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Accelerating innovation translation is a priority for improving healthcare and health. Although dissemination and implementation (D&I) research has made significant advances over the past decade, it has attended primarily to the implementation of long-standing, well-established practices and policies. We present a conceptual architecture for speeding translation of promising innovations as candidates for iterative testing in practice. Our framework to Design for Accelerated Translation (DART) aims to clarify whether, when, and how to act on evolving evidence to improve healthcare. We view translation of evidence to practice as a dynamic process and argue that much evidence can be acted upon even when uncertainty is moderately high, recognizing that this evidence is evolving and subject to frequent reevaluation. The DART framework proposes that additional factors – demand, risk, and cost, in addition to the evolving evidence base – should influence the pace of translation over time. Attention to these underemphasized factors may lead to more dynamic decision-making about whether or not to adopt an emerging innovation or de-implement a suboptimal intervention. Finally, the DART framework outlines key actions that will speed movement from evidence to practice, including forming meaningful stakeholder partnerships, designing innovations for D&I, and engaging in a learning health system.
This chapter reviews the family studies supporting the role of genetics and recent molecular genetic results. Mapping studies using linkage and association methods have had modest success to date despite difficulties in replication between studies. Linkage studies have shown the best support for chromosomal regions: 6q, 8q, 9p, 13q, 14q, and 22q. Several candidate genes first identified in studies of schizophrenia have shown reproducible association in bipolar disorder. Genome-wide association studies (GWAS) have been successful in identifying a few genes with small effects on risk. The data overall suggest a high level of both genic and allelic heterogeneity, as well as, a complex mode of inheritance. The coming availability of economical whole genome sequencing promises availability of complete genomic information. This, and large samples now being collected, may provide the datasets necessary to unravel the genetic complexities of this illness.
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