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  The intersection between genomics and personalized medicine is in an early stage. Challenges to applying the principles of evidence-based medicine (EBM) to the assessment of genomics include the rapid pace of technology advancement, the rarity of most monogenic conditions, and the complexity of human genetic variation underlying common conditions.

  
  
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  Analytic validity, clinical validity, and clinical utility are important concepts related to the performance of molecular diagnostic testing. Analytic validity describes the ability of a test to reliably detect the presence of a given genomic variation or biomarker, whereas clinical validity describes the relationship of the variant or maker with a trait or disease. Clinical utility is a term describing the ability of a genomic technology to improve health outcomes.

  
  
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  The United States Preventive Services Task Force (USPSTF) and Evaluation of Genomic Applications in Practice and Prevention (EGAPP) are developers of rigorous evidence-based guidelines for genomic tests and interventions. Insurer technology assessment groups, professional societies, and disease advocacy organizations are also valuable sources of evidence-based recommendations; however, the rigor of the methodology employed in guideline development and the evidence base supporting these recommendations can be variable.

The intersection of genomic technologies with mainstream health care is growing exponentially. This is occurring at a time where there is a tremendous amount of scrutiny regarding healthcare costs. This is particularly true from public funders (particularly state Medicaid programs) where budget shortfalls are prompting elimination of some traditional services. It is not surprising that health professionals, insurers, and the public are viewing all new technologies through a lens of value—that is, the extent to which resources invested yield measurable improvements in health outcomes. The paradigm of EBM offers approaches for evaluating the benefits and harms of any new technology entering health care, and increasingly these precepts are being applied to evaluate genomic technologies.

This definition of EBM, penned by a founding father of the movement, provides a brief grounding in the major tenets of the field. As a discipline, EBM affords a rigorous approach for ordering, digesting, assessing, and succinctly disseminating the vast amount of knowledge available regarding healthcare technologies. Functionally, EBM is often employed to dissect a disparate knowledge base to yield a dichotomous yes or no answer regarding the value of using a particular technology for a well-defined purpose in health care. EBM is a relatively new field; many of the concepts and tools of EBM were pioneered by individuals at McMaster University in the late 1980s. In the United States, perhaps the pre-eminent organization basing its work on EBM is the United States Preventive Services Task Force (USPSTF). The approaches pioneered by groups like the USPSTF and the Cochrane Collaboration have been adopted by a wide range of professional societies, insurer technology assessment groups, and governmental bodies worldwide. The output of rigorous EBM evaluations of new technologies has served as the substrate for planning for biomedical research, clinical guideline development, regulatory determinations and reimbursement decisions. In the 21st century, it seems unlikely that any major health professional organization in the United States would not state that it adheres to the tenets of EBM to the greatest extent possible. Since the advent of EBM there have been growing pains between EBM and emerging biomedical technologies. Genomics and personalized medicine offers no exception to this. However, it cannot be overemphasized that in no way is Dr. Sackett’s definition of EBM contrary to the precepts of genomics and personalized medicine.

Those that ascribe to the Western medical tradition will agree that healthcare providers should strive to combine the most up-to-date scientific information for a given condition with an understanding of the characteristics of the patient before them to make logically defensible decisions to optimize health outcomes. Likewise, most would agree that, when ethically and economically feasible, all new technologies should be subject to rigorous clinical trials designed to measure health outcomes such as morbidity and mortality (so-called “patient-oriented evidence”). EBM holds that all clinical trials are not created equal, and that the gold standard for evidence is the double-blind, placebo-controlled randomized trial. According to the precepts of EBM, the ideal evidence base supporting clinical decisions rests on meta-analysis of multiple well-designed trials conducted on a sufficient number of individuals of diverse biologic and socioeconomic backgrounds to be assured that the results are applicable to the patient in question. Such high standards have been met for a wide variety of types of health interventions (Table 176-1). To date there are only a few genomic interventions that have met such a high evidentiary bar.