Table 1.

Summary of the Challenges, Identified Solutions, and Possible Next Steps for Public Health Departments and Clinical and Translational Science Awards Programs to Advance Dissemination, Implementation and Improvement (DII) Science Research in Population Health.

Challenge	Identified Solution	Possible Next Steps for Public Health Departments and Clinical and Translational Science Awards Programs
Research questions traditionally posed by researchers are narrowly-focused and are often not a good fit with public health practice realities, creating mismatches. Paradigm that science should focus on service delivery, rather than policy, systems and environmental change. Tradition and funding to evaluate interventions in isolation to assess their incremental value.	DII sciences can be used to modify research approach. Research methods that assess collective impact of multiple interventions. Development of collaborative laboratories in which public agencies, research partners, local organizations, and residents learn together and address common population health problems.	Employ implementation and improvement research designs that are appropriate for testing policies and programs under variable real world conditions. Involve researchers in the early design phase of population and practice interventions so that policy or program evaluations are set up to produce useful information for scaling and sustaining system solutions. Train scientists and provide ongoing training or continuing education to public health and health professionals in that impact population health. Provide biostatistical consultation or evaluation resources to public health-university research teams on the use of experimental and quasi-experimental methods, pragmatic trials, time-series analysis, and complex systems science approaches. Connect and convene multi-sector partners and multi-disciplinary researchers to work on real world public health problems, using multi-sector collaborations to produce comprehensive, more cohesive health programs.
Processes associated with iterative learning and innovations are dynamic and may be difficult to study in the real world. Public health challenged to implement interventions in complex systems (e.g., multiple interventions, uncertain events and implementation timelines). Lack of available true “baseline” data.	Develop and utilize DII methods that fit the dynamic elements of the policy or real world environment. Research methods that embrace complexity, including pragmatic trials, time-series, statistical process control methods, quality improvement and emergent design methods, and complex systems science.
Selection and application of a common theory of change to guide population and system-wide approaches is often lacking in public health planning. Need for public health to implement mutually-reinforcing services and supports from different sectors to reduce duplication and siloed efforts. Research and funding agency nomenclature, autonomous work, and single disease/issue focus.	DII sciences can help promote synergies in policies and public health practice by aligning theories of change with common indicators across conditions and sectors. Use of a common theory of change that identifies shared elements across frameworks. Use of multi-agency collaborations to consider perspectives from various disciplines.
Data challenges can hinder real time, practical application of research findings to policy development and program planning. Lack of timely, appropriate data at the right level to influence and evaluate policy, systems and environmental changes. Lack of data on community assets.	DII sciences can guide data collection and analysis in real world settings. Overcome barriers to use of locally-collected data (e.g., privacy). Identify ways to augment local data-collection systems. Integrate data systems across sectors/partners.