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. 2014 Mar;16(3):251-7.
doi: 10.1038/gim.2013.122. Epub 2013 Oct 17.

The Economic Value of Personalized Medicine Tests: What We Know and What We Need to Know

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The Economic Value of Personalized Medicine Tests: What We Know and What We Need to Know

Kathryn A Phillips et al. Genet Med. .
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Purpose: There is uncertainty about when personalized medicine tests provide economic value. We assessed evidence on the economic value of personalized medicine tests and gaps in the evidence base.

Methods: We created a unique evidence base by linking data on published cost-utility analyses from the Tufts Cost-Effectiveness Analysis Registry with data measuring test characteristics and reflecting where value analyses may be most needed: (i) tests currently available or in advanced development, (ii) tests for drugs with Food and Drug Administration labels with genetic information, (iii) tests with demonstrated or likely clinical utility, (iv) tests for conditions with high mortality, and (v) tests for conditions with high expenditures.

Results: We identified 59 cost-utility analyses studies that examined personalized medicine tests (1998-2011). A majority (72%) of the cost/quality-adjusted life year ratios indicate that testing provides better health although at higher cost, with almost half of the ratios falling below $50,000 per quality-adjusted life year gained. One-fifth of the results indicate that tests may save money.

Conclusion: Many personalized medicine tests have been found to be relatively cost-effective, although fewer have been found to be cost saving, and many available or emerging medicine tests have not been evaluated. More evidence on value will be needed to inform decision making and assessment of genomic priorities.

Conflict of interest statement


The authors declare no conflict of interest.


Figure 1
Figure 1. Overview of data sources
AHRQ, Agency for Healthcare Research and Quality; CDC, Centers for Disease Control and Prevention; CEAR, Cost-Effectiveness Analysis Registry; FDA, Food and Drug Administration; GAPP, Genomic Applications in Practice and Prevention; NVS, National Vital Statistics.
Figure 2
Figure 2
Distribution of ratios of cost per QALY gained for personalized medicine tests. QALY, quality-adjusted life year.
Figure 3
Figure 3. Percentage of tests with published cost–utility analyses
FDA, Food and Drug Administration.

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