this chapter describes emerging capacities, methods, and approaches that hold promise for helping to meet this need. It then examines what is necessary to create the data utility that will be essential to a continuously learning and improving health care system. Next, the critical issue of building a learning bridge from knowledge to practice is explored. This is followed by a discussion of the crucial role of people, patients, and consumers as active stakeholders in the learning enterprise. The chapter concludes with recommendations for achieving the vision of a health care system that generates and applies knowledge in real time.


The current approach to generating new medical knowledge falls short in delivering the evidence needed to support the delivery of quality care. The evidence base is inadequate, and methods for generating medical knowledge have notable limitations.

Inadequacy of the Evidence Base

Clinical and biomedical research emerges at a remarkable rate, with almost 2,100 scientific publications, 75 clinical trials, and 11 systematic reviews being produced every day (Bastian et al., 2010).1 Although clinicians need not review every study to provide high-quality care, the ever-increasing volume of evidence makes it difficult to maintain a working knowledge of new clinical information.

Even so, however, the availability of such high-quality evidence is not keeping pace with the ever-increasing demand for clinical information that can help guide decisions on different diagnostics, interventions and therapies, and care delivery approaches (see Box 6-1 for an example of this information paradox). Rather, the gap between the evidence possible and the evidence produced continues to grow, and studies indicate that the number of guideline statements backed by evidence is not at the level that should be expected. In some cases, 40 to 50 percent of the recommendations made in guidelines are based


1The number of journal publications was determined from searches on PubMed for 2010 (National Library of Medicine: using the methodology described in Chapter 2.

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