbortis_mirth2008.pdf (accessed 20 Jul 2011).
34. Lowe HJ, Ferris TA, Hernandez PM, et al. STRIDEdAn integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009;2009:391-395.
35. Murphy SN, Weber G, Mendis M, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc 2010;17:124-130.
36. Penberthy L, Brown R, Puma F, et al. Automated matching software for clinical trials eligibility: measuring efficiency and flexibility. Contemp Clin Trials 2010;31:207-217.
37. Brooks CJ, Stephens JW, Price DE, et al. Use of a patient linked data warehouse to facilitate diabetes trial recruitment from primary care. Prim Care Diabetes 2009;3:245-248.
38. Seyfried L, Hanauer DA, Nease D, et al. Enhanced identification of eligibility for depression research using an electronic medical record search engine. Int J Med Inform 2009;78:e13-e18.
39. Morris AD, Boyle DI, MacAlpine R, et al. The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. DARTS/MEMO Collaboration. BMJ 1997;315:524-528.
40. Weber S, Lowe HJ, Malunjkar S, et al. Implementing a real-time complex event stream processing system to help identify potential participants in clinical and translational research studies. AMIA Annu Symp Proc 2010;2010:472-476.
41. Nelson JL, Smith BL, Jared JD, et al. Prospective trial of real-time electronic surveillance to expedite early care of severe sepsis. Ann Emerg Med 2011;57:500-504.
42. Ahmad F, Gupta R, Kurz M. Real time electronic patient study enrollment system in emergency room. AMIA Annu Symp Proc 2005:881.
43. Berndt DJ, Fisher JW, Craighead JG, et al. The role of data warehousing in bioterrorism surveillance. Decis Support Syst 2007;43:1383-1403.
44. Santos RJ, Bernardino J. Real-time data warehouse loading methodology. IDEAS '08: Proceedings of the 2008 International Symposium on Database Engineering & Applications. New York, NY: Association for Computing Machinery, 2008. doi:10.1145/1451940.1451949.
45. Agosta L, Gile K. Real-time Data Warehousing: the Hype and the Reality, 2004. http://www.forrester.com/rb/Research/real-time_data_warehousing_hype_and_ reality/q/id/36076/t/2 (accessed 13 Apr 2011