As discussed above, there is a clear need for new approaches to knowledge generation, management, and application to guide clinical care, quality improvement, and delivery system organization. The current clinical research enterprise requires substantial resources and takes significant time to address individual research questions. Moreover, the results provided by these studies do not always generate the information needed by patients and their clinicians and may not always be generalizable to a larger population. New research methods are needed that address these serious limitations. Developments in information technology and research infrastructure have the potential to expand the ability of the research system to meet this need. For example, the anticipated growth in the adoption of digital records presents an unprecedented opportunity to expand the supply of data available for learning, generating insights from the regular delivery of care (see the discussion of the data utility in the next section for further detail on these opportunities). These new developments can increase the output derived from the substantial clinical research investments of agencies and foundations, including the Agency for Healthcare Research and Quality (AHRQ), the National Institutes of Health (NIH), and PCORI.
New tools are extending research methods and overcoming many of the limitations highlighted in the previous section (IOM, 2010a). The scientific community has recognized the need for change. High-profile efforts—including NIH’s Clinical and Translational Science Awards and the U.S. Food and Drug Administration’s Clinical Trials Transformation Initiative—have been undertaken to improve the quality, efficiency, and applicability of clinical trials, and new translational research paradigms have been developed (Lauer and Skarlatos, 2010; Luce et al., 2009; Woolf, 2008; Zerhouni, 2005). Based on these efforts and the work of academic research leaders, new forms of experimental designs have been developed, including pragmatic clinical trials, delayed design trials, and cluster randomized controlled trials2 (Campbell et al., 2007; Eldridge et al., 2008; Tunis et al., 2003, 2010). Other new methods have been devised to develop knowledge from data produced during the regular course of care. Initial results derived with these new methods have shown promise (see Box 6-3 for a description of one new method). Advanced statistical methods, including Bayesian analysis, allow for adaptive research designs that can learn as a study advances, making studies more flexible (Chow and Chang, 2008). Taken together,
2In pragmatic clinical trials, the questions faced by decision makers dictate the study design (Tunis et al., 2003b). In delayed design trials, participants are randomized to either receive the intervention or have it withheld for a period of time, with both groups receiving the intervention by the end of the study (Tunis et al., 2010). In cluster randomized controlled trials, groups of subjects, rather than individual subjects, are randomized (Campbell et al., 2007).