文章基本信息

标题：Accelerating Regulatory Progress in Multi-Institutional Research
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作者：Paolino, Andrea R ; Lauf, Sherry L ; Pieper, Lisa E 等
期刊名称：eGEMs (Generating Evidence & Methods to improve patient outcomes)
印刷版ISSN：2327-9214
出版年度：2014
卷号：2
期号：1
页码：10
出版社：EDM Forum
摘要：Purpose: Multi-institutional collaborations are necessary in order to create large and robust data sets that are needed to answer important comparative effectiveness research (CER) questions. Before scientific work can begin, a complex maze of administrative and regulatory requirements must be efficiently navigated to avoid project delays. Innovation: Staff from research, regulatory, and administrative teams involved in three HMO Research Network (HMORN) multi-institutional collaborations developed and employed novel approaches: to secure and maintain Institutional Review Board (IRB) approvals; to enable data sharing, and to expedite subawards for two data-only minimal risk studies. These novel approaches accelerated required processes and approvals while maintaining regulatory, human subjects, and institutional protections. Credibility: Outcomes from the processes described here are compared with processes outlined in the research and regulatory literature and with processes that have been used in previous multisite research collaborations. Conclusion and Discussion: Research, regulatory, and administrative staff are essential contributors to the success of multi-institutional collaborations. Their flexibility, creativity, and effective communication skills can lead to the development of efficient approaches to achieving the necessary oversight for these complex projects. Elements of these specific strategies can be adapted and used by other research networks. Other efforts in these areas should be evaluated and shared. The processes that help develop a “learning research system” play an important and complementary role in sustaining multi-institutional research collaborations.
关键词：Data agreement; Institutional Review Board; Subcontract; Subaward; Administrative Efficiency