treatments, diagnostic technologies, and care delivery models. As currently structured, clinical trials often are not comparable, so that a new trial must be conducted to compare the effectiveness of new treatments, diagnostics, or care delivery models with that of existing ones. One solution to this problem is to create standard comparators for a given disease or clinical condition, which would allow new innovations to be compared easily using existing data for current treatments or diagnostic technologies. Additionally, as the research enterprise is expanded, additional emphasis may be required in fields that are underserved by the current clinical research paradigm, such as pediatrics (Cohen et al., 2007; IOM, 2009c; Simpson et al., 2010). One exception to this observation is pediatric cancer care. Virtually all of the treatment provided in pediatric oncology is recorded and applied to registries or active clinical trials, which then inform future care for children undergoing treatment (IOM, 2010b; Pawlson, 2010).
In considering how to take advantage of opportunities to create a more nimble, timely, and targeted clinical research enterprise, three basic questions should be considered: (1) What does the system need to know? (2) How will the information be captured and used? and (3) How will the resulting knowledge be organized and shared? These questions have important ramifications for the design and operation of the overall data system.
With respect to the first question, stakeholders in the health care system are interested in comparing the effectiveness of different treatments and interventions, monitoring the current safety of medical products through surveillance, undertaking quality improvement activities, and understanding the quality and performance of different providers and health care organizations. Achieving these goals will require capturing data on the care that is delivered to patients, such as processes and structures of care delivery, and the outcomes of that care, such as longitudinal health outcomes and other outcomes important to patients. With respect to how these data will be used to generate new health care knowledge, uses will include comparing the effects of different treatments, interventions, or care protocols; establishing guidelines and best practices; and searching for unexpected effects of treatments or interventions. Finally, the new knowledge generated will have little impact if not shared broadly with all involved in delivering care for a given patient or, for research cases, all those involved in research. Each of these three questions is explored in further detail below.