Tobacco smoking represents the single largest contributor to premature mortality in people with serious mental illness (SMI). Although effective, evidence-based smoking cessation treatment exists, fewer than 1 in 10 smokers with SMI receive it. One of the keys to scaling cessation treatment is to identify implementation strategies that can effectively address implementation determinants that facilitate or hinder implementation efforts. However, the process of identifying strategies appropriately matched to determinants can be challenging, with opportunities to test possible combinations and variations of strategies largely limited to costly and time-intensive randomized implementation trials. In this study, we propose a novel application of systems science methods to identify implementation strategies to facilitate the delivery of smoking cessation treatment in community mental health settings. Systems science is an interdisciplinary field of science focused on modeling complex systems which can be used to simulate the potential effects of implementation strategies on outcomes prior to implementation. The proposed study seeks to build on our Center’s existing systems modeling work to develop a system model that will serve as a “virtual laboratory” to explore the potential effects of various implementation strategies on the delivery of cessation treatment for people with SMI. To do so, we will leverage our team’s complementary sets of expertise, theory-driven constructs of an implementation science framework, and an extensive array of data sources. In Aim 1, we will iteratively engage with community partners to develop and refine a conceptual model that identifies candidate implementation strategies that can increase delivery of smoking cessation treatment in community mental health settings. Using the conceptual model as a foundation, in Aim 2, we will build an integrated agent-based and system dynamics model to assess the impacts of different implementation strategies on delivery of cessation treatment. The integrated system model will be calibrated using robust empirical data from our team’s prior studies on cessation treatment for people with SMI, new data that will be collected as part of the proposed research, and a literature review of anticipated effect sizes of candidate implementation strategies. In Aim 3, we will follow a User-Centered Design approach to construct an interactive, online dashboard that displays the results of the system model. The purpose of the dashboard will be to assist community partners with decision- making on how to implement and scale-up cessation treatment by allowing users to “virtually test” how adjustments in the selection, dosing, and combination of implementation strategies would potentially affect intervention delivery. This innovative study will result in a system model that will advance our efforts to support scaling of smoking cessation treatment in community mental health settings, including the design...