Sonja Likumahuwa-Ackman

The Implementation Science Centers in Cancer Control (ISC3) initiative, funded by the National Cancer Institute, called for the development of implementation laboratories to bolster implementation science, create research-ready environments, and expedite adoption and implementation of evidence-based interventions (EBIs) into practice. The Building Research in Implementation and Dissemination to close Gaps and achieve Equity in Cancer Control (BRIDGE-C2) Center is one of seven ISC3 centers. BRIDGE-C2 aims to identify strategies to improve implementation of cancer prevention EBIs and conduct research / develop pragmatic methods to tailor, enhance, and support the adoption and sustainability of these strategies; advance implementation science; and build capacity and training opportunities. Since its inception, the BRIDGE-C2 Center has been conducting research and training activities to advance knowledge on how to effectively implement strategies to improve cancer prevention EBIs in primary care clinics serving socioeconomically disadvantaged patients. The translational science benefits model (TSBM) provides a useful framework for organizing a description of the BRIDGE-C2 Center’s activities. In this paper, we describe examples of BRIDGE-C2 activities and the specific impact indicators within each relevant domain/subdomain of the TSBM, demonstrating that a single activity or project has multiple impacts on methods and capacity building, clinical domains, and community health.

Engaging diverse partners in each phase of the research process is the gold standard of community-engaged research and adds value to the impact of implementation science. However, partner engagement in dissemination, particularly meaningful involvement in developing peer-reviewed manuscripts, is lacking. The Implementation Science Centers in Cancer Control are using the Translational Science Benefits Model to demonstrate the impact of our work beyond traditional metrics, including building capacity and promoting community engagement. This paper presents a case example of one center that has developed a policy for including community partners as coauthors. Standard practices are used to foster clear communications and bidirectional collaboration. Of published papers focused on center infrastructure and implementation research pilots, 92% have community partner coauthors. This includes 21 individuals in roles ranging from physician assistant to medical director to quality manager. Through this intentional experience of co-creation, community partners have strengthened implementation science expertise. Community coauthors have also ensured that data interpretation and dissemination reflect real-world practice environments and offer sustainable strategies for rapid translation to practice improvements. Funders, academic journals, and researchers all have important roles to play in supporting community coauthors as critical thought partners who can help to narrow the gap between research and practice.

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

UNSTRUCTURED Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

As recent extreme weather events demonstrate, climate change presents unprecedented and increasing health risks, disproportionately so for disadvantaged communities in the U.S. already experiencing health disparities. As patients in these frontline communities live through extreme weather events, socioeconomic and health stressors are compounded; thus, their healthcare teams will need tools to provide precision ecologic medicine approaches to their care. Many primary care teams are taking actionable steps to bring community-level socioeconomic data (“community vital signs”) into electronic medical records, to facilitate tailoring care based on a given patient’s circumstances. This work can be extended to include environmental risk data, thus equipping healthcare teams with an awareness of clinical and community vital signs and making them better positioned to mitigate climate impacts on health. For example, if healthcare teams can easily identify patients who have multiple chronic conditions and live in an urban heat island, they can proactively arrange to “prescribe” an air conditioner, heat pump, and/or air purifier. Or, when a severe storm/heat event/poor air quality event is predicted, they can take preemptive steps to get help to patients at high medical and socioeconomic risk, rather than waiting for them to arrive in the emergency department. Advances in health information technologies now make it technically feasible to integrate a wealth of publicly-available community-level data into EMRs. Efforts to bring this contextual data into clinical settings must be accelerated to equip healthcare teams to provide precision ecologic medicine interventions to their patients.

Background: Primary care practice−based research networks (PBRNs) are critical laboratories for generating evidence from real-world settings, including studying natural experiments. Primary care9s response to the novel coronavirus-19 (COVID-19) pandemic is arguably the most impactful natural experiment in our lifetime. Evaluating the Impact of COVID-19: We briefly describe the OCHIN PBRN of community health centers (CHCs), its partnership with implementation scientists, and how we are leveraging this infrastructure and expertise to create a rapid research response evaluating how CHCs across the country responded to the COVID-19 pandemic. COVID-19 Research Roadmap: Our research agenda focuses on asking: How has care delivery in CHCs changed due to COVID-19? What impact has COVID-19 had on the delivery of preventive services in CHCs? Which PBRN services (e.g., data surveillance, training, evidence synthesis) are most impactful to real-world practices? What decision-making strategies were used in the PBRN and its practices to make real-time changes in response to the pandemic? What critical factors in successfully and sustainably transforming primary care are illuminated by pandemic-driven changes? Discussion and Conclusions: PBRNs enable real-world evaluation of practice change and natural experiments, and thus are ideal laboratories for implementation science research. We present a real-time example of how a PBRN Implementation Laboratory activated a response to study a historic natural experiment, to help other PBRNs charting a course through this pandemic.

Background: Health insurance is important for children. Public insurance programs are available to many children, but some families report confusion about how to get and keep this insurance. Community health clinics can help families get and keep health insurance for their children.

Background: Changes in health insurance policies have increased coverage opportunities, but enrollees are required to annually reapply for benefits which, if not managed appropriately, can lead to insurance gaps. Electronic health records (EHRs) can automate processes for assisting patients with health insurance enrollment and re-enrollment. Objective: We describe community health centers' (CHC) workflow, documentation, and tracking needs for assisting families with insurance application processes, and the health information technology (IT) tool components that were developed to meet those needs. Method: We conducted a qualitative study using semi-structured interviews and observation of clinic operations and insurance application assistance processes. Data were analyzed using a grounded theory approach. We diagramed workflows and shared information with a team of developers who built the EHR-based tools. Results: Four steps to the insurance assistance workflow were common among CHCs: 1) Identifying patients for public health insurance application assistance; 2) Completing and submitting the public health insurance application when clinic staff met with patients to collect requisite information and helped them apply for benefits; 3) Tracking public health insurance approval to monitor for decisions; and 4) assisting with annual health insurance reapplication. We developed EHR-based tools to support clinical staff with each of these steps. Conclusion: CHCs are uniquely positioned to help patients and families with public health insurance applications. CHCs have invested in staff to assist patients with insurance applications and help prevent coverage gaps. To best assist patients and to foster efficiency, EHR based insurance tools need comprehensive, timely, and accurate health insurance information.

Patients with gaps in health insurance coverage often defer or forgo cancer prevention services. These delays in cancer detection and diagnoses lead to higher rates of morbidity and mortality and increased costs. Recent advances in health information technology (HIT) create new opportunities to enhance insurance support services that reduce coverage gaps through automated processes applied in healthcare settings. This study will assess the implementation of insurance support HIT tools and their effectiveness at improving patients’ insurance coverage continuity and cancer screening rates. This study uses a hybrid cluster-randomized design—a combined effectiveness and implementation trial—in community health centers (CHCs) in the USA. Eligible CHC clinic sites will be randomly assigned to one of two groups in the trial’s implementation component: tools + basic training (Arm I) and tools + enhanced training + facilitation (Arm II). A propensity score-matched control group of clinics will be selected to assess the tools’ effectiveness. Quantitative analyses of the tools’ impact will use electronic health record and Medicaid data to assess effectiveness. Qualitative data will be collected to evaluate the implementation process, understand how the HIT tools are being used, and identify facilitators and barriers to their implementation and use. This study will test the effectiveness of HIT tools to enhance insurance support in CHCs and will compare strategies for facilitating their implementation in “real-world” practice settings. Findings will inform further development and, if indicated, more widespread implementation of insurance support HIT tools. Clinical trial NTC02355262

Lack of health insurance negatively impacts children's health. Despite federal initiatives to expand children's coverage and accelerate state outreach efforts, millions of US children remain uninsured or experience frequent gaps in coverage. Most current efforts to enroll and retain eligible children in public insurance programs take place outside of the health care system. This study is a partnership between patients' families, medical informaticists, federally qualified health center (FQHC) staff, and researchers to build and test information technology tools to help FQHCs reach uninsured children and those at risk for losing coverage.