Review and special article
An Evidence Integration Triangle for Aligning Science with Policy and Practice

https://doi.org/10.1016/j.amepre.2012.02.016Get rights and content

Abstract

Over-reliance on decontextualized, standardized implementation of efficacy evidence has contributed to slow integration of evidence-based interventions into health policy and practice. This article describes an “evidence integration triangle” (EIT) to guide translation, implementation, prevention efforts, comparative effectiveness research, funding, and policymaking. The EIT emphasizes interactions among three related components needed for effective evidence implementation: (1) practical evidence-based interventions; (2) pragmatic, longitudinal measures of progress; and (3) participatory implementation processes. At the center of the EIT is active engagement of key stakeholders and scientific evidence and attention to the context in which a program is implemented. The EIT model is a straightforward framework to guide practice, research, and policy toward greater effectiveness and is designed to be applicable across multiple levels—from individual-focused and patient–provider interventions, to health systems and policy-level change initiatives.

Introduction

Translation of research evidence to widespread application in practice has variously been conceptualized as a linear process—a “pipeline” or “roadmap” that unfortunately is slow, uncertain, and incomplete.1, 2 The dominant conceptualizations of translation of science into practice begin with research products developed by investigators, and then go through various sequential steps to the eventual routine use by practitioners. This type of scientific evidence, however, developed in isolation from its projected users, often fits uncomfortably in the settings and populations where it is intended to be applied. The art of policy and practice involves reconciling the strength of published evidence with its relevance based on the experience of those who know, live, and work with the problem that the evidence is designed to solve.3

The Roadmap for Medical Research by the NIH4 suggests a progression from T1 research (basic discovery) to T2 research (evaluation of efficacy). Recent contributions have expanded this to T3 research (evaluation of implementation in practice) and T4 research (assessing the impact on population health).5, 6 T1 and T2 research, with their emphasis on bringing basic research to clinical trials, dominate biomedical funding but are not enough. The current complex health and healthcare challenges require complex, multilevel solutions tailored to the specific settings in which they are applied.7, 8 The limited effect of research on population health argues for increasing the current low levels of investment in T3 and T4 investigation to enhance the success of prevention and implementation science. The research, policy, and funding communities cannot keep relying on the same highly controlled efficacy research, pushed into the same unidirectional and leaky implementation pipeline, while expecting different outcomes.9, 10

To increase the relevance, application, and impact of scientific investigation, researchers, practitioners, community members, and policymakers need a straightforward and systematic way to understand the pathway from research discovery to population health outcomes.11 Evidence, practice, and policy must begin with the end goal in mind to foster adoption, implementation adaptation, and sustainability.12, 13 The traditional linear approach to research translation has been critiqued by many, including the authors, but few clear, feasible alternatives have been proposed.7, 10, 14, 15

Several research translation models have been employed productively, but they are often found to be too complex, academic or time-consuming for clinicians, community members, and health systems.16, 17, 18 The present paper describes a three-pronged model called the Evidence Integration Triangle (EIT) (Figure 1) that captures essential dimensions of an effective interaction between research and its practice/policy translation. The EIT builds on, and attempts to distill, the critical elements of these important predecessor models. It is designed to be more intuitive and readily applied by stakeholders, including practitioners, policymakers, and citizens to foster high-impact knowledge implementation by research–practitioner–community partnerships.

The purposes of the current paper are to (1) describe the EIT as a model to help optimize practice through research evidence and speed integration of science, policy, and practice19; (2) suggest practical actions and keys to success within and across the three domains; (3) provide examples of application of the EIT; and (4) discuss implications for researchers, practitioners, and policymakers.

Section snippets

The Framework

The EIT depicts in a simple framework the complex multilevel contextual factors affecting the integration of scientific knowledge into practical applications. As shown in Figure 1, bringing together evidence and relevant stakeholders is central. Interactions among the three main evidence-based components—intervention program/policy, implementation processes, and measures of progress—empower these stakeholders to use scientific evidence to maximize positive health impact and value and encourage

Multilevel Context and Interactions Among Components of the Evidence Integration Triangle

Each of the individual components of the EIT—evidence-based intervention, practical longitudinal assessment, and a partnership implementation approach—becomes necessary, but not sufficient, for successful integration of research, practice, and policy. The specific elements of the EIT require attention if research is to influence practice in ways that improve population health. This paying attention45 involves iterating between the big picture and the particulars of the multilevel context,8, 24

Opportunities to Apply the Evidence Integration Triangle to Improve Prevention and Health Care

If national policymakers continue to require that state and local programs be evidence based, even when such evidence does not exist, then the evidence to be considered must be expanded to take the implementation and partnership processes into account. Recommendations also must emphasize not just “best practices” from evidence-based reviews of controlled trials, but also “best processes” of assessing needs, joint decision making, planning, management, and ongoing evaluation in partnership with

Rapid Learning Organizations

One important implication of the ongoing and iterative nature of the EIT is that it fosters the creation of rapid learning organizations.31, 53, 54 Keeping the EIT components and the larger context in view over time results in an ongoing cycle of knowledge generation, implementation, and measurement.23, 31 This iterative process can be entered at any point in the triangle. For example, the intervention and evaluation design considerations become modified by assessments of progress; learning

Public Health and Policy Opportunities

After more than a decade of following the hierarchy of evidence-based medicine,63 systematic reviews of community preventive services and lifestyle interventions frequently found a relative paucity of evidence, and often an impossibility of conducting RCTs on populations, leading repeatedly to conclusions of “insufficient evidence.”64 The urgency of action needed in the face of epidemics in HIV/AIDS, H1N1 influenza virus, food-borne diseases, and obesity has forced a greater appreciation of the

Research Applications

To provide the information needed to apply the EIT, research methods need to be more rapid, practical, transparent, and relevant to stakeholders. These suggestions are congruent with recent movements supported by the Agency for Healthcare Research and Quality (AHRQ) as practical trials,66 and by the Consolidation of Standards for Reporting Trials (CONSORT) working group on pragmatic trials.67 These groups, along with the new Patient-Centered Outcomes Research Institute (www.pcori.org),

Discussion

The EIT framework suggests several testable hypotheses that could inform implementation science. One key hypothesis is that programs that incorporate all three evidence-based components of (1) an effective program collaboratively selected and adapted; (2) practical longitudinal measures for rapid feedback on progress; and (3) true partnership approaches to implementation that pay attention to contextual factors should be superior to programs that focus on fewer components. A more subtle

Conclusion

Many of the needs for prevention, health care, and population health solutions involve complex problems in complex community and healthcare environments, faced by complex patients, settings, and cultures. These challenges demand complex interventions, which are unlikely to be immediately successful when initially applied.83 Application of the EIT, and approaching improvement efforts as complex adaptive systems,84 can help guide us toward solutions to these “wicked problems.”47

Addressing the EIT

References (84)

  • L.W. Green

    The Prevention Research Centers as models of practice-based evidence: two decades on

    Am J Prev Med

    (2007)
  • D.J. Cohen et al.

    Fidelity versus flexibility: translating evidence-based research into practice

    Am J Prev Med

    (2008)
  • K.C. Stange et al.

    Sustainability of a practice-individualized preventive service delivery intervention

    Am J Prev Med

    (2003)
  • R.P. Harris et al.

    Current methods of the U.S. Preventive Services Task Force: a review of the process

    Am J Prev Med

    (2001)
  • E.B. Fisher et al.

    Behavior matters

    Am J Prev Med

    (2011)
  • E.P. Whitlock et al.

    Evaluating primary care behavioral counseling interventions: an evidence-based approach

    Am J Prev Med

    (2002)
  • Crossing the quality chasm: a new health system for the 21st century

    (2003)
  • E.A. McGlynn et al.

    The quality of health care delivered to adults in the U.S.

    N Eng J Med

    (2003)
  • L.W. Green

    From research to “best practices” in other settings and populations

    Am J Health Behav

    (2001)
  • About the NIH Roadmap

  • M.J. Khoury et al.

    The emergence of translational epidemiology: from scientific discovery to population health impact

    Am J Epidemiol

    (2010)
  • J.M. Westfall et al.

    Practice-based research—“Blue Highways” on the NIH roadmap

    JAMA

    (2007)
  • L.W. Green

    Public health asks of systems science: to advance our evidence-based practice, can you help us get more practice-based evidence?

    Am J Public Health

    (2006)
  • E.M. Yano et al.

    Implementation and spread of multi-level interventions in practice: implications for the cancer care continuum

    J Natl Cancer Inst

    (2011)
  • L.W. Green

    Making research relevant: if it is an evidence-based practice, where's the practice-based evidence?

    Fam Pract

    (2008)
  • S. Tunis

    Strategies to improve comparative effectiveness research methods and data infrastructure

  • L.K. Bartholomew et al.

    Intervention mapping: designing theory and evidence-based health promotion programs

    (2001)
  • L.M. Klesges et al.

    Beginning with the application in mind: designing and planning health behavior change interventions to enhance dissemination

    Ann Behav Med

    (2005)
  • L.W. Green et al.

    Health program planning: an educational and ecological approach

    (2005)
  • A. Best et al.

    An integrative framework for community partnering to translate theory into effective health promotion strategy

    Am J Health Promot

    (2003)
  • L.J. Damschroder et al.

    Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science

    Implement Sci

    (2009)
  • T.E. Kottke et al.

    Optimizing practice through research: a new perspective to solve an old problem

    Ann Fam Med

    (2008)
  • P.M. Senge et al.

    The dance of change: the challenges of sustaining momentum in learning organizations

    (1999)
  • E. Breslau et al.

    State-of-the-art and future dirctions in multilevel interventions across the Cancer Control Continuum

    J Natl Cancer Inst

    (2011)
  • S.L. Mercer et al.

    Federal funding and support for participatory research in public health and health care

  • R. Pawson et al.

    Realist review: a new method of systematic review designed for complex policy interventions

    J Health Serv Res Policy

    (2005)
  • S. Zaza et al.

    The Guide to Community Preventive Services

    (2005)
  • S.M. Mercer et al.

    Study designs for effectiveness and tranlsation research: identifying trade-offs

    Am J Prev Med

    (2007)
  • A foundation for evidence-driven practice: a rapid learning system for cancer careWorkshop Summary

    (2010)
  • L.M. Etheredge

    A rapid-learning health system: what would a rapid-learning health system look like, and how might we get there?

    Health Aff

    (2007)
  • L.W. Green et al.

    Evaluating the relevance, generalization, and applicability of research: issues in external validity and translation methodology

    Eval Health Profess

    (2006)
  • R.E. Glasgow

    What types of evidence are most needed to advance behavioral medicine?

    Ann Behav Med

    (2008)
  • Cited by (153)

    • Testing the Implementation of Function-focused Care in Assisted Living Settings

      2021, Journal of the American Medical Directors Association
      Citation Excerpt :

      Social cognitive theory guides the interpersonal interactions that motivate direct care workers and residents to engage in function-focused care.19–21 Lastly, the EIT was used to facilitate systemic implementation of function-focused care.17,18 The EIT process begins and ends with engagement of local stakeholders and focuses on setting specific challenges and goals.

    View all citing articles on Scopus
    View full text