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Clinical data management: Current status, challenges, and future directions from industry perspectives

Authors Lu Z, Su

Published 22 June 2010 Volume 2010:2 Pages 93—105


Review by Single anonymous peer review

Peer reviewer comments 2

Zhengwu Lu1, Jing Su2

1Smith Hanley Consulting, Houston, Texas; 2Department of Chemical Engineering, University of Massachusetts, Amherst, MA, USA

Abstract: To maintain a competitive position, the biopharmaceutical industry has been facing the challenge of increasing productivity both internally and externally. As the product of the clinical development process, clinical data are recognized to be the key corporate asset and provide critical evidence of a medicine’s efficacy and safety and of its potential economic value to the market. It is also well recognized that using effective technology-enabled methods to manage clinical data can enhance the speed with which the drug is developed and commercialized, hence enhancing the competitive advantage. The effective use of data-capture tools may ensure that high-quality data are available for early review and rapid decision-making. A well-designed, protocol-driven, standardized, site workflow-oriented and documented database, populated via efficient data feed mechanisms, will ensure regulatory and commercial questions receive rapid responses. When information from a sponsor’s clinical database or data warehouse develops into corporate knowledge, the value of the medicine can be realized. Moreover, regulators, payer groups, patients, activist groups, patient advocacy groups, and employers are becoming more educated consumers of medicine, requiring monetary value and quality, and seeking out up-todate medical information supplied by biopharmaceutical companies. All these developments in the current biopharmaceutical arena demand that clinical data management (CDM) is at the forefront, leading change, influencing direction, and providing objective evidence. Sustaining an integrated database or data repository for initial product registration and subsequent postmarketing uses is a long-term process to maximize return on investment for organizations. CDM should be the owner of driving clinical data-cleaning process in consultation with other stakeholders, such as clinical operations, safety, quality assurance, and sites, and responsible for building a knowledge base to add potential value in assisting further study designs or clinical programs. CDM needs to draw on a broad range of skills such as technical, scientific, project management, information technology (IT), systems engineering, and interpersonal skills to tackle, drive, and provide valued service in managing data within the anticipated e-clinical age. Commitment to regulatory compliance is required in this regulated industry; however, a can-do attitude with strong willingness to change and to seek ways to improve CDM functions and processes proactively are essential to continued success and to ensure quality data-driven productivity.

Keywords: clinical trials, data management, standard, efficacy, safety, clinical systems, clinical data, electronic data-capturing

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