Integrating Decision Science and Implementation Science to Inform Policy Decision Making

Decision science and implementation science share the common goal of improving individual and population health through choosing and providing effective health innovations at scale. In this article, we summarize a symposium we hosted at the 45th Annual Society for Medical Decision Making North American meeting. The symposium aimed to illustrate how integrating implementation science and decision science can strengthen the real-world impact and practical utility of decision science methods. The symposium was attended by 51 individuals. It included 4 presentations by early-career researchers and a moderated discussion. Presentations covered innovative work at the intersection of implementation science and decision analytic modeling and focused on policy applications because of the presenters' expertise and strong history of decision analytic modeling to inform policy decisions. The symposium's moderated discussion indicated a need for developing collaborations between implementation and decision scientists to move this type of work forward. Suggested areas of future research are modeling to identify gaps in data and considering value-of-information methods, exploring how implementation could be incorporated into simulation methods beyond those discussed in the symposium (e.g., distributional and extended cost-effectiveness analyses), and integrating implementation science into other areas of decision science (e.g., preference and prioritization research, shared decision making). We urge decision science researchers to pursue interdisciplinary research integrating decision and implementation science to best inform policy decision making and drive the scale-up of promising policies across contexts.

Implementation science concepts could strengthen the external validity and uptake of decision science methods such as decision analytic models.The symposium discussed and highlighted innovative ways that decision science researchers could integrate implementation science frameworks and outcomes (e.g., cost, reach, fidelity) into decision analytic models to be more responsive to the multifaceted considerations of policy decision making.Supporting interdisciplinary networking and collaboration between decision scientists and implementation scientists is critical to strengthen the real-world impact and practical utility of decision science methods.