UNIQUE eCRF â€“ IMPORTANCE TO CLINICAL TRIALS AND HUMAN HEALTH
Unique eCRF is a platform in which electronically maintained information about an individual's lifetime health status and health care records can be stored such that it can serve multiple legitimate users and along with serving as case report form of patients in clinical trials. Through Unique eCRF individuals can access, manage and share their health information with others who are authorized, in a private, secure, and confidential environment. Unique e-CRF has potential of integrating various domains of clinical trial like data capture, data cleaning, and data mining into one system and hence significantly contributes in clinical trial management.Â It also contributes in huge saving of a pharmaceutical company in terms of cost and time. Â Electronic CRFs offer advantages such as improved data quality, online discrepancy management and faster database lock.The other potential advantages include integrated patient's health and financial data, audit trial capabilities, identifying eligible patients for clinical trials from patient's records, trial randomisation, agile data transfer, follow patient outcomes, in creating patient registries, monitoring adverse drug reactions and pharmacovigilance reporting. Â Unique eCRF deserve a serious look because they are the most efficient way to connect patients to their medical data. They not only facilitate information sharing among doctors and guard against needless medical errors, but also offer a safety advantage in that health record would never again need to be stored. All eCRFs should be validated in compliance to 21 CFR Part 11.
2. Nahm M, Shepherd J, Buzenberg A, Rostami R, Corcoran A,McCall J, et al. Design and port form library. Clin Trials 2011; 8: 94-102.
3. Collins FM journal report health care the WSJ report http://www.wsj.com/articles/SB10001424052970204124204577154661814932978
4. The Economic Times, 16 July 2009. "Central government seeks statutory cover for UIDAI".
5. The Times of India, 24 July 2009."Nilekani takes charge, says first set of IDs in 12-18 months.
6. Hanna K. Think research: Using electronic medical records to bridge patient care and research; 2005.
7. Shortliffe EH, Cimino JJ. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 3rded. New York Springer; 2006.
8. Experience a new way of clinical trials planning and management:www.ecrfplus.com.
9. Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Personal Health Records: Definitions,Benefits, and Strategies for Overcoming Barriers to Adoption. J Am Med Inform Assoc. 2006; 13(2): 121-6.
10. Gostin LO Policeâ€ Powers and Public Health Paternalism: HIV and Diabetes Surveillance, 37 hasting Center REP. 9, 10 (2007) (Most poor people do not enjoy the benefits of education and income that enable them to form stable physician-patient relationships and comply with complex treatment regimes.â€).
11. Kush R, Bleicher P, Kubick W, Kush S, Marks R, Raymond S et al. Planning & Implementation eClnical Trials. USA: Boston Mass; 2003.
12. Clindex data integration http://www.fortressmedical.com/why-clindex/data-integration
13. Wright A,Bates DW, Middleton B, Hongsemeier T, Kashyan V, Thomas SM, et al. Creating& sharing clinical decision support content with web 2.0. Issues & examples. Journal of biomedical informatics 2009; 42(2): 334-6.
14. Integrated medical records at: www.research.ibm.com
15. Supporting clinical research discovere : www.cerner.com
16. Integrated health record can show meaningful use. Ravi Sharma is president and CEO of 4medica: www.healthmgttech.com
17. Kalra D, Ohmann C. EuroRec: BioMedBridges Annual General Meeting! ECRIN on behalf of the EHR4CR Consortium.
18. Welker JA. Implementation of electronic data captures systems: barriers and solutions. Contemp Clin Trials 2007; 28: 329â€“36.
19. Brandt CA, Argraves S, Money R, Ananth G, Trocky NM, Nadkarni PM. Informatics tools to improve clinical research study implementation. Contemp Clin Trials 2006; 27: 112â€“22.
20. Pavlovic I, Kern T, Miklavcic D. Comparison of paper-based and electronic data collection process in clinical trials: Costs simulation study. Contemp Clin Trials 2009; 30: 300â€“16.
21. Lu ZW. Information technology in pharmacovigilance: Benefits, challenges, and future directions from industry perspectives. Drug, Healthcare and Patient Safety 2009; 1: 35â€“45.
22. US Food and Drug Administration. Guidance for Industry: Computerized Systems used in Clinical Investigations, May 2007. Available from http://www.fda.gov/OHRMS/DOCKETS/98fr/04d-0440-gdl0002.pdf.
23. European Parliament and the Council of the European Union. Directive 2001/20/EC. Off JnEur Communities 2001; 121: 34â€“44.
24. Paul J, Seib R, Prescott T. The internet and clinical trials: background, online resources, examples and issues. J Med Internet Res 2005; 7(1): e5.
25. Alschuler L, Bain L, Kush RD. Improving data collection for patient care and clinical trials.SciCareerMagMar262004http://sciencecareers.sciencemag.org/career_magazine/previous_issues/articles/2004_03_26/noDOI.5622907321165187916.Archivedat:http://www.webcitation.org/5cGF6Xmf8.
26. De Bondt J. Clinical Data Acquisition Standards Harmonization (CDASH) the rising star.http://www.cdisc.org/system/files/all/reference_material/application/pdf/cdash_the_rising_star.pdf.
27. Lu ZW. Electronic data-capturing technology for clinical trials: Experience with a global post marketing study. IEEE Eng Med Biol Mag. 2010; 29: 95â€“102.
28. Pavlovic I, Miklavcic D. Web-based electronic data collection system to support electrochemotherapy clinical trial. IEEE Trans Inf Technol Biomed Mar 2007; 11(2): 222â€“30.
29. EI Emam K, Jonker E, Sampson M. The use of electronic data captures tools in clinical trials: Web-survey of 259 Canadian trials. J Med Internet Res. 2009; 11: 8-13.
30. Introduction to clinical trials. Accessed at http://clinicaltrials.gov/ct/info/whatis#types april 25,2007.
31. Von Spall HGC, Toren A. Eligibility criteria of randomized controlled trials published in high impactgeneral medical journals: a systematic sampling review. The journal of American Medical Association 2007; 297: 1233â€“40.
32. According to 21 CFR 201.57(f)(9)(i), the pediatric age group is defined as "birth to 16 years, including age groups often called neonates, infants, children, and adolescents." FDA states in guidance, however, that the Best Pharmaceuticals for Children Act (BPCA) defines pediatric studies to include studies in all pediatric age groups including neonates in appropriate cases, in which a drug is anticipated to be used.â€ For the purposes of satisfying the requirements of the Pediatric Research Equity Act (PREA), the appropriate age ranges to be studied may vary, depending on the pharmacology of the medical product, the manifestations for the disease in various age groups, and the ability to measure the response to therapy. In general, however, the pediatric population includes patients aged birth to 16 years, including age groups often called neonates, infants, children, and adolescents.â€
33. Pediatric Research Equity Act of 2003. Public Law No 108-155 (December 3, 2003). Available at http://www.gpo.gov/fdsys/pkg/PLAW-108publ155/html/PLAW-108publ155.htm.Accessed November 29, 2012.uk/bcs/pdf/eclinical_equation.pdf.
35. Van Bemmel JH, Musen MA. Handbook of Medical Informatics. Heidelberg-New York: Springer Verlag, 1997.
36. In 21 CFR 312.62(b), reference is made to records that are part of case histories as supporting dataâ€; the ICH guidance for industry E6 Good Clinical Practice: Consolidated Guidance (the ICH E6 guidance) (available at http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm
37. Dick RS, Steen EB, Detmer DE. The Computer-based Record: an Essential Technology for Healthcare. 2nd edition. Washington, DC: National Academy Press; 1997.
38. For the principal record keeping requirements for clinical investigators and sponsors developing drugs and biologics, see 21 CFR 312.50, 312.58, 312.62, and 312.68. For medical devices, see 21 CFR 812.140 and 812.145.
39. Food and Drug Regulations, Part C, Division 1, C.R.C., c. 870.
40. Collins FM journal report health care the WSJ report http://www.wsj.com/articles/SB10001424052970204124204577154661814932978.
42. Embi PJ, Tsevat J. Commentary: the relative research unit: providing incentives for clinician participation in research activities. Academic Medicine: Journal of the Association of American Medical Colleges 2012; 87(1): 11-4.
43. FDA website on drug development and drug interactions, http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractions Labeling/ucm080499.htm.
44. Tucker G, Houston JB, Huang SM. Optimizing drug development: strategies to assess drug metabolism/transporter interaction potential â€” toward a consensus. Br J Clin Pharmacol. 2001; 52(1): 107â€“17.
45. Chalmers TC, Smith H Jr, Blackburn B, Silverman B, Schroeder B, Reitman D et al. A method for assessing the quality of a randomized control trial. Controlled Clinical Trials 1981; 2(1): 31â€“49.
46. Moher D, Hopewell S, Schulz KF, Montori V, Gotzsche PC, Devereaux PJ et.al. Consort 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. Br Med J 2010; 340: c869.
47. Pharmacovigilance at the European Medicines Agency Patient Health Protection Pharmacovigilance and Risk Management: www.ema.europa.eu.
48. ICH Topic E2E Pharmacovigilance planning (PvP) June 2005 CPMP/ICH/5716/03.
49. Venulet J. Possible strategies for early recognition of potential drug safety problems. Adverse Drug React Acute Poisoning Rev 1988; 7(1): 39-47.
50. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol drug Saf 2001; 10(6): 483-6.
51. e-Crf Â® EDC Software / EDC software & tools integrated in the same single sign-on Portal http://www.ethical.ch/ecrf/ecrf-main-features/eclinical-portal-services
52. Spink C. Electronic Data Capture (EDC) as a means for e-clinical trial success. IBM Global Services. Pharmaceutical Clinical Development; 2002.
53. Bart T. Comparison of electronic data capture with paper data collection â€” is there really an advantage? Bus Brief Pharmatech 2003:1â€“4.
54. Lu ZW. Technical challenges in designing post-marketing eCRFs to address clinical safety and pharmacovigilance needs. Contemp Clin Trials. 2010; 31: 108â€“18.
55. Forster E. The changing R and D model â€“ What does e-clinical development need to support? Available from: http://www-935.ibm.com/services.
56. Van den Broeck J. Maintaining data integrity in a rural clinical trial. Clin Trials 2007; 4(5): 572-82.
57. Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: A systematic review. Journal of American Medical Informatics Association 2005; 12: 505-16.
58. Iezzoni LI. Assessing quality using administrative data. Ann Intern Med, 1997; 127 : 666-74.
59. Cullen T. Vogelson is a former assistant editor of Modern Drug Discovery. He writes and teaches in northern California. Send your comments or questions regarding this article to firstname.lastname@example.org or the Editorial Office by fax at 202-776-8166 or by post at 1155 16th Street, NW; Washington, DC 20036.
60. Jeannic AL, Quelen C, Alberti C, Durand-Zaleski I. Comparison of two data collection processes in clinical studies: electronic and paper case report forms. BMC Medical Research Methodology 2014; 14: 7-16.
61. Terry NP, Francis LP. Ensuring the Privacy and Confidentiality of Electronic Health Records. University of Illinois law review 2007; 681, 725â€30.
62. Hoffman S, Podgurski A. Securing the HIPAA security rule. J Internet Law 2007; 10: 1-18.
63. DesRoches CM, Dr.PH, Campbell EG, Rao SR, Donelan K, Ferris TG et al. Electronic Health Records in Ambulatory Care - A National Survey of Physicians. N Engl J Med 2008; 359: 50-60.
64. Schmitt KF, Wofford DA. Financial analysis projects clear returns from electronic medical records. HealthcFinanc Manage.2002; 56(1): 52-7.
65. Agarwal A. Return on investment analysis for a computer-based patient record in the outpatient clinic setting. J AssocAead Minor Phys. 2002; 13(3): 61-5.
66. Id. Nonformulary medications are [d]rugs not on a [health care] plan-approved drug list.â€ Medicare.gov â€” Glossary Definitions, http://www.medicare.gov/Glossary/search.asp? SelectAlphabet=N&Language=English#Content (last visited Dec. 19, 2008).
67. Hartzband P, Groopman J. Off the record: avoiding the pitfalls of going electronic. New England Journal of Medicine 2008; 358: 1656-7.
68. Potter-Holden & company on electronic health record: The Advantages and Risks.www.potterholden.com/news/feb09pres.htm.
69. Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005; 293(10): 1197-1203.
70. Rushby J, Henke FV. Formal verification of algorithms for critical systems. IEEE Transactions on Software Engineering 1993; 19(1): 13â€“23.
71. Pharmica consulting- Top five potential drawback of electronic health Records. www.pharmicaconsulting.com.
72. Marks R, Bristol H, Conlon M, Pepine CJ. Enhancing clinical trials on the internet: lessons from invest. Clin Cardiol 2001; 24: 17-23.
The publication is licensed under CC By and is open access. Copyright is with author and allowed to retain publishing rights without restrictions.