Freimuth, Robert

Abstract Background: Multidisciplinary research networks often face challenges in harmonizing metrics and measures across participating sites, limiting the ability to aggregate data and generate generalizable insights. To address this barrier within the Connecting Patient Populations to Clinical Trials (CP2CT) Network, the Data, Evaluation, and Coordinating Center (DECC) coordinated the establishment of a unified framework that supports comparable evaluation and enhances the scientific impact of network-wide activities. Methods: Consensus was developed through a structured, iterative, and collaborative process: (1) drafting initial constructs, metrics, measures, and benchmarks informed by Network site materials and relevant literature; (2) soliciting qualitative and quantitative feedback from Network sites via Basecamp, an innovative online collaborative workspace for construct development; (3) conducting open dialogue sessions followed by systematic review and integration of site feedback; and (4) refining all elements through repeated feedback cycles until no further modifications were requested. Each iteration strengthened the clarity, coherence, and scientific precision of the final constructs. Results: A total of seven constructs were incorporated into the consensus-development process: clinical trials referral, clinical trials accrual, participants’ awareness of clinical trials, participants’ knowledge of clinical trials, providers’ awareness of NCI-supported clinical trials, providers’ engagement with NCI-supported clinical trials, and providers’ referral to NCI-supported clinical trials. For each construct, the Network established a conceptual definition (broad description of the construct), an operational definition (specification of how the construct is measured or observed), benchmarks (standards for assessing performance), metrics (quantitative indicators of performance), and measures (tools used to quantify inputs, processes, or outcomes). Common measures were developed for two constructs—clinical trials accrual and participants’ knowledge of clinical trials. Although full consensus was not achieved across all four Network sites for every construct, at least three sites reached consensus for the remaining five constructs. Conclusion: This consensus-building initiative resulted in the development of a unified, scientifically grounded framework. By harmonizing key elements related to clinical trial referral, accrual, awareness, knowledge, and provider engagement, the Network has established foundational tools that enable cross-site data comparability and facilitate more rigorous assessment of efforts to connect diverse patient populations to clinical trials. This framework positions the Network to inform strategies that improve equitable clinical trial participation. Citation Format: Folakemi T. Odedina, Jennifer Le-Rademacher, Robert Freimuth, Cristina Orozco, Laura Lectora, Timethia Bonner, Holly Thorson, Connecting Patient Populations to Clinical Trials(CP2CT) Program Evaluation Work Group, Connecting Patient Populations to Clinical Trials(CP2CT) Steering Committee. Development of and consensus building towards shared metrics, measures and benchmarks for the connecting patient populations to clinical trials (CP2CT) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 860.

Familial Hypercholesterolemia (FH) is underdiagnosed in the United States. Clinical decision support (CDS) could increase FH detection once implemented in clinical workflows. We deployed CDS for FH at an academic medical center and sought clinician insights using an implementation survey. In November 2020, the FH CDS was deployed in the electronic health record at all Mayo Clinic sites in two formats: a best practice advisory (BPA) and an in-basket alert. Over three months, 104 clinicians participated in the survey (response rate 11.1%). Most clinicians (81%) agreed that CDS implementation was a good option for identifying FH patients; 78% recognized the importance of implementing the tool in practice, and 72% agreed it would improve early diagnosis of FH. In comparing the two alert formats, clinicians found the in-basket alert more acceptable (p = 0.036) and more feasible (p = 0.042) than the BPA. Overall, clinicians favored implementing the FH CDS in clinical practice and provided feedback that led to iterative refinement of the tool. Such a tool can potentially increase FH detection and optimize patient management.

AbstractObjective Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center.Methods CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify “possible FH”; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS.Results The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine.Conclusion We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

Electronic health record (EHR)-based clinical decision support (CDS) can address the low awareness and undertreatment of familial hypercholesterolemia (FH), a disorder associated with a markedly increased risk of coronary heart disease. We aimed to incorporate provider perspectives into the development and implementation of a CDS tool for FH. An implementation science framework and a user-centered design process were used to create a CDS tool for FH. Primary care physicians and specialist physicians participated in qualitative interviews, usability testing and an implementation survey. The CDS was configured in two formats—a best practice alert (BPA) and an in-basket message and subsequently deployed in the EHR in silent mode. The key themes that emerged from the analysis of interview transcripts included understanding and awareness of FH, clinical workflow, physician preferences and value of CDS tools, perspectives on patient needs and values and dissemination and implementation. Recommendations related to usability included preferred CDS format and placement, content, timing and frequency, and level of alert urgency/prioritization. In response to the survey, 84.6% of physicians agreed that the CDS would improve early FH diagnosis and 92.3% agreed that it would help them identify and manage FH patients. Physician feedback led to iterative CDS refinement. In summary, we developed a CDS tool for FH using an implementation science framework and physician feedback. Initial deployment revealed a significant burden of FH and the potential for the CDS tool to have a large impact.