data, considered another linchpin of progress. This chapter outlines some policy levers that can drive innovative research and progress in practice-based approaches as well as the potential roles that various healthcare stakeholders can play to accelerate progress.
Focused on course-of-care data, Greg Pawlson of the National Committee for Quality Assurance describes a major opportunity to use these clinical data for “rapid learning.” By capturing the experience of each patient and clinician in a structured and quantifiable manner, EHR systems have great potential to help transform our capacity to develop information that can be used as important evidence in making clinical decisions. Policy interventions will play a crucial role in improving the development of and access to databases that are suitable for clinical effectiveness research. With product approval increasingly tied to postmarket trial or database commitments to demonstrate the value of treatments, health product developers also are contending with a variety of issues related to the development and use of data for clinical effectiveness analyses. Merck’s Peter K. Honig discusses several key challenges that manufacturers face in responding to these demands. Those challenges include finding a suitable balance between demands for data transparency and maintaining competitive advantage, and improving the methods used to develop clinical effectiveness information.
Recognizing that the scope and scale of existing and future evidence gaps exceed any one entity’s capacity to address all of the needs related to improving evidence availability and application to improve practice, Mark B. McClellan of the Brookings Institution advocates that other approaches also are needed. These approaches should take better advantage of regulatory data that offers a rich opportunity to improve our knowledge base. McClellan cites the Food and Drug Administration Amendment Act of 2007 (FDAAA) and the Medicare Coverage with Evidence Development policy as models for how regulatory data can be integrated successfully into the ongoing capacity to develop better evidence on what works and, in turn, inform medical practice. Another speaker, J. Sanford Schwartz of the University of Pennsylvania, acknowledges that large amounts of data generated and supported by public investment provide innovative opportunities to inform clinical and comparative effectiveness assessment, but that substantial barriers must be passed for optimal use of these data. Schwartz offers a series of suggestions to mitigate the following paradox: We have large amounts of data and significant opportunities, but we are prevented from fully accessing the data and taking advantage of potential opportunities. In view of the reality that evidence-based medicine (EBM) requires integration of clinical expertise and research and depends on an infrastructure