Literature DB >> 11018562

Reengineering a database for clinical trials management: lessons for system architects.

C A Brandt1, P Nadkarni, L Marenco, B T Karras, C Lu, L Schacter, J M Fisk, P L Miller.   

Abstract

This paper describes the process of enhancing Trial/DB, a database system for clinical studies management. The system's enhancements have been driven by the need to maximize the effectiveness of developer personnel in supporting numerous and diverse users, of study designers in setting up new studies, and of administrators in managing ongoing studies. Trial/DB was originally designed to work over a local area network within a single institution, and basic architectural changes were necessary to make it work over the Internet efficiently as well as securely. Further, as its use spread to diverse communities of users, changes were made to let the processes of study design and project management adapt to the working styles of the principal investigators and administrators for each study. The lessons learned in the process should prove instructive for system architects as well as managers of electronic patient record systems.

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Year:  2000        PMID: 11018562     DOI: 10.1016/s0197-2456(00)00070-2

Source DB:  PubMed          Journal:  Control Clin Trials        ISSN: 0197-2456


  11 in total

1.  YMD: a microarray database for large-scale gene expression analysis.

Authors:  Kei-Hoi Cheung; Kevin White; Janet Hager; Mark Gerstein; Valerie Reinke; Kenneth Nelson; Peter Masiar; Ranjana Srivastava; Yuli Li; Ju Li; Hongyu Zhao; Jinming Li; David B Allison; Michael Snyder; Perry Miller; Kenneth Williams
Journal:  Proc AMIA Symp       Date:  2002

2.  Metadata-driven ad hoc query of patient data: meeting the needs of clinical studies.

Authors:  Aniruddha M Deshpande; Cynthia Brandt; Prakash M Nadkarni
Journal:  J Am Med Inform Assoc       Date:  2002 Jul-Aug       Impact factor: 4.497

3.  TrialDB: A web-based Clinical Study Data Management System.

Authors:  C A Brandt; A M Deshpande; C Lu; G Ananth; K Sun; R Gadagkar; R Morse; C Rodriguez; P L Miller; P M Nadkarni
Journal:  AMIA Annu Symp Proc       Date:  2003

4.  Managing complex change in clinical study metadata.

Authors:  Cynthia A Brandt; Rohit Gadagkar; Cesar Rodriguez; Prakash M Nadkarni
Journal:  J Am Med Inform Assoc       Date:  2004-06-07       Impact factor: 4.497

5.  Guidelines for the effective use of entity-attribute-value modeling for biomedical databases.

Authors:  Valentin Dinu; Prakash Nadkarni
Journal:  Int J Med Inform       Date:  2006-11-13       Impact factor: 4.046

6.  The Common Data Elements for cancer research: remarks on functions and structure.

Authors:  P M Nadkarni; C A Brandt
Journal:  Methods Inf Med       Date:  2006       Impact factor: 2.176

7.  Temporal query of attribute-value patient data: utilizing the constraints of clinical studies.

Authors:  Aniruddha M Deshpande; Cynthia Brandt; Prakash M Nadkarni
Journal:  Int J Med Inform       Date:  2003-04       Impact factor: 4.046

8.  Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data.

Authors:  Gang Luo; Lewis J Frey
Journal:  IEEE J Biomed Health Inform       Date:  2015-01-15       Impact factor: 5.772

Review 9.  The Internet and clinical trials: background, online resources, examples and issues.

Authors:  James Paul; Rachael Seib; Todd Prescott
Journal:  J Med Internet Res       Date:  2005-03-16       Impact factor: 5.428

10.  Could an open-source clinical trial data-management system be what we have all been looking for?

Authors:  Greg W Fegan; Trudie A Lang
Journal:  PLoS Med       Date:  2008-03-04       Impact factor: 11.069

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