The ‘research to policy gap’ describes the failure to translate research findings into real-world, evidence-informed policies. This gap is dangerous to health systems and population health, but dissemination and implementation science (D&I) is poised to address this pervasive problem by designing effective strategies that promote evidence-informed policy and policy implementation success. To be most efficient while advancing science, policy D&I efforts must meaningfully draw from lessons learned in other fields. This presentation will discuss: (1) how different social science disciplines have studied the research to policy gap, (2) how those theories and methods can be incorporated into policy D&I efforts, and (3) highlight an example of a current policy dissemination study at this multidisciplinary intersection. We hope to foster a discussion about challenges in studying policy and policy-level factors, and practical multidisciplinary research approaches to advance policy D&I.

Efficiency is upheld as a cornerstone of high-quality care. Unfortunately, the term efficiency is used in different contexts, creating substantial confusion about what it means. In this talk, I define efficiency as it relates to the delivery of health care. I then review different frameworks for measuring it and common strategies that can be used to improve it.

As evidence-based innovations (EBIs) are translated into clinical and community settings, implementation determinant frameworks can help understand the factors that impede or facilitate implementation outcomes. Use of different frameworks across implementation initiatives can impede shared learning. To develop a framework for shared learning in the DECIPHeR Alliance (decipheralliance.org), we conducted a crosswalk of three determinant frameworks used in the Alliance: 1) Exploration, Preparation, Implementation, and Sustainment (EPIS); 2) Practical, Robust Implementation and Sustainability Model (PRISM); and 3) Consolidated Framework for Implementation Research (updated CFIR). To operationalize health justice and equity considerations, we also incorporated elements of the Health Equity Implementation Framework (HEIF). In this talk, we will discuss the process for developing the integrated framework, called “IM4Equity,” and describe our user-centered feedback process to improve the framework’s understandability and usefulness across the Alliance. IM4Equity can help guide evaluations of EBIs and implementation outcomes across diverse contexts. Compared to any single framework, it has the potential to better identify the myriad of implementation determinants with a focus on health equity considerations. It can also aid in common data elements and cross-project synthesis.

Policy implementation is a key but often-ignored aspect of policy effectiveness. Public policy research typically considers the effects of having versus not having a policy on outcomes, without considering whether and how the policy was implemented – even though the effects of policies on their intended outcome depend upon degree of implementation. Experimental approaches to studying policy dissemination and implementation are challenging, given that policies are difficult to randomly assign, but not impossible. Natural experiments in policy implementation abound, as states, localities, and organizations consider, adopt, and implement – with varying degrees of implementation success, and intensity – a range of policies over time. Rigorous nonexperimental methods for studying policy dissemination and implementation in these types of natural experiments are critical, but methodological advances are needed. This presentation will motivate the importance of studying policy implementation, provide examples of current approaches, and discuss promising future directions.

Drawing upon a strengths-based, cultural assets perspective, I will present evidence on the growth properties of prosocial behaviors and assert the need to incorporate prosocial behaviors as a marker of health and well being. The presentation will focus on risk and protective correlates and consequences of prosocial behaviors in U.S. Latine youth. Implications for prevention and intervention research and programs will be briefly discussed.

This presentation reports the cost-effectiveness analysis from a fully powered randomized clinical trial that featured a head-to-head comparison between telemedicine and digital health interventions for providing continuing care for patients with alcohol use disorder. A telemedicine intervention (telephone monitoring and counseling: TMC) and a mobile health intervention (Alcohol Comprehensive Health Enhancement Support System: A-CHESS) were examined in a 2×2 factorial randomized trial that enrolled 262 participants from two Philadelphia area intensive outpatient programs. Intervention costs and effectiveness (in terms of number of risky drinking days) were assessed for each group with respect to the control group. The effectiveness of reduced days heavy drinking for all 3 treatment groups (TMC, A-CHESS, and TMC+A-CHESS) were statistically significant compared to the control group. However, no treatment group was more effective than the others in terms of statistical significance. Compared to the control group, where participants averaged 43.75 days of heavy drinking over 12 months, participants in the treatment groups had significantly fewer average heavy drinking days over the 12-month intervention (TMC: 15.78 days heavy drinking, P)

FAIR (Families Actively Improving Relationships) is an evidence-based practice for parents referred to the child welfare system. Treatment addresses substance use (primarily opioids and/or methamphetamine), mental health, parenting, and ancillary needs. FAIR is community-based and relies on collaboration with service and community partners. As part of the NIH Helping to End Addiction Long-term (HEAL) initiative, FAIR was adapted for upstream prevention to be evaluated for clinical effectiveness in a Hybrid Type 2 trial in four rural counties. Counties were selected in collaboration with state leadership. A clearly defined implementation process was followed at the county, clinic, and provider levels. Challenges and opportunities related to the outer context of a staggering increase in opioid and methamphetamine use in the participating regions and the COVID-19 pandemic will be highlighted. Implementation process (using the Stage of Implementation Completion) and the resources used to complete it (measured using the Cost of Implementing New Strategies) will be presented in relation to these different contexts.

Equity continues to garner attention in the field of implementation science. Coming from a learner perspective, this presentation will offer some recommendations and suggestions as to how to embed equity in implementation studies, with a focus on processes and outcomes.

Significant investments are being made to close the mental health (MH) treatment gap, which often exceeds 90% in many low- and middle-income countries (LMICs). However, limited attention has been paid to patient quality of care in nascent and evolving LMIC MH systems. In system assessments across sub-Saharan Africa, MH loss-to-follow-up often exceeds 50% and sub-optimal medication adherence often exceeds 60%. This talk will summarize our work on adapting, implementing, and now testing the effectiveness of the Systems Analysis and Improvement Approach for Mental Health (SAIA-MH) implementation strategy to optimize the primary mental healthcare treatment cascade in government health facilities in Mozambique.

Full realization of the societal benefits of our work in implementation science requires high levels of successful sustainment (maintenance) and scale-up/spread of our implementation strategies and the effective practices we strive to implement. This presentation offers a series of questions we should ask, and recommendations for actions we can – and should – take, to enhance our societal contributions as implementation scientists.

Background: Clinicians and community health workers may wish to use digital interventions to reach more patients with unhealthy substance use, optimize costs of care, and improve outcomes. However, digital interventions have unique implementation considerations (e.g., technology infrastructure, digital literacy, monitoring and follow-up) and may not fit traditional care pathways. Effectiveness and implementation trials are needed to understand how well digital interventions work and how to best deploy them in the real-world. This presentation presents a framework to help researchers design their trials in such a way that maximizes scientific understanding. Methods: This framework draws from the literature on trial design, expert perspectives on the use of digital interventions, and lessons learned from implementation science research programs. It outlines three major steps for designing trials of digital interventions: 1) framing the research question; 2) delineating components of the intervention, implementation strategy, and delivery approach; and 3) specifying the experiment and other elements of trial design. Results: In Step 1 of this framework, researchers frame the research question in terms of the goals or activities to be tested (i.e., features of the digital intervention itself, specific implementation strategies, or level of clinical support). In Step 2, researchers define and delineate each study...

The authors of the article, Re-envisioning, Retooling, and Rebuilding Prevention Science, will engage in a conversation to describe the team approach in writing this paper, as well as take away message from this effort. Following this fireside chat format, the authors will engage attendees in a reflective and projective dialogue on how to advance equity and social justice in our own work.

Despite its potentials benefits, using prediction targets generated based on latent variable (LV) modeling is not a common practice in supervised learning, a dominating framework for developing prediction models. In supervised learning, it is typically assumed that the outcome to be predicted is clear and readily available, and therefore validating outcomes before predicting them is a foreign concept and an unnecessary step. The usual goal of LV modeling is inference, and therefore using it in supervised learning and in the prediction context requires a major conceptual shift. This study lays out methodological adjustments and conceptual shifts necessary for integrating LV modeling into supervised learning. It is shown that such integration is possible by combining the traditions of LV modeling, psychometrics, and supervised learning. In this interdisciplinary learning framework, generating practical outcomes using LV modeling and systematically validating them based on clinical validators are the two main strategies. In the example using the data from the Longitudinal Assessment of Manic Symptoms (LAMS) Study, a large pool of candidate outcomes is generated by flexible LV modeling. It is demonstrated that this exploratory situation can be used as an opportunity to tailor desirable prediction targets taking advantage of contemporary science and clinical insights.

Many people hope that evidence — be it lived experience, clinical know how, or scientific research findings – can be used to improve policies towards addictive drugs, addiction, treatment, and recovery. But public policymaking can seem mysterious and irrational, such that many are cynical that evidence can matter. This presentation describes how even though many factors other than evidence shape public policy, evidence can exert some influence and sometimes a great deal of influence. After detailing what counts as evidence and how evidence is different than opinions and values, this presentation walks through actually policy changes in which they author was involved, including the 2008 campaign for mental health and addiction parity in health insurance and the 2010 passage of the Affordable Care Act. Each case study illuminates when evidence mattered and in what way, emphasizing that while evidence can’t and shouldn’t carry the day in a democracy, it has unique value both for the design and passage of policy as well as its implementation.

Evidence-based practices often fail to be implemented or sustained due to barriers at multiple levels of an organization (e.g., system-level, practitioner-level). A growing cadre of implementation strategies can help mitigate challenges at these multiple levels, but significant heterogeneity exists in whether, and to what extent, organizations—and the practitioners who deliver treatment within them—respond to different strategies. However, it is impractical to provide all (or even most) of these strategies to all levels, at all times. This suggests the need for an approach that sequences and adapts the provision of implementation strategies to the changing context and needs of practitioners within the multiple levels of an organization. A multi-level adaptive implementation strategy (MAISY) offers a replicable, approach to precision implementation that guides implementers in how best to adapt and re-adapt (e.g., augment, intensify, switch) implementation strategies based on the changing context and changing needs at multiple levels.

This presentation will introduce the new Center for Dissemination and Implementation At Stanford (C-DIAS), a NIDA P50 Center of Excellence. C-DIAS’ overarching mission is to expand equitable access to the most effective treatments available for addiction. It unites experts from implementation science and addiction treatment services research, and hosts three innovative, synergistic research projects at the PREPARATION, IMPLEMENTATION and SUSTAINMENT phases of the implementation process. In addition, C-DIAS aims to increase the expert capacity of D&I science in addiction, and will offer a stratified range of education, training and mentoring opportunities based on need. Audience will learn how to access C-DIAS open resources and programs, and understand how research findings can be integrated across the 3 C-DIAS research projects, as well as other D&I investigations with an addiction content focus.