Preemptive pharmacogenetic testing: exploring the knowledge and perspectives of US payers
Purpose Preemptive pharmacogenetic testing aims to optimize medication use by having genetic information at the point of prescribing. Payers’ decisions influence implementation of this technology. We investigated US payers’ knowledge, awareness, and perspectives on preemptive pharmacogenetic testing...
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Veröffentlicht in: | Genetics in medicine 2019-05, Vol.21 (5), p.1224-1232 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Purpose
Preemptive pharmacogenetic testing aims to optimize medication use by having genetic information at the point of prescribing. Payers’ decisions influence implementation of this technology. We investigated US payers’ knowledge, awareness, and perspectives on preemptive pharmacogenetic testing.
Methods
A qualitative study was conducted using semistructured interviews. Participants were screened for eligibility through an online survey. A blended inductive and deductive approach was used to analyze the transcripts. Two authors conducted an iterative reading process to code and categorize the data.
Results
Medical or pharmacy directors from 14 payer organizations covering 122 million US lives were interviewed. Three concept domains and ten dimensions were developed. Key findings include clinical utility concerns and limited exposure to preemptive germ-line testing, continued preference for outcomes from randomized controlled trials, interest in guideline development, importance of demonstrating an impact on clinical decision making, concerns of downstream costs and benefit predictability, and the impact of public stakeholders such as the Food and Drug Administration and Centers for Medicare and Medicaid Services.
Conclusion
Both barriers and potential facilitators exist to developing cohesive reimbursement policy for pharmacogenetics, and there are unique challenges for the preemptive testing model. Prospective outcome studies, more precisely defining target populations, and predictive economic models are important considerations for future research. |
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ISSN: | 1098-3600 1530-0366 |
DOI: | 10.1038/gim.2017.181 |