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Clinical trials continue to face significant challenges in participant recruitment and retention. The Recruitment Innovation Center (RIC), part of the Trial Innovation Network (TIN), has been funded by the National Center for Advancing Translational Sciences of the National Institutes of Health to develop innovative strategies and technologies to enhance participant engagement in all stages of multicenter clinical trials. In collaboration with investigator teams and liaisons at Clinical and Translational Science Award institutions, the RIC is charged with the mission to design, field-test, and refine novel resources in the context of individual clinical trials. These innovations are disseminated via newsletters, publications, a virtual toolbox on the TIN website, and RIC-hosted collaboration webinars. The RIC has designed, implemented, and promised customized recruitment support for 173 studies across many diverse disease areas. This support has incorporated site feasibility assessments, community input sessions, recruitment materials recommendations, social media campaigns, and an array of study-specific suggestions. The RIC’s goal is to evaluate the efficacy of these resources and provide access to all investigating teams, so that more trials can be completed on time, within budget, with diverse participation, and with enough accrual to power statistical analyses and make substantive contributions to the advancement of healthcare.

OBJECTIVES/GOALS: Innovations with positive health impact are a high priority for NCATS and CTSAs. Program design that uses the Causal Pathway approach incorporates performance indicators that assess impact. We applied Causal Pathway thinking to an ongoing national program to enhance the evaluation of program impact. We report Lessons Learned. METHODS/STUDY POPULATION: We conducted a day-long onsite workshop to introduce the model to the project team, build capacity, and map the existing program elements to Logic Models representing program Specific Aims. A local Causal Pathway (CP) champion was identified. Alignment of the Logic Models with the CP approach (input→activities→ outputs→effects/impact) developed iteratively through biweekly, then monthly conferral among stakeholders. Key tasks included distinguishing among activities, outputs, and effects (impacts), and identification of performance indicators for each stage of the Causal Pathway. Visualization tools and an additional late stage half-day workshop were used to foster consensus. Implementation of the CP model tested the feasibility of collecting specific indicators and prompted model revisions. RESULTS/ANTICIPATED RESULTS: Program leadership and team members (n = 30) participated in the kick-off workshop. Four Specific Aims were mapped to Logic Models. Multiple Causal Pathway (CP) diagrams, one for each project in the program, were developed and mapped to Aims. Alignment of CP threads to Aims and identification of performance indicators required iteration. CP threads converged onto common final Impacts, sometimes crossing to another Aim. Performance indicators for operations were readily accessible to team members, and less so for impacts. Assumptions about program effects were subjected to specific indicators. Over time, Leadership noticed more expression of CP thinking in daily activities. New projects developed during this period incorporated the CP approach. Teams were able to streamline and simplify Logic/CP models. DISCUSSION/SIGNIFICANCE OF IMPACT: Through capacity-building and mentored exercises, an innovation team was able to infuse CP thinking into the evaluation of their ongoing program. The CP approach to design and evaluation maps progress and indicators across the life of a program from initial activities to its ultimate impact.

Background:To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment.Methods:To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals’ electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial’s enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3,453; test set n = 2,315). We also tested both on data from emergency department electrocardiographs from across the US (n = 8,556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals.Results:Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs.Conclusion:Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.