Implementing Pharmacogenomics in Diverse Health Care Systems

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Project Summary There is a growing evidence base to support use of genetic information to optimize prescribing decisions; how- ever, implementation has been slow relative to discovery. Health care delivery systems need pragmatic tools to facilitate use of genetic information for prescribing both within and across clinical settings with anticipated growth of this innovation. The long-term goal is to support precision medicine to improve health services and outcomes at a population level. The overall objective is to identify best practices for health systems to use or integrate (i.e., to implement) genomic information within real-world clinical care settings. The rationale for the proposed research is to identify determinants, strategies, and causal pathways in complex, real-world settings in order to develop evidence-based guidance for implementation. This R21 includes two specific aims: 1) Iden- tify determinants and strategies for implementation of genotype-guided prescribing at the institutional level and 2) Determine causal pathways, i.e., how implementation determinants and strategies work together to affect adoption of genotype-guided prescribing by providers. Our sample will include institutions that have imple- mented genotype-guided prescribing to varying degrees (e.g., beginning phase, preemptive policy) outside of academic research funding. For the first aim, data from semi-structured, in-person interviews with multiple stakeholders (administrators, providers, patients) will be elicited and integrated to obtain their perspectives about factors that affect implementation. Interview questions will primarily derive from two implementation de- terminant frameworks, the Theoretical Domains Framework (TDF) and the Consolidated Framework for Imple- mentation Research (CFIR). In the analysis phase, implementation strategies described by respondents will be identified using the Expert Recommendations for Implementing Change (ERIC) compilation. For the second aim, a novel method, Configurational Analysis (CNA), will be applied to data from Aim 1 to reveal what combi- nations of factors make a difference for provider adoption of pharmacogene test orders. CNA uses a mathe- matical approach to identify minimally necessary and sufficient conditions for intermediate outcomes, such as implementation strategies, and desired endpoints, such as pharmacogene test orders, and has not yet been applied to evaluate implementation of genotype-guided prescribing. Qualitative data from Aim 1 will additionally be used to interpret findings. These qualitative and quantitative data are essential for the selection of imple- mentation strategies and tailoring to different settings. In the short-term, this research will enhance tools for implementing genotype-guided prescribing that have been developed by the NIH Consortium Implementing Genomics into Practice (IGNITE). In the longer-term it will provide foundational information for an implementa- tion trial, in which we could assess relative costs and effects of protocols for implementing genotype-guided prescribing under varying conditions. This contribution is expected to be significant because prescribing and outcomes could be optimized if genetic testing were more widely and appropriately deployed in clinical care.