A logic model for precision medicine implementation informed by stakeholder views and implementation science.

Purpose: Precision medicine promises to improve patient outcomes, but much is unknown about its adoption within health-care systems. A comprehensive implementation plan is needed to realize its benefits.
Methods: We convened 80 stakeholders for agenda setting to inform precision medicine policy, delivery, and research. Conference proceedings were audio-recorded, transcribed, and thematically analyzed. We mapped themes representing opportunities, challenges, and implementation strategies to a logic model, and two implementation science frameworks provided context.
Results: The logic model components included inputs: precision medicine infrastructure (clinical, research, and information technology), big data (from data sources to analytics), and resources (e.g., workforce and funding); activities: precision medicine research, practice, and education; outputs: precision medicine diagnosis; outcomes: personal utility, clinical utility, and health-care utilization; and impacts: precision medicine value, equity and access, and economic indicators. Precision medicine implementation challenges include evidence gaps demonstrating precision medicine utility, an unprepared workforce, the need to improve precision medicine access and reduce variation, and uncertain impacts on health-care utilization. Opportunities include integrated health-care systems, partnerships, and data analytics to support clinical decisions. Examples of implementation strategies to promote precision medicine are: changing record systems, data warehousing techniques, centralized technical assistance, and engaging consumers.
Conclusion: We developed a theory-based, context-specific logic model that can be used by health-care organizations to facilitate precision medicine implementation.