Freimuth, Robert R.

Abstract Background: Despite the decline in overall mortality and incidence of cancer in the US population, the burden of cancer is still high, especially in underserved and under-researched populations. A major contributor to poor cancer outcomes is the absence of dedicated efforts utilizing scientifically rigorous approaches and implementation strategies to optimize participant enrollment and accrual in clinical trials. The Connecting Patient Populations to Clinical Trials (CP2CT) program implements innovative multilevel interventions to enhance clinical trial awareness and optimize referral pathways that facilitate patient enrollment. The CP2CT Data, Evaluation, and Coordinating Center (DECC) supports its data and evaluation activities, and coordinates a learning collaborative towards mitigating the key barriers of cancer burden in all populations. Methods: Using an implementation science-informed approach, the DECC deployed coordinated strategies over three years: (1) administrative structuring through streamlined governance, centralized reporting, and stakeholder integration; (2) data standardization via Common Data Elements (CDEs), a cross-network REDCap system, and a multi-institution Data Transfer Agreement (DTA); (3) evaluation infrastructure through a Network Logic Model, evaluation plan, standardized constructs, and a Process Evaluation Plan with tools; (4) learning supports through an internal website and expanded Speaker Series; and (5) site-level implementation facilitation through structured visits. Results: In collaboration with the CP2CT Network and NCI, the DECC’s achievements span four domains: (1) Administrative: built infrastructure, deployed centralized reporting platforms, coordinated key meetings, and strengthened community integration. (2) Data: standardized collection, secured network-wide data sharing through the DTA, enabled harmonized multi-site reporting, and advanced groundwork for interoperability and scientific value. (3) Program Evaluation: linked CP2CT activities to measurable outcomes through a Network-wide Logic Model, guided evaluation, established shared evaluation language, and launched a transferable Process Evaluation framework. (4) Learning Collaborative: developed a centralized resource hub, expanded the Speaker Series, and advanced authorship transparency with the CP2CT Publication Policy. < Conclusion: The CP2CT DECC has established a sustainable, scalable infrastructure that integrates administrative coordination, standardized data collection, robust program evaluation, and a collaborative learning environment. These efforts enhance clinical trial awareness, optimize referral pathways, and provide a transferable model for improving participant enrollment and scientific rigor across national research networks, ultimately addressing disparities in cancer outcomes. Citation Format: Laura E. Lectora, Cristina J. Orozco, Holly A. Thorson, Timethia Bonner, Robert R. Freimuth, Jennifer G. Le-Rademacher, Folakemi T. Odedina. Building a national network coordination center for clinical trials: Early implementation outcomes from the CP2CT DECC [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 899.

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.

Abstract The adoption of electronic health records (EHRs) has transformed health care, improving efficiency and chart accessibility. However, the widespread reliance on unstructured data entry and the lack of standardized documentation frameworks have resulted in significant data fragmentation across health care systems. The prevalence of unstructured data in EHRs limits their potential for clinical decision support, trial matching, real‐world evidence (RWE) generation, and quality measurement. Data fragmentation in health care triggers a cascade of challenges that ultimately compromise patient care. Clinicians face an excessive documentation burden and struggle to locate critical information buried in unstructured notes. Researchers encounter difficulties in extracting reliable clinical data. EHR vendors grapple with standardizing unstructured information for interoperability, and payers are unable to process unstructured clinical data efficiently to support value‐based care models. These challenges are particularly acute in oncology, where complex clinical elements like cancer staging, disease status, and treatment changes require precise, structured documentation. Emerging artificial intelligence (AI) technologies, such as large language models (LLMs) and ambient listening, offer a path to automate structured data generation while reducing the workload on providers. Here, the authors propose LLM‐based workflows that balance automation with clinician verification, streamlining data entry without compromising accuracy. Realizing these benefits requires coordinated efforts among clinicians, researchers, EHR vendors, payers, and policymakers to align regulatory frameworks with AI‐driven innovations. This article outlines a strategy to enhance structured data capture within EHRs, ultimately improving patient care, research, and health care efficiency.

Abstract Categorical variants, or sets of genomic alterations constrained by shared properties, are pervasive across clinical, regulatory, and research domains in the biomedical ecosystem, yet their inconsistent and non-computable representation hinders data interoperability and clinical interpretation. We surveyed genomic knowledgebases spanning regulatory approvals and the biomedical literature and found that categorical variants underpin a substantial proportion of clinical genomics knowledge, but are largely described using incompatible bespoke models. To address this, we developed the GA4GH Categorical Variation Representation Specification (Cat-VRS), a constraint-based framework that provides a unified computable representation for both precise and intentionally broad categories across molecular and systemic variant domains. Cat-VRS enables harmonization of genomic knowledgebases, computable category-based search, and automated matching between assayed variants and categorical entities in clinical and research contexts. By providing a principled, extensible model for categorical variation, Cat-VRS enables computable reasoning over genomic variant categories and establishes a foundation for the standardized representation and exchange of genomic knowledge.

Abstract Objectives To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions. Materials and Methods A five-stage process was executed at ten sites. Data streams from REDCap surveys, PRS and monogenic reports, and MeTree pedigrees were normalized and forwarded through a REDCap plug-in to the CanRisk API. Results Integrated risk was returned to >10 000 women; 3.6% were ≥25 % lifetime risk and 0.9% carried pathogenic variants. Pipeline generated score aligns well with manual generated ones. Major barriers such as heterogeneous pedigree formats, missing data, edge-case handling, and evolving model versions were identified and resolved through mapping rules, imputations, and iterative testing. Discussion Cross-platform data harmonization and stakeholder alignment were decisive for success. Borderline-risk communication and model-version drift remain open issues. Conclusion Large-scale PRS-integrated breast-cancer risk reporting is feasible but requires robust interoperability standards and iterative governance.

Abstract Background Genetic testing has traditionally been divided into molecular genetics and cytogenetics, originally driven by the use of different assays and their associated limitations. Cytogenetic technologies such as karyotyping, fluorescent in situ hybridization or chromosomal microarrays are used to detect large “megabase level” copy number variants and other structural variants such as inversions or translocations. In contrast, molecular methodologies are heavily biased toward subgenic “small variants” such as single nucleotide variants, insertions/deletions, and targeted detection of intragenic, exon level deletions or duplications. The boundaries between these approaches are now increasingly blurred as next-generation sequencing technologies and their use for genome-wide analysis are used by both disciplines, therefore eliminating the historic and somewhat artificial separation driven by variant type. Content This review discusses the history of genomic nomenclature across both fields, summarizes implementation challenges for the clinical genetics community, and identifies key considerations for enabling a seamless connection of the stakeholders that consume variant descriptions. Summary Standardization is naturally a lengthy and complex process that requires consensus building between different stakeholders. Developing a standard that not only fits the multitude of needs across the entities that consume genetic variant information but also works equally well for all genetic variant types is an ambitious goal that calls for revisiting this vision.

Abstract Objective Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association—Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. Process A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. Conclusions Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

The National Institute of Health (NIH) Genetic Testing Registry (GTR) provides a variety of information about genetic tests such as relevant methods, conditions, and performing laboratories. This study mapped a subset of GTR data to the newly developed HL7®-FHIR® Genomic Study resource. Using open-source tools, a web application was developed to implement data mapping and provides many GTR test records as Genomic Study resources. The developed system demonstrates the feasibility of using open-source tools and the FHIR Genomic Study resource to represent publicly available genetic testing information. This study validates the overall design of the Genomic Study resource and proposes two enhancements to support additional data elements.

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.