We developed the Clinical Imaging Data for UR Researchers (CIDUR) platform and process to address the critical need for secure collection, storage, governance, and utilization of clinical imaging files and data in IRB-approved research projects. The system, built on the open-source XNAT application1, provides robust data management capabilities enabling direct integration with clinical scanners and Picture Archiving and Communication Systems (PACS), supporting diverse imaging modalities including MRI, X-ray, CT, and ultrasound. CIDUR incorporates a comprehensive DICOM de-identification pipeline compliant with Supplement 142 standards2, ensuring patient privacy protection while maintaining data utility for research purposes. The platform implements sophisticated access controls through role and group-based permissions, complemented by comprehensive auditable activity logs for regulatory compliance and data governance. 

From an analytical perspective, CIDUR provides researchers with integrated DICOM viewing capabilities featuring measurement tools, seamless integration with structured data and subject demographics, and a robust Application Programming Interface (API) for external analysis tools.     	

Since deployment in May 2024, we have setup up CIDUR for 14 research teams and delivered over 12 million DICOM files for research studies.


Advancing Discovery: Bridging the Gap between Clinical Imaging Acquisition, Management, and Research Analysis

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2025 Fall CTSA Program&nbsp;Annual Meeting

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Strategic Management, Pilot Programs, and/or Element E Programs

Workforce Development

Research Resources

Biostatistics, Biomedical Informatics, and Data Science

Community and Stakeholder Engagement

The Poster Submission period for the Fall 2025 Annual Meeting is closed. For questions or support please contact us at <a href="mailto:events@ccos.ctsa.io">events@ccos.ctsa.io</a>.

The theme for this year’s poster session is “Innovative Hub Projects Advancing Clinical and Translational Science." The Poster Presentation Sessions offer a platform for CTSA hub-selected presenters to showcase innovative hub projects that advance clinical and translational science.&nbsp;Poster Submissions open September 2 at 9:00 am ET and close September 30 at 3:00 pm ET.&nbsp;Eligible Submissions:Each hub may submit one poster related to the theme of the Poster Presentation Sessions, Innovative Hub Projects Advancing Clinical and Translational Science, and falls within one of the five categories:<ol style="list-style-type:decimal;"><li>Strategic Management, Pilot Programs, and/or Element E Programs: Initiatives that enhance hub operations, including innovative evaluation programs, pilot programs or Element E programs.</li><li>Workforce Development: Initiatives that support training, mentoring, and career development for translational research and science professionals, including clinical research professionals, scholars, trainees, and early-career investigators.</li><li>Research Resources: Initiatives that improve access to or utilization of core services, tools, or infrastructure that support translational research and/or advance translational science.</li><li>Biostatistics, Biomedical Informatics, and Data Science: Initiatives that leverage data-driven approaches, informatics platforms, or statistical innovations to accelerate research and improve decision-making in translational science.</li><li>Community and Stakeholder Engagement: Initiatives that foster meaningful partnerships with patients, communities, and stakeholders to ensure research is inclusive, relevant, and impactful.</li></ol>We ask that the UL1/UM1 Principal Investigators work with their respective teams to select a hub initiative that exemplifies innovations advancing clinical and translational science and offers potential for broader adoption across the consortium.&nbsp;Traditional scientific research posters are not the focus of this year’s program; however, if you would rather have a scientific poster showcased, we will provide space for the poster to be displayed. Only posters that clearly align with the theme and fit within one of the five categories listed above will be eligible for judging and awards.&nbsp;Submission Instructions:<ul><li>Hub Administrators will log onto the CCOS website to submit the poster abstract and poster PDF by or before September 30 at 3pm ET.</li><li>Hub Administrators will be able to designate a delegate to submit on their behalf by email at <a href="mailto:support@ccos.ctsa.io">support@ccos.ctsa.io</a> by September 15.</li><li>Upon submission, Hub Administrators and first authors will receive email confirmation of the poster submission. Changes cannot be made after submission.?</li></ul>Poster Presentations:<ul><li>Poster dimensions can be a maximum of 44"H x 90"W.</li><li>Poster presenters must print their poster for onsite presentation.?</li><li>Printing and delivery to the conference are the responsibility of the hub/presenter.</li><li>Posters will be randomly assigned to one of two sessions on October 23.</li><li>A jury will review posters onsite and rate them based on hub innovation, quality of presentation and overall poster design. Awards will be given for Best in Show and for performance in each submission category listed above.</li><li>Award presentations will be made at the close of the meeting on October 24.</li></ul>Attendance:<ul><li>Posters must be presented in-person during the Annual Program Meeting.</li><li>The limit for in-person registration is nine (9) per hub plus one (1) poster presenter, for a total of ten (10) in-person attendees (speakers are counted outside of this limit).</li></ul>Communications:<ul><li>Questions about the poster sessions can be directed to <a href="mailto:events@ccos.ctsa.io">events@ccos.ctsa.io</a>.&nbsp;</li><li>Submission confirmations will be sent automatically to the Hub Administrator and first author.</li><li>Assistance with resubmission/deletion of posters submitted in error should be directed to <a href="mailto:support@ccos.ctsa.io">support@ccos.ctsa.io.</a>&nbsp;</li><li>Authors will be notified of presentation time slot, poster number and set up instructions by October 17.</li></ul>

Poster Submission Details

Poster Submissions are currently closed and will open September 2 at 9:00 am ET. For questions or support please contact us at <a href="mailto:events@ccos.ctsa.io">events@ccos.ctsa.io</a>.

Poster Submission

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The Fall 2025 CTSA Program Annual Meeting theme is CTSAs Leading the Way: Resilience, Innovation and Impact. Topics to be addressed include New/Pivoting Priorities, Collaboration &amp; Communication, and Resilience, Artificial Intelligence, and Real World Data.

2025 Fall CTSA Program Annual Meeting

2025 Fall Annual Meeting - Joint EC Leads Meeting

2025 Fall Annual Meeting - Hub Administrators Meeting

2025 Fall Annual Meeting - Workforce Development Meeting

2025 Fall Annual Meeting - Collaboration and Engagement Meeting

2025 Fall Annual Meeting - BIDS Meeting

2025 Fall Annual Meeting - Integration Across the Lifespan Meeting

2025 Fall Annual Meeting - Pivoting to Address Emerging Priorities

2025 Fall Annual Meeting - Collaborative Approaches to Communicating Translational Science Impact with Community Relevance

2025 Fall Annual Meeting - Resilience: How the CTSAs Innovate and Impact in a Time of Crisis

2025 Fall Annual Meeting - UL1/UM1 PI Meeting

2025 Fall Annual Meeting - Day Two Introduction

2025 Fall Annual Meeting - Setting the Foundation for Successful AI-Enabled Clinical Research

2025 Fall Annual Meeting - Real World Data in Action: From Insight to Impact

2025 Fall Annual Meeting - NCATS Session on Real World Data: Workforce Development and Element E Projects

2025 Fall Annual Meeting - Closing Session

Agenda

Meeting materials are available on the <a href="https://ccos-cc.ctsa.io/groups/program-meetings/2025/2025-fall-meeting?view=agenda">Agenda page</a> on the left side menu.&nbsp;If you have questions about meeting material access, please <a href="mailto:support@ccos.ctsa.io" target="_blank" rel="noopener noreferrer">email CCOS support</a>.

Meeting Materials

October 22 | Administrative and Leadership Meetings: Steering Committee, Joint EC Leads, Hub Administrators, TIN, Enterprise Committees (Workforce Development, Collaboration &amp; Engagement, BIDS, Integration Across the Lifespan)October 23 | General Session Meetings: Welcome &amp; Keynote address by Dr. Jay Bhattacharya, Sessions on New/Pivoting Priorities, Collaboration &amp; Communication, and Resilience. There will also be Poster Sessions focused on innovative hub projects/activities and networking sessions for K, T and UL1/UM1 PIsOctober 24 | General Session Meetings: Sessions on Real World Data, Artificial Intelligence, and NCATS-led content ending with the Top 10 takeaways and announcement of poster session winners

Meeting Topics

LocationHyatt Regency Crystal City at Reagan National Airport2799 Richmond Hwy,&nbsp;Arlington, VA 22202

Location and Reservations

Hub Attendance Guidelines and Registration Each hub may send up to 9 attendees, not including TIN, Steering Committee, or Fall Planning Committee members. This includes:<ul><li>Up to 5 representatives of hub leadership (per the NOFO)</li><li>1 representative per EC (up to 4 total)</li></ul>&nbsp;Justification for Attendance&nbsp;Due to the addition of Enterprise Committee meetings to this year’s event, we are planning for a larger number of attendees at both the EC meetings and the general meeting. We strongly encourage Enterprise Committee (EC) members to attend the general sessions along with your hub leadership. Their participation ensures committee perspectives are integrated into broader programmatic discussions and helps align efforts across the CTSA Program. This cross-functional engagement is vital for fostering collaboration and innovation.

Attendance Guidelines

Each hub may submit one poster related to the theme of the Poster Presentation Session, “Innovative Hub Projects Advancing Clinical and Translational Science,” that falls within one of the five categories:<ol style="list-style-type:decimal;"><li>Strategic Management, Pilot Programs, and/or Element E Programs: Initiatives that enhance hub operations, including innovative evaluation programs, pilot programs or Element E programs.</li><li>Workforce Development: Initiatives that support training, mentoring, and career development for translational research and science professionals, including clinical research professionals, scholars, trainees, and early-career investigators.</li><li>Research Resources: Initiatives that improve access to or utilization of core services, tools, or infrastructure that support translational research and/or advance translational science.</li><li>Biostatistics, Biomedical Informatics, and Data Science: Initiatives that leverage data-driven approaches, informatics platforms, or statistical innovations to accelerate research and improve decision-making in translational science.</li><li>Community and Stakeholder Engagement: Initiatives that foster meaningful partnerships with patients, communities, and stakeholders to ensure research is inclusive, relevant, and impactful.</li></ol>We ask that the UL1/UM1 Principal Investigators work with their respective teams to select a hub initiative that exemplifies innovations advancing clinical and translational science and offers potential for broader adoption across the consortium.

Poster Session

2025 Fall CTSA ProgramAnnual Meeting

A foundational transformer leveraging full-night, multichannel sleep study representations estimate cardiovascular mortality risk

Title: A Foundational Transformer Leveraging Full-Night, Multichannel Sleep Study Representations to Estimate Cardiovascular Mortality Risk
Authors: Benjamin Fox, Sajila Wickramaratne, Ankit Parekh, Girish Nadkarni
Affiliations: Icahn School of Medicine at Mount Sinai
Objective:
This study introduces PSG-JEPA, a foundational transformer model trained on multichannel, full-night polysomnography (PSG) data using a Joint Embedding Predictive Architecture (JEPA). The model aims to learn robust, task-agnostic sleep representations to estimate 10-year cardiovascular (CV) mortality risk.
Background:
Sleep disorders are linked to several leading causes of death, including cardiovascular disease. PSG data, when combined with advanced AI/ML techniques, can yield meaningful representations for clinical risk prediction. JEPA, a recent self-supervised learning approach, offers a promising framework for learning from complex signal data.
Methods:
The model was trained on 33,551 PSG studies (~1.9 million hours) from four large datasets: the Human Sleep Project, APPLES, MESA, and Mt. Sinai’s Sleep Data Warehouse. Each study included seven signal channels (EEG, EOG, EMG, ECG, SpO₂, thoracic and abdominal respiratory rates), resampled to 128 Hz and segmented into 3-second patches. A depthwise convolutional encoder embedded these into a 768-dimensional space. JEPA was used to predict masked target patches from context patches using a mean squared error loss.
An attentive classifier was trained on the learned representations using the SHHS and MrOS datasets (n=8,693) to estimate CV mortality risk, with time discretized into yearly bins over a 10-year horizon. The Cox proportional hazards model was used as the loss function.
Results:

PSG-JEPA achieved a mean AUROC of 0.68 and an integrated Brier score of 0.04 on the held-out test set.
Risk scores showed strong calibration and discrimination. Kaplan-Meier curves demonstrated statistically significant survival differences across risk quartiles (p < 0.0001).
Individuals with an apnea-hypopnea index (AHI) ≥ 5 had significantly lower survival probabilities than those with AHI < 5 (p < 0.01).
The model generalized well without fine-tuning, indicating its potential for broader clinical applications.

Conclusions:
PSG-JEPA is the first JEPA-based model to learn full-night, multichannel PSG representations for CV mortality risk prediction. Its ability to generate meaningful, task-agnostic features without fine-tuning highlights its utility for downstream clinical tasks. Future directions include evaluating performance on other outcomes (e.g., stroke, MI, sleep staging) and exploring sub-cohorts such as CPAP-treated patients.
Funding: K25HL151912, R01HL171813, R21HL165320, TL1TR004420

Accelerating Science and Technology Translation Through the Development of Agentic AI Solutions Supported by Just in Time Learning

Mayo Clinic Center for Clinical and Translational Science (CCaTS) facilitates high-quality, team-based, multidisciplinary research that accelerates clinical trial innovation, advances digital health initiatives, and collaborates with stakeholders and communities to improve patient care and health outcomes for all.

Tufts Clinical and Translational Science Institute launched the Blue Star Investigator Certificate Program in 2021, an education innovation designed to address a gap in hands-on training in the operational skills of developing and running investigator-initiated clinical trials. The first iteration of the program was designed as an 8-week blended learning experience, combining self-paced online didactic content with weekly 2-hour live sessions where participants engaged in active learning to apply concepts to real-world scenarios. After a successful initial cohort, [1] the program was expanded from 8 to 10 weeks in 2022 and 2023 to incorporate more opportunities for learners to develop their own clinical trial ideas in consultation with project mentors. Across all three cohorts, program participants consistently reported that the live in-person sessions were one of the most valuable aspects of the program. These sessions, however, are also resource- and labor-intensive and do not scale well. The challenge, then, was to develop a strategy to disseminate this training innovation to other audiences to expand reach and impact.

In collaboration with our partners at MaineHealth Institute for Research and the Northern New England Clinical and Translational Research Network (NNE-CTR) we developed a new variation of the program, Blue Star – Maine and an accompanying train-the-trainer model to adapt and scale the core curriculum to new audiences. In response to local needs, we adapted this program to focus on serving as a site Principal Investigator (PI) for industry clinical trials and shortened the in-person sessions to 1 ½ days. In 2025 we developed and offered a third variation of the core Blue Star curriculum, this time tailored to the needs of lay leaders in patient advocacy groups – an underserved audience with an identified need for education in clinical trial operations.

This poster illustrates how Tufts CTSI is adapting the Blue Star program to meet the needs of different learner groups and scaling our training innovation to other sites while retaining the key components that contribute to high-impact learning. 

1. Markman KM, Brewer SK, Klein AK, Magnuson B. Developing an engaging and accessible clinical research training program for new investigators. Journal of Clinical and Translational Science. 2023;7(1):e53. doi:10.1017/cts.2022.446 

Adapting and Disseminating the Blue Star Investigator Training Program: Variations on a Theme

To advance the translational science workforce through an inclusive, scalable model that integrates education, mentor development, experiential opportunities, peer education, didactic learning, and community engagement. Our approach spans the KL2 and Translational Research Certificate Programs, the Rigor, Reproducibility, and Reporting (“R3”) curriculum, epidemiology and “big data”, weekly research seminars, mentor training, clinical and nursing externships, community-based clinicians, health centers and community advisory board initiatives. Collectively, this model builds skills, strengthens mentorship, expands career pathways, and deepens public trust while demonstrating cross-hub relevance and alignment with CTSA goals.


Advancing the Translational Science Workforce: An Integrated Model of Research, Education, Mentorship, and Community Engagement

Background: Clinical and translational science streamlines the translation of research evidence into real-world practice, benefiting broader communities. Demonstrating the relevance and impact of this work across diverse settings is crucial for disseminating and implementing (D&I) high-quality and culturally relevant clinical practices. The Translational Science Benefits Model (TSBM) helps assess clinical and community health impacts of translational research outcomes beyond traditional measures. The University of California San Diego’s Altman Clinical and Translational Research Institute (UCSD ACTRI) adopted the TSBM to evaluate the impact of their supported research projects. This study describes an iterative approach to develop TSBM Impact Profiles and identify themes related to TSBM domains and key benefits.
Methods: UCSD ACTRI investigators completed an online form adapted from the TSBM Toolkit, detailing the project challenge, approach, intended impact, and relevant TSBM domains and benefits. The ACTRI TSBM team synthesized this information and aligned it with the TSBM framework. Finalized content was transformed into 1-3 page TSBM Impact Profiles published on the ACTRI website. A directed content analysis examined themes related to TSBM domains and benefits across the profiles.
Results: Results from three published D&I-specific Impact Profiles indicate TSBM benefits covered all four TSBM domains (Community, Clinical, Policy, and Economic), with a notable focus on community, clinical, and policy-related benefits. Each profile contained unique benefits (M=5) across these domains, including both potential (M=2) and demonstrated benefits (M=3). The ATTAIN NAV project highlighted Community, Clinical, and Policy benefits by improving engagement with health services and influencing healthcare policies. The STOP COVID-19 project addressed all four domains, enhancing healthcare practices for underserved communities. The Enhancing Collaborative Decision-Making project focused on Clinical, Community, and Policy benefits by engaging veterans of color to improve mental health care quality and utilization.
Discussion: This work provides practical methods and example case studies for applying the TSBM framework to evaluate the relevance and impact of research projects. Results highlight an effective process for capturing a multitude of impacts across diverse projects, reflecting core D&I objectives by assessing evidence-based interventions that address public health and healthcare disparities. Additional impact profile data will be presented to further demonstrate the applicability of this method.


An iterative approach to evaluating impact of a CTSA program using the Translational Science Benefits Model

TITLE: ARLife: Building a Cross-Institutional, Cross-Sector, Real-World Data Platform for Lifespan Research in Arkansas
BACKGROUND: The National Academy of Medicine envisions a continuously learning health system with data as a core utility for success. Clinical and Translational Science Award (CTSA) programs have leveraged electronic health care data for COVID-19 research at the national level. However, individual state-based approaches for clinical research using electronic health records data, as well as data from public health departments and health insurance claims has received less attention. Barriers to compiling data from multiple health-related entities include entity-specific missions and regulations, data privacy and security, variations in data quality, and logistical challenges associated with patient name changes over time. The Translational Research Institute (TRI) at the University of Arkansas for Medical Sciences (UAMS) developed ARLife to overcome data linkage challenges and to support research initiatives in lifespan research. We present the legal and operational aspects of ARLife, TRI’s initiative to overcome these barriers in support of lifespan research.
AIMS/PUPOSE: To enable linked longitudinal data from multiple institutions without individually informed consent to be utilized by researchers for lifespan research.
METHODS: TRI leveraged existing relationships with representatives from two hospitals, the Arkansas Department of Health, and an independent health policy center to build a viable mechanism to create analytic files for lifespan research. We describe the institutional agreements, “honest broker” data infrastructure, and project-specific processes for data acquisition. 
RESULTS: The method of combining existing data including a mandatory all-payer claims database, state health department data, EPIC electronic health record data from the two tertiary care centers serving Arkansas adults and children, UAMS and Arkansas Children’s Hospital, respectively, is demonstrated. A case study of initial application for congenital syphilis is provided.
CONCLUSION: TRI’s ARLife infrastructure directly supports lifespan research currently underway and represents a model for other institutions to advance efforts to establish longitudinal studies. 

ARLife: Building a Cross-Institutional, Cross-Sector, Real-World Data Platform for Lifespan Research in Arkansas

Advancing a New Paradigm for T4 AI Application

Background: Many AI applications in healthcare optimize narrow functions rather than whole systems. These products can be taxing to physicians and address a symptom rather than a cause of health system pain points. Also, translational researchers often seek to persuade clinicians to trust AI predictions rather than use AI to enhance human performance, which is true clinical decision support and a unique contribution of AI. The CTSI hub collaborated with the UCLA Health AI Development Lab to address these issues

Methods/Innovation: The Lab leverages UCLA Health analytics and operations, and multiple hub programs, to form teams of clinical informaticists, computer scientists, translational researchers, and health system operational leads who scope the problem and iterate toward a durable solution. The teams seek system-level problem solving—creating conditions for durable change. Most recently, the Lab is advancing a new paradigm for T4 applications of AI by: (1) orienting teams on optimizing a system rather than its isolated parts, (2) focusing on making humans better through learning, rather than making models better; (3) embedding iterative improvement frameworks into AI projects; and (4) incorporating feedback loops that reflect the variation and evolution of healthcare systems, i.e. the lack of ground truth in problems such as providing the right care in the right place. This has produced an interactive platform that applies nearest-neighbor methods to surface clinically relevant cases for physicians. This accelerates their learning and improves future decision-making about patient placement and treatment.

Impact: The CTSI collaboration with the AI Development Lab is a translational science innovation that enables more durable translation of knowledge into practice. The new paradigm fosters a new culture of AI for translation: clinicians, informaticists, and translational researchers work side by side, using shared language and continuous feedback to accelerate knowledge into practice. It is highly adaptable for dissemination across the national CTSA consortium, with an open and modular informatics platform integrable into many electronic health record environments; scalable statistical and data-driven methods (e.g., nearest-neighbor case surfacing) that can be applied within and across clinical domains; a shared evaluation and feedback framework that supports the real-time needs of delivery systems; and a collaborative action model that fosters a shared learning culture of frontline clinicians, operations, health system informatics, and scientists. The collaboration is a practical, reproducible blueprint for other CTSA hubs seeking to expand translational science capacity through AI-driven, data-enabled infrastructure.



Artificial Intelligence in Last Mile Translation: Catalyzing Learning

The Informatics and Data Science core at NC TraCS provisions electronic health record data for 250-300 research studies each year. These datasets range from simple Excel spreadsheets; to millions of records across tens of tables, extracted as individual CSVs; to sizeable JSON files containing thousands or more free-text clinical notes. While this provisioning process has served thousands of studies, it has fallen behind in meeting the needs of modern data science in a number of ways: (1) performance, for analyses that require significant compute; (2) auditing, to ensure users are adhering to our policies; (3) security, to programmatically restrict users from moving or exfiltrating data; and (4) file formatting, to provide very large files in optimal formats (e.g., Parquet) rather than unwieldy CSVs.
To address these shortcomings, UNC School of Medicine and UNC Health have developed a new analytical ecosystem, including the following systems:
•	TriNetX, a commercial platform for cohort counts and no-code analyses.
•	ORDR(D), a homegrown platform for exploring deidentified clinical data with a low barrier to entry.
•	The SHIRE, a homegrown cloud environment to support sophisticated analyses and secure AI with identified data.
Here, we describe ORDR(D) and SHIRE, particularly focusing on the components of those systems that are shareable with other CTSA hubs. 

Building a CTSA-powered, modern analytic ecosystem: SHIRE and ORDR(D)

Artificial Intelligence (AI) holds significant promise for developing personalized predictions patient-specific healthcare. However, AI studies often fail to integrate the needs of special patient populations, including pregnant women. Digital twin (DT) technology represents an emerging approach for addressing these limitations while optimizing healthcare decisions. The GHUCCTS team aims to conduct an AI-focused Clinical and Translational Research (CTR)Element E study as an inter-institutional innovative collaborative exploring EHR-driven big data applications in addressing maternal health risks through the development of a digital twin (DT-CTR) methodology. DT-CTR serves as virtual representations of patients, functioning as “living,” simulated patient models. Patient-specific DT-CTR is built by integrating data from multiple sources, including EHR information, real-time health indicators (e.g., vital signs), and demographics, as well as nonmedical environmental health factors, which are continuously calibrated using real-world data. Research Question and related Specific Aims: How can a Digital Twin (DT-CTR) model leverage synthetic patient data for predicting maternal health risks and related comorbid factors? DR-CRT specific aims are structured around a proposed 4-year study which emphasizes Aim 1 (further development), Aim 2 (refinement), and Aim 3 (evaluation of an existing tool). Aim 1 will optimize the Maternal Digital Twin (MDT) platform via a iterative process that includes diverse stakeholders. Aim 2 will create adynamic MDT-based health risk prediction model that utilizes data from structured and unstructured EHRs. Aim 3 will validate the machine learning (ML) algorithms of the platform for personalized maternal health risk screening, enabling early detection of complications among patients from disadvantaged backgrounds (see Figure 1). Preliminary Big Data Study Results: Data from 40,000+ individuals were collected during our preliminary Safe Baby Safe Moms (SBSM) study from 202-2024. A Ready-to-go dataset was designed through streamlining data definitions to support the pre and postnatal journey. Methods and Analysis: The project will develop trustworthy AI tools for vulnerable pregnant women by implementing community engagement with trust-enhancing algorithms. We plan to mitigate major roadblocks that could hinder MDT implementation (see Figure 2). Significance and Innovation: The co-design activities will be supported by the GHUCCTS Collaborative Center for AI CoLab. The AI CoLab is also be embedded within other GHUCCTS Elements to enhance ongoing workforce building, training, education, and pilot activities through their rich expertise. The long-term goal of DT-CTR is to create and disseminate a scalable, trusted, and generalizable AI-based platform for predicting maternal health risks. This will facilitate an interface for collecting patient data a core model of maternal health and care recommendations.

Building a Digital Twin Model for Predicting Maternal Health Risks: Utilizing Multi-Institutional Collaborations to Advance Innovative Clinical and Translational Science

The Rio Grande Valley (RGV) of Texas represents a diverse, largely Hispanic community with health needs distinct from national trends. Type 2 diabetes and its sequelae, particularly diabetic foot ulcers, contribute to disproportionately high rates of lower extremity amputations in the region, despite national declines. Addressing these disparities requires identifying unmet clinical needs within the cultural and resource constraints of this community.
To meet this challenge, Rice University partnered with the University of Texas Rio Grande Valley (UTRGV) School of Medicine and the UT School of Public Health in Brownsville to conduct a structured clinical needs finding experience. Across two years, graduate students from the Global Medical Innovation (GMI) program and undergraduates enrolled in design courses participated in immersive trips to the RGV. Activities included clinical shadowing in federally qualified health centers and hospitals, engagement with community health workers, and contextual site visits that explored social determinants of health. Student teams synthesized observations into need statements, project sketches, and prototype concepts, several of which were further developed in undergraduate and graduate design programs.
Survey results indicated that students highly valued direct patient-facing experiences and small-group shadowing. Contextual engagements, such as urban farms and local markets, enriched their understanding of cultural and socioeconomic factors shaping diabetes care. Identified needs spanned preventive technologies for diabetic foot disease, improved home dialysis options, and accessible diagnostic tools for comorbid conditions such as fatty liver disease. A graduate project emerging from this work: Innoscope, a low-cost tele-retinal camera. This project demonstrated the translation of needs finding into tangible innovation.
This pilot highlights the value of embedding engineering students in under-resourced clinical contexts to generate authentic, community-driven design challenges. Sustained partnerships in the RGV will expand opportunities for medical device innovation while fostering culturally responsive engineering education.

Clinical Needs Finding in Domestic Settings That Challenge Students With In-Country Out-of-Contacts Experiences

Clinical Research Staff Development Initatives at Johns Hopkins University

Clinical Research Professionals (CRPs) are an integral part of the clinical research infrastructure. Johns Hopkins University (JHU) has developed novel approaches to grow the availability of CRPs, onboard new staff rapidly and efficiently, provide investigators flexible staffing models to support ongoing trials, and offer advanced educational opportunities.
The Research Coordinator Support Service (RCSS) is a pool of research staff available for hire on an hourly basis by JHU investigators. RCSS consists of Apprentices trained on the job through the Coordinator Apprenticeship Program (CAP). Each Apprentice receives 200 hours of essential training, shadows an experienced mentor. Apprentices, report high satisfaction and many have been promoted to Coordinator positions within the University and beyond. Overall 26/61 (43%) are still at Johns Hopkins in various capacities and 56/61 (92%) are still in clinical research and/or healthcare up to 12 years later. 
The Research Personnel Onboarding Program (RPOP) was created to address the training needs of research personnel hired by other departments. RPOP has trained 44 new research staff and fellows with 70% still in active mentoring. A satisfaction survey shows 11/11 (100%) agreed their questions were answered in a timely manner, and they are confident in their role.  Furthermore 10/11 (91%) believed the program helped acclimate them to the JHU work environment and 9/11 (82%) believe the program was an efficient way to help them learn about their role. 
An Investigator survey had a response rate of 8/32 (25%). All, 8/8 (100%) were satisfied and reported  the training improved the quality of research, was an efficient way to onboard new personnel, the value was worth the cost and that they would be likely to use again. 
The latest effort is directed at senior research staff. A two-day, in-person Advanced Topics in Clinical Research (ATCR) course was held in September 2025. Fourteen subject matter experts to instructed on recruiting, working with the IRB, contracting and budgeting, inspection prep, REDCap and much more. Feedback from the attendees was very positive. 
All of these initiatives are fee-for-service in order to  create an enriching and self-sustaining environment that can dynamically adjust and adapt to ever changing needs.  
A formal Steering Committee comprised of Senior leadership and Faculty clients meets quarterly to review extensive metric reporting and staffing predictions to evaluate program success and make recommendations.
Budget belt-tightening requires organizations to reduce expenses while continuing to provide essential services. This focus has caused RCSS to examine our program and add efficiencies. We hope others looking to build or expand their apprentice programs, float pools and/or onboarding programs will benefit from our experiences and the specific efficiencies we implemented. 


Wright Regional Center for Clinical and Translational Science at Virginia Commonwealth University (VCU) has identified a gap in the research process: a need to provide opportunities to enhance research earlier on. The center's External Advisory Board also highlighted a need for greater collaboration rather than one-off consultations to improve scientific outcomes.

To address these needs, the center proposes Design Studios, which are "interactive roundtable discussions that bring relevant research experts from diverse academic disciplines together to focus on a research project, early in the design phase". These no-cost sessions are intended for clinical and translational researchers in the VCU environment and its partner institutions. The primary goal is to enhance the scientific rigor of investigations early in the design phase, increasing the likelihood that successful execution will lead to rigorous and impactful findings.

The Design Studio model targets new and early-stage researchers with high relevance to the Wright Center's mission. Eligibility requires an identified grant mechanism, a specific research question, and, for early-stage or non-independent researchers, a mentor or senior collaborator who will support the implementation of the studio's recommendations.

Next steps for the initiative include piloting the program with K12 scholars and K Club participants, refining processes, and advertising the Design Studios more broadly. The center plans to continue tracking outcomes and examining data to further refine and enhance the model's effectiveness.

Collaboration to Enhance Science: Establishing Research Design Studios

People with HIV (PWH) have increased risk of complications from measles. Only 11.2% of 13,622 PWH in one U.S. clinic had presumptive measles immunity. The most significant correlates of seronegativity were younger age, white race, and CD4 count <200 cells/µL. Increasing measles immunity in PWH is essential given increasing outbreaks.

CTSA Prioritized BERD Support for Rapid Publication of Measles Immunity in Large Urban HIV Clinic

The University of Washington (UW) ITHS TL1 mentored predoctoral training program is designed to grow trainees' competence and knowledge in translational science. The successful expansion of the program to include trainees at Montana State University (MSU) was the first step in our long-term objective to create an accessible training program across the 5-state WWAMI (Washington, Wyoming, Alaska, Montana, and Idaho) region. The expansion amplifies the robust academic research networks of both institutions, brings new learning opportunities for students, and supports research that addresses public health needs more broadly in the WWAMI region.

Curriculum development in the expanded TL1 program is built on the foundation of Fundamental Characteristics and Core Competencies, addresses WWAMI research priorities, and bi-annual trainee feedback. Specific pedagogical innovations used to realize the translational science training goals include: 1) intentionally creating a cross-disciplinary cohort of emerging researchers, 2) a focus on individualized career development and team science skills using hybrid, synchronous, and asynchronous methods, 3) multiple methods for learning and practicing transdisciplinary communication, 4) real world practicums outside of their field, and 5) practicing transdisciplinary skills to address a Grand Challenge (Orca Tank).  

Developing a curriculum to train the next generation of translational scientists: Lessons from our regional TL1 expansion

To cultivate early interest in clinical research and strengthen the STEM pipeline, our team developed the Next STEPP (STEM, Technology and Educational Pathways Program) Clinical Research Summer Camp, a collaborative, multi-institutional initiative designed for high school students. Recognizing the importance of reaching the future STEM workforce earlier in their career journey, we applied team science and project management approaches to form, launch and evaluate the initiative. We grounded our curriculum and pedagogy in Bandura’s Social Learning Theory. These principles informed the development of interactive lesson plans and hands-on activities that emphasized experiential learning and peer collaboration. 
Held in Columbus, Ohio, the pilot camp, targeted rising sophomores and juniors from local high schools. Of 100 applicants, 34 students were accepted, with 33 finishing. The curriculum spanned four days and included laboratory techniques, informed consent simulations, and exposure to cutting-edge technologies. Students worked in teams to design clinical research questions, develop study protocols, and defend their projects through poster presentations to an audience of peers, parents, and professionals.
A key feature of the camp was its emphasis on career exploration. Students engaged with a panel of clinical research professionals (CRPs) across varying organizations, gaining insight into roles within the field. Students were also exposed to a pediatric study participant and their caregiver. This exposure was designed to help students envision future career pathways and understand the real-world impact of clinical research.
Evaluation methods included pre and post-test self-efficacy skills assessments across clinical research skills covered in the camp, as well as mixed methods surveys capturing achievements of personal learning goals, perceptions of learning, session quality, and content experiences. Camp facilitator evaluations provided additional insights into logistical challenges, safety considerations, and feasibility of scaling the program. Feedback from both students and staff highlighted the camp’s success in fostering engagement, learning, and motivation. Students especially valued the interactive components and expressed interest in more hands-on activities and expanded speaker representation. Key findings include identification of facilitators and barriers to successful implementation, strategies for forming collaborative teams, and best practices for curriculum design using social learning theory.
The team is developing a reproducible toolkit, including lesson plans, evaluation instruments, and implementation guidelines. Plans are underway to expand the camp to include additional cohorts and launch a high school clinical research club to sustain engagement throughout the academic year. The camp offers a scalable model for inspiring the next generation of CRPs and advancing STEM education through meaningful, hands-on experiences. 

Developing Future Researchers: The Next STEPP Clinical Research High School Summer Camp

Objectives/Goals: Skilled clinical research professionals are essential to efficient and effective research teams, but many undergraduate students are not aware of staff-level careers in the field. To address this, the Michigan Institute for Clinical and Health Research launched the CHRP Pathways Program.

Methods/Study Populations: The 10-week hybrid Clinical and Health Research Professional (CHRP) Pathways Program was piloted in Summer 2024. Undergraduate students interested in research careers were recruited from local universities and community colleges. The program consisted of 8 hours/week of didactic curriculum guided by ECRPTQ competencies and 32 hours/week of mentored work experience on research teams. Classroom activities were completed with participants from a partner program within the university. Surveys were administered at 3 weeks and 10 weeks along with an end-of-program focus group to assess acceptability and self-reported learning outcomes.

Results/Anticipated Results: Two cohorts of CHRP program enrolled 6 students and was well-received by students and mentor research teams. Students agreed that they would recommend the program to their classmates. Students indicated that the program helped them understand the role of a study coordinator, provided insight into research career paths, and helped them form professional relationships. Self-reported confidence levels in a range of research competencies increased.  Three students who completed the 10-week program chose to continue working part-time with their research teams while continuing their undergraduate studies.

Discussion/Significance: Experiences like the CHRP Pathways Program provide valuable exposure to staff-level research career opportunities for students engaged in health science studies. They can address an existing workforce gap by equipping college graduates with relevant work experience and basic research competencies. 


Development and Implementation of a Pilot Summer Training Program for Clinical Research Professionals

While the Montefiore-Einstein ICTR has productive relationships with community partners in all manners of research, there was no mechanism for tracking collaborations in all stages of the research spectrum to facilitate and sustain research initiatives and the ICTR's larger goals. Together with the College of Medicine, Montefiore-Einstein Offices of Community and Population Health, Community Affairs, and the Montefiore-Einstein Comprehensive Cancer Center, the Einstein ICTR Community Collaborative Core (CCC) developed the Montefiore-Einstein Community Engagement Tracker. This tracking system aims to connect researchers with community partners with shared priorities, build capacity and catalyze meaningful clinical and translational research partnerships and monitor past and current community collaborations. After collecting over 200 pieces of feedback from 36 stakeholder groups, the CCC established four interlinked REDCap databases to accurately capture the scope and breadth of community engagement our institution facilitates. Within the Organizations, Programs, Events, and Individuals databases, we capture organizational contacts and priority areas served, research and health-based programs sponsored by us, event and recruitment fair details and populations served, and secure information on individual contacts, respectively. We have 38 active users who have used multiple databases to track their community-based initiatives and partners, and we are currently rolling out the tracker to the wider Montefiore-Einstein faculty. The CCC aims to continually refine the tracking system, develop dashboards and reports, develop evaluations built into data capture and integrate iterative feedback form users as data is entered, and collaborate with other CTSA institutions utilizing similar data management structures over time with the aim of catalyzing and sustaining meaningful clinical and translational research partnerships.

Development of the Montefiore-Einstein Community Engagement Tracker 

Rare diseases impact over 30 million Americans, yet patients often face delayed diagnoses, limited treatment options, and fragmented care. In South Carolina, the Rare Disease Advisory Council (RDAC) was established in 2022 and is housed within the Medical University of South Carolina’s Clinical and Translational Science Award (CTSA) hub, the South Carolina Clinical & Translational Research Institute (SCTR). This poster highlights how this unique administrative structure supports community and stakeholder engagement, enhances research infrastructure, and informs translational science efforts. 

The RDAC facilitates collaboration among patients, caregivers, clinicians, researchers, and policymakers through initiatives such as the annual Rare Disease Symposium and a statewide needs assessment. These activities have helped identify key barriers to care and research participation, while amplifying patient voices and lived experiences. Additionally, a pilot study using claims data has begun to address the lack of reliable prevalence data for rare diseases in the state. 

By aligning RDAC efforts with CTSA resources and priorities, South Carolina is strengthening its capacity to respond to public health needs and support community-informed research. This approach demonstrates the value of integrating stakeholder councils into translational research infrastructure to ensure relevance, impact, and sustainability. 

Embedding a State Rare Disease Advisory Council Within a CTSA Hub to Advance Community-Driven Translational Research

Dartmouth SYNERGY serves the largely rural region of New Hampshire and Vermont with partnerships across northern New England. SYNERGY’s Element E Clinical and Translational Science (CTS) Research Program addresses the siloing between disciplines, siloing between translational scientists and health system administrative leaders and the timely acquisition, integration and reporting of data necessary for an effective learning health system (LHS). Our objectives are to introduce Dartmouth’s model of embedding LHS scientists and to highlight key Dartmouth resources that facilitate LHS research, training, and dissemination including: (1) SYNERGY’s Element E research program, (2) Collaborations between Dartmouth College, Dartmouth Health, and community partners via the Center for Advancing Rural Health Equity, (3) Dartmouth’s LHS Embedded Scientist Training and Research (E-STaR) Center, and (4) the Dartmouth Journal of Health Care Delivery Science. 
Element E’s goals are to (1) accelerate translational science through projects that address the LHS “data engine”, which entails patient-generated data capture, data integration and reporting to support the implementation/application of evidence-based practices, care improvement and population health tracking and (2) launch a CTS Learning Collaborative to build a community of practice and innovation incubator that works to mitigate barriers, network, support, scale and accelerate the development and adoption of evidence-based practices and to enhance the well-being of all within our community. Initial projects are: Project 1: A clinical decision support system with agenda-setting tools to improve care engagement under the evidence-based Collaborative Care Model for treating behavioral health conditions within primary care, and; Project 2: Clinical use of a digital biomarker from passively collected smartphone data to trigger timely care for behavioral health patients who are completing Collaborative Care. Both projects address unmet needs for behavioral health patients and have generalizability to other populations.
Dartmouth’s E-STaR Center is focused on training the next generation of embedded LHS scientists with a focus on projects addressing the unmet health care needs faced by rural communities. Trainees engage in an 18-month experiential learning program for clinicians and non-clinicians featuring coproduction wherein the collective expertise of care teams, patients, and care partners guide care innovation. Monthly seminars address LHS competencies, patient-centered outcomes research and comparative effectiveness research.
A new open-source journal, the Dartmouth Journal of Health Care Delivery Science, which will debut in February 2026, will provide a new avenue for embedded LHS scientists to share their research findings and lessons learned with the broader academic community.



Embedding Learning Health System Scientists to Accelerate Discovery and Impact at Dartmouth

Effective science communication is essential for translating research into real-world impact, yet formal training opportunities remain limited across the biomedical workforce and are often restricted to specific career stages. To address this gap, the Northwestern University Clinical and Translational Sciences (NUCATS) Institute integrated the Art of Science Communication (ASC) course—originally developed by the American Society for Biochemistry and Molecular Biology—into its professional development offerings beginning in 2024. This blended model supports faculty and staff in building skills to engage non-expert audiences and communicate the significance of their work.

The ASC course follows a flipped classroom model over eight weeks, combining asynchronous video lectures with live discussion sessions led by trained facilitators. Participants develop and refine a science presentation tailored to a non-expert audience, culminating in a final recorded talk. Each week includes homework assignments, peer review, and guided discussions to reinforce learning and foster community.

In its first year, the course received overwhelming interest, with over 115 individuals expressing intent to participate. A total of five sessions were delivered across Fall 2024 and Winter 2025. Preliminary evaluation data highlight both the strengths of the course and opportunities for continuous improvement, allowing for further tailoring to institutional needs. This reflects a scalable and adaptable approach to integrating science communication training within a UM1 Hub, leveraging an established curriculum with demonstrated outcomes.

Embedding Science Communication Training in a UM1 Hub to Enhance Research Impact

•	Identifying relevant expertise across domains is challenging, especially with complex scientific and technical profiles.
•	We introduce EXPERT, an artificial intelligence (AI) driven platform using large language model (LLM), graph-based semantic search, and Retrieval-Augmented Generation (RAG) to identify expert profiles matching requirements.
•	The system utilizes agentic AI through ontology-aware agents that autonomously interpret queries and choose optimal retrieval strategies using a knowledge graph.
•	Aligned with CTSA goals, EXPERT enables scalable, context-aware decision-making and helps identify collaborators, mentors, and domain experts.


Expertise & Profile Engine for Repository of Talent (EXPERT): An Agentic AI Platform for Advancing Collaboration in Clinical and Translational Research

Background and Approach: CTSA hubs must deliver decision-ready evidence for daily management and meet reporting requirements. This dual responsibility often results in duplicate data entry and delayed insights. To address this, our Clinical & Translational Science Collaborative (CTSC) of Northern Ohio Evaluation Core reframed evaluation by using a system designed for immediate use and minimal burden. The system aligns with NCATS priorities and supports the CTSA program's operational innovation. We prioritize people and decisions over tools. Our participatory evaluation sessions create shared definitions and clarify who needs which insights and when. To ensure comparability and avoid the creation of new silos, we mapped indicators to the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance; Gaglio et al., 2013; Holtrop et al., 2021) and to NCATS concept mapping metrics (Kane et al., 2025). The reporting system runs on real-time dashboards, drawing from our centralized MyCTSC data warehouse.

Innovation:
1. Participatory, utilization-focused design: We employed a participatory approach to co-design outcome-based evaluation systems with module leaders and administrators. Drawing from logic models, the RE-AIM framework, and NCATS concept mapping, this system aligns evaluation criteria from module to hub to consortium. These sessions reduce ad-hoc data requests and enable efficient reporting, emphasizing the immediate collaborative benefits. The design creates a learning loop, making feedback cycles central to building collective capability, not just improving metrics. By integrating iterative learning, the system adapts in collaboration with stakeholders to facilitate continuous improvement and shared growth.
2. Centralized data warehouse: We use MyCTSC, a centralized platform that consolidates data from sources such as REDCap (a tool for collecting and managing web-based surveys and databases), SPARCRequest (a system for managing research service requests), InfoReady (software for tracking and managing applications), Constant Contact (an email marketing tool), Canvas Catalog (software for registering for online courses), and Overton (a platform for accessing policy documents). This database supports reporting, quality improvement, and evaluation, ensuring data integrity, completeness, and reliability.
3. Tableau dashboards: Leaders monitor core metrics like user engagement and resource allocation for immediate insights into performance and progress. Data drives interactive Tableau dashboards for SPARC, quarterly, and outcome reporting, making impact and performance visible.

Significance: This strategic evaluation system demonstrates resilience (rapid visibility despite shifting priorities), innovation (a centralized database powering real-time dashboards), and impact (timely use of evidence).

From Burden to Benefit: Designing Real-Time Dashboards through RE-AIM and Participatory Evaluation

Early studies of large language models (LLMs) in clinical settings have largely treated artificial intelligence (AI) as a tool rather than an active collaborator. As LLMs now demonstrate expert-level diagnostic performance, the focus shifts from whether AI can offer valuable suggestions to how it can be effectively integrated into physicians’ diagnostic workflows. We conducted a randomized controlled trial (n=70 clinicians) to evaluate the value of employing a custom GPT system designed to engage collaboratively with clinicians on diagnostic reasoning challenges. We evaluated two workflow variants: one where the AI provided an initial opinion (AI-first), and another where it followed the clinician’s assessment (AI-second). Clinicians using either collaborative workflow significantly outperformed those using traditional tools. Our hub has been preparing and learning about the data coordination that is needed for this technology as their studies and clinical trials become relevant to translational science.


From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis

The Indiana Network for Patient Care (INPC), one of the nation’s largest Health Information Exchanges, contains data from over 18 million patients but has been challenging to use for research due to its complexity and heterogeneity. To address this, the data is standardized into the Observational Medical Outcome Partnership, Common data Model (OMOP CDM)1 and integrated with Observational Health Data Science and Informatics (OHDSI) Atlas3 (Cohort Discovery Tool), enabling transparent and reproducible cohort discovery. Since April 2024, this resource has supported research through user training, governance, and data quality efforts, with plans to expand across Indiana Clinical and Translation Science Institute(CTSI) partners.


How can we democratize access to clinical data at scale?

In the complex and often siloed environment of research operations at large academic medical centers, promising ideas and innovations can get lost or overlooked when relying solely on traditional feedback mechanisms such as suggestion boxes, team meetings or annual surveys.

To address this challenge and foster more iterative, transparent communication, the NYU Clinical and Translational Science Institute (CTSI) launched a structured crowdsourcing initiative using the IdeaScale platform. Designed to solicit, evaluate, and act on ideas from researchers, clinicians and nurses, staff and administrators, and students and trainees, the initiative surfaced high-value innovations while strengthening engagement across the research community. This poster presents its implementation and early outcomes, highlighting a replicable model for improving research operations through collaborative problem-solving and data-informed innovation.

Notably, "Communication & Recognition" emerged as the most frequent theme, highlighting not only the importance of improving institutional communication, but also suggesting that the IdeaScale platform itself may serve as a valuable communication innovation. This CTSI crowdsourcing effort helped members of our research community share new ideas by providing an open and participatory feedback system to improve communication and drive broader organizational change.

Illuminating Innovations: Ideas that Rise to the Top. Activating the power of crowdsourcing to shine a light on barriers and actionable solutions to advance clinical and translational science.

Background: Effective program development in clinical and translational science (CTS) requires structured planning, stakeholder engagement, and continuous evaluation. To address this need, SC CTSI Workforce Development developed the IMplementation and Program Action Coordination Toolkit (IMPACT) Guide, a lifecycle-based framework designed to support coordination and implementation for CTS educational programs. 

Objectives: The IMPACT Guide offers a standardized yet adaptable framework for designing learner-centered, measurable, and scalable CTS educational programs. By organizing complex tasks into manageable stages, the guide enhances program quality and credibility, promotes consistency across SC CTSI cores, and long-term impact. It supports alignment with CTSA goals and NCATS Translational Science Principles: catalytic, collaborative, generalizable, innovative, patient-focused, and measurable. The guide serves as a centralized planning and documentation tool that can be revisited regularly to refine programs, track milestones, and extend impact over time.

Methods: Structured around a five-phase lifecycle: Program Foundation → Design → Implementation → Evaluation → Dissemination, the guide includes reflection prompts, clear instructions, planning tools, templates, and timelines. Enhancements based on instructional design research and pilot feedback include tools for formative assessment, accessibility planning, continuous quality improvement, and instructional alignment. The guide is modular, user-friendly, and flexible, supporting various program formats and learning styles. Optional worksheets support brainstorming, documentation, and team collaboration.

Results: Launched in early 2025, the IMPACT Guide has supported multiple SC CTSI educational programs. Pilot testing with users of varied backgrounds and experience levels helped refine usability and engagement. Teams reported improved clarity in program structure, stronger alignment with NCATS goals, and increased confidence in evaluation planning. The guide now supports onboarding of new and existing initiatives, cross-functional alignment, and ongoing program tracking. Upon completion, hub leadership can review the guide to gain a comprehensive overview of the planned program.

Conclusions: The IMPACT Guide offers a practical, scalable solution for advancing clinical and translational science educational programs through structured planning. It enables teams to work systematically toward clear outcomes, transforming program development from an informal process into a strategic, repeatable approach. With its lifecycle structure and collaborative design, the guide promotes strategic alignment, continuous improvement, and generalizable innovations that enhance the efficiency and effectiveness of translation. By serving as a centralized planning and documentation tool, the guide supports program quality, consistency, and long-term impact across SC CTSI cores.

IMPACT Guide: A Lifecycle Framework to Design, Implement, and Evaluate Clinical and Translational Science Educational Programs

UC Davis Health has implemented the Universal Consent Registry (UCR) to provide an ethical, scalable framework for research participation and biospecimen access. Integrated directly into the Epic electronic medical record, UCR allows patients to opt in through MyChart or during clinic check-in, ensuring transparency and minimizing staff burden. Since launch, more than 12,000 patients have consented, with fewer than 1% withdrawing, demonstrating both feasibility and patient trust. 

To operationalize these consents, UC Davis developed BioExplorer, an informatics platform that links consent status with biospecimen availability. This integration enables daily, automated identification of remnant clinical specimens, facilitates streamlined investigator access, and supports compliance with NIH guidelines for the use of de-identified data and samples. 

Together, UCR and BioExplorer function as both a research resource and a data science innovation, providing investigators with a reliable, equitable, and efficient pathway to biospecimens and linked clinical data. This poster highlights lessons learned in implementation, governance, and workflow integration, illustrating how UC Davis has built a sustainable infrastructure that accelerates translational research while fostering patient-centered participation. 

*Note that poster uploaded is an example - CTSA Fall Program Meeting Poster will be slightly altered, highlighting different aspects of this project. 

Implementing universal consent to leverage health system biospecimen and data resources for research 

UC Davis Health has implemented the Universal Consent Registry (UCR) to provide an ethical, scalable framework for research participation and biospecimen access. Integrated directly into the Epic electronic medical record, UCR allows patients to opt in through MyChart or during clinic check-in, ensuring transparency and minimizing staff burden. Since launch, more than 12,000 patients have consented, with fewer than 1% withdrawing, demonstrating both feasibility and patient trust. 

To operationalize these consents, UC Davis Health developed BioExplorer, an informatics platform that links consent status with biospecimen availability. This integration enables daily, automated identification of remnant clinical specimens, facilitates streamlined investigator access, and supports compliance with NIH guidelines for the use of de-identified data and samples. 

Together, UCR and BioExplorer function as both a research resource and a data science innovation, providing investigators with a reliable, equitable, and efficient pathway to biospecimens and linked clinical data. This poster highlights lessons learned in implementation, governance, and workflow integration, illustrating how UC Davis Health has built a sustainable infrastructure that accelerates translational research while fostering patient-centered participation utilizing the centralized infrastructure support of its Clinical and Translational Science Center. 

Innovating robust career pathways for undergraduate students

Clinical and translational science (CTS) and CTS research require a well-trained, transdisciplinary workforce prepared with key skill sets and experience with health systems. Thus, capitalizing on several aspects of its multicampus system, the UMass Center for Clinical and Translational Science (UMCCTS) sought to innovate robust career pathway programs through which to engage potential workforce participants at a critical juncture in their career decision-making. Three programs, EXcellence in Clinical REsearch and Leadership – Undergraduate Pathway (EXCEL-UP), Bridging Engineering And Medicine (BEAM), and Building Research And Innovation in the Neurosciences through Technology (BRAIN-Tech) were designed to engage and train undergraduate students from across the UMass campus system by pairing them with clinical and translational research mentors at UMass Chan Medical School or at clinical partner sites UMass Memorial Health and Lahey Hospital & Medical Center. EXCEL-UP provides students with a 10-week, full-time summer internship in clinical research, including foundational didactics and mentored experience as an embedded member of a clinical research team. BEAM offers multiple options for engineering student engagement, including research for course credit (9 hrs/week tech elective and/or 2-semester thesis project); a 10-week, full-time summer internship; and an engineering senior design capstone. BRAIN-Tech proposes to furnish technology-focused students with similar mentored experiences through research for course credit or a 10-week, full-time summer-internship in the neurosciences, along with experiential training in technology transfer. Program outcomes two years after participation in EXCEL-UP show exceptional workforce placement impact (91% of program alumni in clinical research or other health/biotechnology employment or graduate programs). While newer, BEAM has shown sustained pathway participation and development of new CTS research bridges across the UMass campus system.

Insights were gained from a community engagement studio aimed at addressing anxiety in a remote rural area in New Mexico. The studio involved eight local experts representing various sectors from the community, including healthcare, education, and faith-based organizations. These experts identified key insights surrounding anxiety, including its causes, community perceptions of mental health services, and strategies for addressing anxiety within the community. Contributions included a rubber band metaphor to describe lingering anxiety, the role of societal pressures (particularly for men), and the impact of physical activity and infrastructure on mental well-being. The studio also highlighted the importance of integrating faith-based support and promoting community pride as a mental health intervention. Our insights underscore the value of community engagement in understanding and addressing mental health challenges in rural settings.

Integrating Culture into Care: Leveraging Community Engagement to Address Anxiety in a Rural Community

Vanderbilt Center for Learning Healthcare
Mission: 
Bridge gaps in evidence for common treatments and accelerate the implementation of evidence-based care by integrating scientific discovery with healthcare delivery.
Key Objectives:
	Generate Knowledge: Embed pragmatic trials within clinical care to identify the most effective treatments and healthcare delivery approaches for optimal patient outcomes.
	Improve Outcomes: Implement evidence-based treatments across the health system to enhance patient care.
Core Activities:
	Architect and operationalize infrastructure to empower the conduct and support of rigorous comparative effectiveness research, with a focus on pragmatic trials embedded within clinical care.
	Innovate methods and provide training for researchers and clinicians in pragmatic trial design and execution.


Learning Healthcare: Managing Partnerships, Pathways, and Pragmatic Trials

Pilot programs are essential for launching innovative research ideas, allowing investigators to test hypotheses and refine methodologies before scaling to larger studies. Recognizing this, the Clinical and Translational Science Institute (CTSI) collaborated with leadership from the Dean’s Office and the Institutional Research Centers to establish a centralized Institutional Pilot Program at Wake Forest University School of Medicine. Modeled after the successful CTSI Pilot framework, this program streamlines administrative processes, enhances co-funding opportunities, and improves return-on-investment tracking. It also serves as a strategic pipeline for developing new Research Program initiatives. While maintaining autonomy over funding decisions, review processes, and award amounts, participating centers benefit from shared infrastructure. A standardized Request for Applications template and a centralized REDCap eApplication system were developed to simplify submissions. Investigators can apply to up to two centers simultaneously, increasing their likelihood of receiving funding or co-funding. This integrated approach fosters collaboration, reduces administrative burden, and promotes efficient resource utilization across research centers.

Leveraging a CTSA Pilot Model to Launch an Institutional Pilot Program

The UCSF Participant Recruitment Program (PRP) implemented an AI-assisted workflow enabling a small team to deliver large-scale, compliant social media recruitment services. Historically, developing effective advertisements for Meta platforms was resource-intensive and slow, limiting the number of supported studies and delaying campaign launches.

In 2023, PRP developed a structured prompt framework embedding compliance requirements, NIH guidance on equitable language, person-centered communication principles, plain-language best practices, peer-reviewed strategies, and campaign performance data. Transitioning to Versa AI (UCSF’s compliant GPT-5–based tool), the workflow enables iterative refinement to ensure outputs remain accurate, inclusive, and respectful.
This innovation produced measurable improvements:

• Pre-AI (before May 2023): 7.45M unique social media users reached; average cost-per-click (CPC) $1.54
• ChatGPT pilot (May–Sept 2023): CPC $0.46
• Versa AI launch (Oct 2023 onward): 32.7M users reached; CPC $0.34

These results represent a nearly 80% reduction in CPC compared to the pre-AI period. The number of monthly active studies expanded from fewer than 10 in 2022 to more than 25 by late 2024. Recruitment budgets now generate substantially greater engagement per dollar, while extending outreach to diverse communities.
This work shows how a small recruitment team can expand services responsibly while maintaining compliance and respect for participants. The workflow is built on clear prompt standards, careful review, and continuous updates based on compliance guidance and campaign performance. Next steps include advancing AI-assisted prompt development to better support ADA compliance, launching a self-service dashboard in late 2025 to further streamline operations, and expanding into new creative formats such as infographics and short-form video. Together, these efforts position the workflow as a scalable model to strengthen recruitment infrastructure across CTSA hubs.


Leveraging AI and Prompt Engineering to Scale Up a Social Media Recruitment Service

There is a growing recognition of the importance of quantitative training to support Tribally driven health research. We aimed to develop a track to foster Tribal scholars in developing needed skills in data science and biostatistics, but availability of resources posed a challenge. By partnering with our CTSA, the Oregon Clinical and Translational Research Institute (OCTRI), we have leveraged resources to support trainees from Tribes and Tribal communities in developing data science and biostatistics skills needed to strive for their educational and career goals.

Leveraging CTSA to Support Program Fostering Tribal Scholars in Data Science and Biostatistics

University of Illinois Chicago’s (UIC) Center for Clinical and Translational Science (CCTS) created a new consultative services system to improve cross-service collaboration. This system consists of a team of Research Navigators and an in-house built consultation management system (Research Ally) that together improve the efficiency and output of consultations at CCTS. 
The new consultative services system added 2.5 FTE Research Navigators. Research Navigators are the central point of contact for research teams seeking consultative services. Research Navigators work directly with research teams and service groups to shepherd projects through the consultative process and stay engaged throughout the entire lifespan of the research project. They provide holistic oversight of projects, ensuring that research teams receive comprehensive support that spans multiple service areas. Research Navigators enable CCTS to have a firmer connection with research teams throughout the consultative process and ensure warm hand-offs between different service groups, decreasing the need for research teams to bring peer scientists up to speed as projects evolve and grow. This approach not only streamlines communication and coordination, but also frees peer scientists to concentrate on their specialized expertise and core responsibilities.
CCTS also developed the Research Ally web application, which allows research teams to more easily request assistance, track their project’s services, manage billing, and understand the suite of services provided to them. Creating Research Ally allowed CCTS to better understand how many research teams were using consultative services, which services were being utilized, and cross-service utilization – information that was imperfectly tracked in the prior system. Together, Research Ally and Research Navigators increase the operational efficiency at CCTS and improve data tracking for consultative services. 
Direct feedback from research teams and peer scientists on the new consultative system is overwhelmingly positive, especially the addition of the Research Navigators. Research teams have a clearer understanding of how the consultative system works, can quickly establish connections with CCTS service groups, and track the status of their consultations through Research Ally. 
Research Ally delivers efficiencies by providing a user-friendly interface that allows for easier navigation on a platform built in-house. This platform provides a centralized location for project information, which gives peer scientists and research teams a holistic view of their project and a streamlined project management approach. Dedicated Research Navigators provide personalized guidance for UIC research teams, serving as a single point of contact to coordinate services across multiple groups. This streamlined approach improves communication, freeing CCTS’ peer scientists to focus on their specialties, and supports the research team’s overall goals.


Make It Better: Improving service access and operational efficiency

Objective
Early-career scientists frequently face barriers in translating laboratory discoveries into clinical and community impact. These barriers include limited understanding of the translational process, challenges in interdisciplinary collaboration, and difficulty designing research that produces meaningful outcomes for stakeholders and communities. To address these gaps, the Miami CTSI has developed a 2.5-day intensive and immersive Translational Science Course aimed at equipping emerging scientists with the knowledge, skills, and confidence to navigate the full translational research spectrum. Survey feedback from the UM research community informed the course content, ensuring it targeted commonly identified barriers and provided practical strategies to advance research from discovery to real-world application. 

Methods
Prior to course development, CTSI stakeholders completed a structured survey to identify common barriers to translational research. Seventeen participants were guided by 17 experienced facilitators through a structured curriculum consisting of goal-mapping exercises, interactive case-based discussions, and networking activities. Pre- and post-course surveys quantitatively assessed changes in confidence, understanding of the translational spectrum, and intentions to apply course concepts in their work. The curriculum encompassed 22 interactive sessions, case-based discussions, and network-building activities designed around six core competency domains:
1.	Understanding the Translational Process
2.	Interdisciplinary Integration
3.	Collaboration and Communication in Team Science
4.	Fostering Creativity in Science
5.	Innovation and Entrepreneurship
6.	Challenges and Solutions in Linking Sectors in Clinical and Translational Science

Results
Participants reported significant gains in confidence across multiple domains, including interdisciplinary integration, collaboration, communication, creativity, innovation, and entrepreneurship. Post-course feedback indicated that participants developed actionable plans for advancing translational research, gained clarity on navigating barriers, and established connections with mentors and peers. Qualitative feedback emphasized the value of the immersive format and collaborative environment in enhancing learning and engagement.

Conclusion
•	Miami Translational Science course equips early-career scientists in translational science principles
•	Curriculum addressed clinical and translational science barriers and developed a deeper understanding of the translational process
•	Participants reported gaining practical knowledge and confidence to advance their research from discovery to real world impact

Miami CTSI’s Translational Science Course

The New Jersey Alliance for Clinical and Translational Science (NJ ACTS) has implemented Smartsheet as a centralized, cloud-based platform to optimize the management of CTSA resources, projects, and reporting. This initiative enhances coordination, transparency, and efficiency across translational science activities. By standardizing intake processes, engaging investigators directly, and visualizing data through dynamic dashboards, Smartsheet supports equitable access to resources and reduces administrative burden. The framework enables continuous tracking, feedback, and aggregated reporting, streamlining RPPR and institutional submissions. This scalable model fosters collaboration, accelerates research, and provides a replicable approach for other CTSA hubs. Supported by NJ ACTS Grant UM1TR004789, this work demonstrates the transformative impact of digital platforms in advancing translational science.

Optimizing Resource Utilization Through Smartsheet:  A Framework for Advancing Translational Science

Pilot Wrap-Around Support Initiative: Enhanced support for internally-funded CTSA pilot awards to increase project success and build enduring teams

Penn State CTSI provides annual pilot funding opportunities, including the Bridges to Translation and the Translational Science Pilot Funding Program, with the goal of advancing clinical and translational research and translational science. Since 2012, the CTSI has funded 138 projects producing a return on invest of $15 in follow-on funding for every $1 invested. To strengthen the quality of proposals received and support funded projects across the pilot lifecycle, the CTSI launched a wrap-around support initiative in 2023. The primary goal of the initiative is to provide support to researchers to improve the timely and successful completion of project aims with a high likelihood of being awarded follow-on funding to advance clinical and translational science. The secondary goal is to help with the establishment of multidisciplinary teams and boundary-crossing partnerships through cross-disciplinary match-making and foundational team science training. To achieve these goals, the initiative provided faculty with access to the following: 1) a translational science educational session; 2) voluntary and facilitated CTSA Core consultations during the application development timeline; 3) team science collaboration planning sessions for all funded teams; and 4) an orientation to educate and assist teams with the internal and NCATS regulatory processes. 
CTSI received 23 total full applications during the 2023 and 2024 pilot funding cycles. Of the 23 project PIs, 22 participated in CTSI Core consultations during their proposal development. Teams engaged in an average of 2.2 consultations (range: 0-4). The average for funded teams was 2.1 consultations while the average for unfunded teams was 2.3 consultations. Both funded and unfunded teams reported that the CTSI Core consultations provided valuable expertise, fostered new collaborations, and strengthened the proposal. The team science collaboration planning sessions for funded teams were also a valuable experience. Eight of eleven post-session follow-up survey (6 months) respondents agreed or strongly agreed the training enhanced the team’s ability to collaborate effectively.
This wrap-around support model has been extended to pilot projects supported through the Consortium of Rural States (CORES) multi-institutional pilot funding opportunity. CORES is a nine-hub CTSA collaboration, and funding proposals require cross-institutional partnerships. One exemplar project by Drs. Calo (PSU) and Adsul (UNM), addresses the lack of inclusion of rural priorities and inconsistencies in the selection, adaptation, and implementation of EBIs for cancer prevention and control. Using Community Implementation Studios, the project integrates structured consultations/studios, self-paced training, technical assistance, and implementation plan development to adapt evidence-based interventions for community use.


Portfolio of Medical Foundation Models to Accelerate Clinical and Translational Research

Real-World Data holds significant promise to advance personalized medicine through improved prediction of clinical events, diagnoses, and treatment responses across all domains of medicine. However, existing analytical frameworks fall short in their ability to cover the breadth of input data and downstream tasks required by clinical and translational research. Here we present a Hub Initiative that leverages our expertise in deep learning and secure high-performance GPU computing to create a portfolio of multimodal medical foundation models to accelerate clinical and translational research across all fields of medicine. Medical foundation models are inspired by large language models, but instead of predicting sequences of words, they predict the sequence of all clinical observations, actions, and passage of time in longitudinal patient health records. Once trained, medical foundation models can predict any outcome present in the training data without task-specific training and outperform classical machine learning approaches. Our portfolio of models is supported by a secure high-performance computing pipeline and is designed to address key unanswered questions in the field and provide access to cutting-edge methods. In Stage 1 of the pipeline, we curate data sources, allowing researchers to study questions that require granular data, develop approaches compatible with federated learning, or benchmark novel methods in a reproducible manner. In Stage 2, we develop methods to improve tokenization and training to ensure that models are making optimal use of clinical data. In Stage 3, we provide ways for researchers to use these models to support answering a range of clinical and translational research questions. Using models to simulate patient trajectories will allow researchers from across departments to generate predictions for their specific questions without any additional model training.  Conditional outcome estimation, a novel extension developed by our group, supports target trial estimation and comparative effectiveness studies. Generation of patient embeddings allow for patient matching and phenotyping to support study recruitment and design. These stages combine to create a unified informatics platform to support cutting-edge research across medical fields.

Unresolved power dynamics within Clinical and Translational Research (CTR) teams can undermine collaboration, reduce productivity, and threaten long-term viability. The Duke CTSA’s Research Program (Element E) will address a critical gap in team science by developing and testing scalable, evidence-based interventions to help CTR teams recognize and manage power imbalances. Using a multiphase mixed-methods design, the study integrates qualitative interviews, survey development, and pilot testing of interventions grounded in implementation science and team effectiveness theory. Anticipated outcomes include increased understanding of power dynamics on teams, validated tools for assessing power dynamics, and a feasible, acceptable team science intervention ready for large-scale evaluation. By fostering more transparent and resilient collaboration, this work aims to accelerate the translation of scientific discoveries into clinical and community settings.

Power Dynamics and Collaboration in Clinical & Translational Research Teams

Slow and inadequate recruitment is a persistent and costly barrier in Clinical and Translational Research, driving prolonged study timelines and escalating budgets. Recruitment consumes more time and resources than any other research activity and is a leading cause of site closures, premature termination, and unsustainable cost overruns.1-3 Delays of one to six months are commonly linked to recruitment bottlenecks, with downstream effects on scientific progress.4 Recent studies haven’t substantially ameliorated these problems, suggesting that recruitment remains a common cause of inefficiency and failure in clinical trials.4-7 
This CTSA Element E program seeks to fill a preventative gap in the clinical study lifecycle, predicting risks to recruitment so that adjustments in the study or its operationalization can be made. To do this, we are systematically reviewing the literature to (1) identify tested recruitment interventions and the evidence supporting them, and (2) identify factors impacting recruitment performance. We are identifying top and low recruitment performers so that we can work with them to assess content validity of literature factors and identify local factors. To develop prediction models to recruitment risk, we will use ClinicalTrials.gov, Clinical Trial Management System (CTMS) and Institutional Review Board (IRB) data to populate identified factors and test predictive power using 500 completed local studies. The desire to predict and intervene in risks to recruitment is an important part of an ecosystem of recruitment support and management throughout the study lifecycle.
References
1.	Campbell MK, Snowdon C, Francis D, et al. Recruitment to randomised trials: strategies for trial enrollment and participation study. The STEPS study. Health Technol Assess. Nov 2007;11(48):iii, ix-105.
2.	Penberthy LT, Dahman BA, Petkov VI, DeShazo JP. Effort required in eligibility screening for clinical trials. J Oncol Pract. Nov 2012;8(6):365-70.
3.	Kasenda B, von Elm E, You J, et al. Prevalence, characteristics, and publication of discontinued randomized trials. JAMA. Mar 12 2014;311(10):1045-51. doi:10.1001/jama.2014.1361
4.	Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: Challenges and solutions. Perspect Clin Res. Apr-Jun 2020;11(2):64-69.
5.	Peralta G, Sanchez-Santiago B. Navigating the challenges of clinical trial professionals in the healthcare sector. Front Med (Lausanne). 2024;11:1400585.
6.	Unger JM, Vaidya R, Hershman DL, Minasian LM, Fleury ME. Systematic Review and Meta-Analysis of the Magnitude of Structural, Clinical, and Physician and Patient Barriers to Cancer Clinical Trial Participation. J Natl Cancer Inst. Mar 1 2019;111(3):245-255.
7.	Ebrahimi H, Megally S, Plotkin E, et al. Barriers to Clinical Trial Implementation Among Community Care Centers. JAMA Netw Open. Apr 1 2024;7(4):e248739.

Predicting Recruitment Risk Before a Trial Starts

Project REACH (Rural Experts Advancing Community Health) supports rural community leaders by providing health policy and leadership training to hone writing, analysis, and communication skills. The program connects participants with University of Minnesota (UMN) faculty, staff, students, and rural stakeholders statewide. Since July 2021,13 leaders from rural Minnesota have successfully completed the program and helped improve local health outcomes

Project REACH (Rural Experts Advancing Community Health): Health Policy and Leadership Training for Rural Minnesotans

The Scripps Hub Academic Research Core (SHARC) was established to address critical gaps in translational research by enhancing scientific access, fostering collaborations, and improving the utilization of core services, data tools, and infrastructure. SHARC is a collaborative initiative between the Scripps Research Translational Institute (SRTI) and Scripps Health, led by Dr. Eric J. Topol (SRTI and CTSA PI) and Dr. Athena Philis-Tsimikas (Scripps Health PI). The program is strategically managed by a team of experienced professionals specializing in statistical support, research navigation, and community engagement. SHARC provides critical support to clinical research professionals, scholars, trainees, and early-career investigators in the areas of consultations, infrastructure enhancement, and career development. Its overarching goal is to promote an interconnected and interoperable translational science ecosystem through the digitization and expansion of essential resources and services within the local network and across the CTSA network. The program focuses on the development, demonstration, deployment, and dissemination of innovative, locally validated services, as well as informatics-driven software, data, and analytic resources. These efforts aim to enhance hypothesis generation and study execution. SHARC’s primary goals include advancing translational science education and support services, achieving bioinformatics excellence, and enhancing biomedical informatics and EHR-based research capabilities. By adopting critical infrastructure and tools identified by the national CTSA network, SHARC aims to accelerate the translation of scientific discoveries into meaningful health outcomes.

SHARC Vision: Charting a Course to Advance Clinical and Translational Science Scripps Hub Academic Research Core (SHARC)

Background: Partnerships between private academic medical centers and public universities often face challenges from differing missions and governance models. Medical centers tend to operate with greater financial autonomy, manage large clinical practices, and research portfolios. In contrast, public universities are bound by state regulations, public accountability, and broader educational missions. These distinctions complicate research collaborations and resource sharing. Despite their distinct institutional cultures and research priorities, Baylor College of Medicine (BCM) and the University of Houston (UH) have come together to launch the Consortium for Translational and Precision Health (CTPH). The goal of CTPH is to build sustainable partnerships that serve communities and advance clinical and translational science. Recognizing the need for a collaborative framework that prioritizes sustainable partnerships through shared leadership, the partnership is grounded in Community-Based Participatory Research (CBPR) principles. Methods: In its first 11 months, the CTPH prioritized dismantling institutional silos by applying CBPR principles and launching targeted strategies to establish a strong foundation for collaboration. We established a co-leadership model and a shared sense of identity to bind the CTPH community. We convened cross-sector working groups that leveraged the skills and knowledge of members from both institutions to identify priorities and co-develop actionable goals. These priorities include transparent communication, shared research infrastructure, sustainable opportunities for research collaborations and co-learning, and community-driven solutions that can transform both institutions and the communities they serve. Results: The CBPR-guided approach led to the formalization of a partnership agreement between BCM and UH to establish CTPH as a transformative partnership driven by trust, resource sharing, mutual respect, and institutional alignment. CTPH successfully addressed both expected and emergent challenges in building a cross-institutional infrastructure to include: 1) creation of shared digital platforms for communication, collaboration and project management; 2) a harmonized approach to Institutional Review Board processes through a reliance agreement; and 3) a successful pilot program that included cross-institutional grant proposals and 4) innovative programs designed to foster dialogue and integrate community voices into strategic planning and implementation. One program, CTPH Friday Forum, has emerged as a cornerstone for stakeholder engagement because of its open space for idea exchange and co-creation. Conclusions: Incorporating CBPR principles adapted to interuniversity partnerships, provides a durable framework for strategic alignment, dynamic engagement, and sustained collaboration and dynamic engagement to establish a firm foundation to accelerate translational and precision health across university boundaries.

Strengthening Interinstitutional Collaboration Through CBPR: The BCM-UH Consortium for Translational and Precision Health 

The goal of this presentation is to show how the CTSA Hub can coalesce its existing resources to improve the generation of RWE from RWD. The promise of RWD and RWE is compelling: acceleration of biomedical discovery to clinical application; improved access to individuals and populations that have been difficult to reach in traditional research activities; and reduction in the cost of innovation in precision medicine. RWD is generally defined as data related to health collected from sources outside traditional randomized controlled trials. Although RWD is not primarily collected for research purposes, it can provide valuable insights into individual and population health. RWE is the clinical evidence derived from the analysis of RWD. The value of RWE is ultimately tied to the quality of RWD, while obstacles to RWE exist in terms of result validation, reproducibility, and replicability. Our Hub undertook the challenge by outlining a “science of translation” strategy designed to enhance the quality of our RWD projects. We focused on the use of two distinct approaches that are uniquely capable of improving the quality of RWD at early stages of project development: 1) Domain Analysis Modeling (DAM), an HL7 activity, is a formal, stakeholder-driven blueprint of a clinical or operational domain that captures business processes, actors/roles, storyboards/use cases, information objects, and vocabularies—typically expressed in Unified Modeling Language. DAM serves as a translation layer between messy real-world health data and structured, interoperable standards. By grounding data in a shared semantic framework, it enhances the quality, completeness, and reproducibility of RWD, which in turn strengthens RWE; 2) Learning Health Systems (LHS)–stakeholder driven activities that seamlessly embed continuous quality improvement based on real-world data. LHS research builds on concepts of systems-based participatory research to form a new paradigm for partnered research in which health data quality, reproducibility, and updating is an essential work product. Investigators seeking guidance in translational research involving real world data will be provided consultation with a range of Hub resources covering novel statistical analyses, terminology harmonization, community outreach and partnership, ethical considerations, and ultimate data access for everyone. Two active Hub projects are: 1) using a formal DAM to transform the RWD of school-based physical fitness testing, currently mandated in 60% of 5th, 7th, and 9th graders in U.S. public schools to data of sufficient quality to embed in the electronic health record of children and adolescents; and 2) building a system-wide LHS to address the expected changes in healthcare facility utilization that will accompany new mandates for under-insured people. Our Hub Evaluation unit will use dynamic approaches, such as the Translational Science Benefits Model, to determine the impact of this project.

Strengthening the Foundations of Real-World Evidence (RWE)  A UC Irvine CTSA Hub Resource to Ensure Real World Data (RWD) Quality, Rigor, and Translation

Sustaining Electronic Health Record Networks in Academic Medical Research Centers: Insights from Implementation Science

The expansion of Electronic Health Record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing.  Despite the wealth of networks, their sustainment has not been described systematically. Often, implementation and dissemination plans do not emphasize the longitudinal perspective needed for sustainability research.

Designing for Dissemination and Sustainability (D4DS) refers to principles and methods for enhancing the fit between a health program, policy, or practice and the context in which it is intended to be adopted. The intent is to promote adoption by individual, organization or community to commit, initiate, and sustain use of health intervention and/or practice.  The study team utilized the D4DS framework to describe the findings from the needs assessment conducted for the Evolve to Next-Gen Accrual to Clinical Trials (ENACT) Network and how the Diffusion of Innovations supports a Sustainability Plan (SP) for RWD data networks in an academic research environment.

This research suggests having a Compelling Value Proposition, a Dynamic Dissemination and engagement strategy, and a Sustainable business model are important factors for sustaining Academic Real-World Data (RWD) research networks.

Utilizing Implementation Science principles and the D4DS framework provides a systematic approach to developing a sustainability plan in the complex environment of academic medical research centers to support RWD networks among multiple user segments, all while establishing interest and trust among users.

The need for effective teaming skills for scientists has grown exponentially over the last several decades. TeamMAPPS is an evidence based behavioral skills training program appropriate for students, trainees, young scholars, as well as seasoned PIs. This andragogically based program focuses upon nine specific competencies across the areas of Psychological Safety, Awareness and Information Exchange, and Team Awareness and Development. The program can be delivered completely online, face to face, or in a hybrid format. Each module involves pre and post assessment, case studies, video scenarios, choose your own team adventure, and application planning. Over thirteen CTSA’s have now been leader-trained for implementation at their specific hub, after completing Train the Trainer events. A recent Dissemination and Implementation Study provides pathways for other institutions to implement the program. Plans for further implementation include more train the trainer opportunities, community of practice events, and partnering with other hubs for future development.


TeamMAPPS: Team Methods to Advance Processes and Performance in Science: Overview and Summary of Program and Research

The Georgia Health Landscape is a collection of resources, including data visualizations, designed to build partnerships between biomedical researchers and the communities that contribute to and benefit from their work. Currently, a positive impact on health outcomes through biomedical research is hampered by high failure rates and low patient acceptance. A lack of trust and lack of willing research participation are key challenges produced by a gap between the biomedical research engine and the communities it serves (Austin 2024). The opportunity is to improve the translation of biomedical research to meaningful health impact by leveraging the power of community partnerships (Khanpoor et al., 2024). This poster demonstrates how existing resources of Cooperative Extension, the outreach arm of land-grant institutions, and the Georgia Health Landscape can be aligned with the biomedical research process to address issues (e.g., invisible end user, recruitment, retention, lack of interest and trust) that limit the translation of research into health impacts. 
The Georgia Health Landscape includes Community Voice (i.e., an organized approach to obtaining community health priorities to inform biomedical discovery), Community Education & Communication (i.e., an established channel for two-way communication to inform biomedical research even in the pre-clinical phase), the Georgia Health Landscape Dashboard and Community Connections (i.e., an evidence-based tool to match biomedical researchers with interested communities to improve recruitment and retention), and Continued Connection / Understanding the Community Change Cycle (i.e., a means of keeping the community engaged between projects and communicating findings to increase understanding and acceptance). 
Because Cooperative Extension is present throughout the United States and functions at the community level, the Health Landscape concept provides a scalable solution to bridging the gap between research and community while allowing the biomedical researcher to focus on what they do best. In this way, meaningful and sustainable partnerships can be built to advance research and health outcomes.



The Georgia Health Landscape: Building Meaningful Partnerships through Cooperative Extension

The Iowa Health Data Resource (IHDR) is a cross-disciplinary initiative designed to address persistent challenges faced by Clinical and Translational Science Award (CTSA) hubs in leveraging real-world data for research. These challenges include limited workforce capacity, data literacy gaps, and the need for secure, high-performance computing environments for protected health information (PHI). IHDR introduces a scalable, reproducible model built on three integrated pillars: People, Technology, and Data.
 
The People component centers on a Data Liaison Workforce—faculty and staff from eight colleges—trained through an experiential program to improve data literacy, increase access to data tools and datasets, and mentor researchers. The Technology component features a HIPAA-compliant data enclave with proximate compute access, enabling advanced analytics, machine learning, and artificial intelligence on real-world health data. The Data component includes a suite of transformative, domain-specific datasets curated through iterative engagement with scientists and supported by robust governance protocols.
 
Since its inception, IHDR has supported over hundreds of studies involving researchers across 11 colleges. It has streamlined data access workflows, reduced time-to-delivery for complex datasets, and fostered multidisciplinary collaboration. The IHDR model is portable across CTSA hubs, leveraging existing institutional infrastructure and offering a reproducible framework for accelerating translational science.

While this poster comes from Biomedical Informatics, it has elements of Workforce Development, Research Resources, Community and Stakeholder Engagement, and Biostatistics, Biomedical Informatics and Data Science.

The Iowa Health Data Resource (IHDR): A Scalable Ecosystem for Advancing Translational Science through Data, Workforce, and Infrastructure Innovation

Throughout academic health centers (AHCs) and beyond, considerable attention has been paid to equip clinical research professionals with the knowledge, skills and perspectives to be critical contributors in support of high-performance clinical investigation. Based on a foundational understanding of good clinical practice, responsible scientific conduct and human subjects research, these individuals develop a strong understanding of the components to succeed at all stages of the study lifecycle. Yet, when placed into the context of a team, a lack of clarity in roles and responsibilities was observed as well as the emergence of interpersonal conflicts that led to operational inefficiencies. The Center for Clinical and Translational Science (CCTS), the CTSA Hub based at the University of Alabama at Birmingham, took steps to overcome this workforce challenge by developing a course tailored to the needs of clinical research teams. CREATE (Collaborative Research and Effective Teamwork Essentials) sought to strengthen partnerships between Principal Investigators (PIs) and Clinical Research Staff Professionals (CRPs) to enhance mutual accountability and improve project outcomes. The course was structured to meet weekly for 2-hours, with sessions designed to include relationship building exercises and to reinforce Team Science competencies across five domains: 1) Building Trust, 2) Team Communications, 3) Managing Research, 4) Problem Solving, and 5) Leadership (PMC11626577 (2024), PMC8057415 (2020)). The initial course was piloted with a nascent trial team (<1 year experience working together), including the PI and five CRPs. Team members resonated with course elements, including DISC personality assessments to understand individual-level behaviors, project management tactics and the importance of communication. Initial feedback revealed ongoing confusion on how to interact with others and characteristics of leadership at all levels. Participants appeared to gain insight into the value of their own roles but continued to struggle to understand or appreciate their relationship with others’ roles. The course also revealed stronger demand for content addressing team dynamics, including problem solving and conflict resolution skills. CREATE course leaders are using these evaluation insights in the continuous quality improvement of the course structure. Importantly, this first cohort signaled unique challenges associated with teams that draw on institutional CRP pools, an increasingly common strategy at AHCs, that will inform future revisions in course content.

Training Effective Clinical Research Teams

Background: The Trial Innovation Network (TIN) is an NCATS-funded initiative designed to enhance collaboration and streamline the operational processes of multisite clinical trials. As part of the Clinical and Translational Science Award (CTSA) program, the TIN is focusing on improving efficiency, fostering inclusivity, and broadening access to clinical research opportunities across a diverse range of institutions and populations, including minority-serving institutions (MSIs). 

Methods: The TIN has implemented a centralized, streamlined Expression of Interest (EOI) process that facilitates rapid dissemination of multicenter study opportunities across the CTSA network which addresses barriers to participation in multisite research. The TIN has established an infrastructure that supports bidirectional resource sharing via an online toolbox and monthly newsletter. The toolbox allows any site to contribute best practices, tools, and resources. This foster’s sharing and dissemination across the consortium. In addition, the TIN is also focusing on enhancing training, workforce development, and capacity building. 

Results: From 2017 to 2025, 99 proposals were submitted to the TIN for an EOI, of which 78 received one. The average response time was 15 days, with a 64% response rate from CTSAs. Additionally, the TIN developed a toolbox with 95 open-source resources developed jointly by the TICs, RIC and the CTSA, resulting in 127,000 downloads to date. With training programs like Faster Together, Training Program for Multicenter Clinical Trial Management, Statistical DIDACT, and Community of Practice, the TICs and RIC strive for continued dissemination of best practices and innovative solutions.  

Conclusion: The Trial Innovation Network provides essential resources and support to conduct efficient, collaborative trials. Its comprehensive infrastructure aims to accelerate multisite clinical research in all domains. 


Trial Innovation Network - Connecting with the CTSA and Beyond

Background: The Trial Innovation Network (TIN), a partnership funded by the National Center for Advancing Translational Sciences (NCATS) within the Clinical and Translational Science Award (CTSA) program, addresses critical challenges in multi-site clinical research. By leveraging the extensive national CTSA infrastructure, its network partners, and fostering collaboration, the TIN accelerates the translation of innovative research into therapeutic solutions. 

Methods: Two Trial Innovation Centers (Johns Hopkins University, Vanderbilt University Medical Center) and one Recruitment Innovation Center (Vanderbilt University Medical Center) provide no cost consultation services for multicenter studies, connecting clinical investigators with domain experts, strategic partners, and leveraging the entire CTSA network to optimize trial design and operational efficiency. The TICs and RIC have formed partnerships with more than a dozen CTSAs to enhance and strengthen the TIN consultation experience. 

Results: By working closely with investigators, the TICs and RIC have provided resources and facilitated numerous successful consultations across the TIN. 

Conclusion: The TIN offers a sustainable model for overcoming barriers in multisite clinical trials with innovative solutions, supporting the future optimization of clinical research.


Trial Innovation Network (TIN)-A collaborative, academic approach to optimizing the national clinical research infrastructure

Utility of Central Study Registration as a Starting Point for Clinical Research Support in a University System
Kimberly Brunton RN, MSN; Michael Grogan BS; Pamela Anderson RN, BSN; Sanjay Sethi MD
University at Buffalo, State University of New York, Buffalo NY

Background
In 2014, the University at Buffalo (UB) established a Clinical Research Office (CRO) to better manage and support clinical research, with a focus on clinical trials.  Further, in 2015 UB, as the leader of the Buffalo Translational Consortium, launched the Clinical and Translational Science Institute (CTSI) with the CRO part of the Research Facilitation Core.  

A survey of stakeholders in 2014 revealed several barriers to conducting clinical research at UB. Investigators and staff described a lack of knowledge of available research support and lack of coordination between research support offices resulting in a delay in study initiation. Research leadership identified lack of accurate reporting of clinical trial activity and inadequate scientific review of investigator initiated studies while our affiliate institutions cited incomplete knowledge of ongoing research in their institutions and related resource use.

Objective
To develop and implement a process to accelerate initiation of clinical research in our consortium, improve resource awareness and utilization, ensure uniform and complete study information into UB systems and enhance reporting for our multiple stakeholders.

Methods
In concert with UB’s Research Information Systems, the Central Study Registration System (CSR) was developed to provide a “starting point” for all clinical research at our hub. It is hosted in UB’s cloud environment. In 2017, CSR was mandated for use making it a prerequisite for IRB submission. Studies are entered into CSR and processed by CRO/CTSI personnel. Additionally, a departmental scientific review is initiated through CSR. Once processed, a detailed summary is provided to the research team as to next steps, protocol feedback, requirements, available resources pertaining to the proposal. CSR allows for parallel processing of all pre-study requirements.

Results
Since 2017, we have processed >2900 studies through CSR resulting in an elimination of redundant entry; initiation and completion of scientific review; provision of necessary feedback regarding required study initiation steps to accelerate start-up; communication of available CTSI resources relevant to the study; a platform to communicate changes to policy, process and forms directly to study teams and robust reporting capabilities for research leadership and affiliates.

Conclusions
Through CSR we have our clinical research support efforts more efficient and effective with accurate reporting to leadership and affiliates. Investigators and research staff do not find it onerous and are appreciative of the feedback provided. 


Utility of Central Study Registration as a Starting Point for Clinical Research Support in a University System

With over 1,410 Participating Institutions (including all CTSA hubs) across 49 states, DC and Puerto Rico, SMART IRB has revolutionized single IRB (sIRB) domestic multi-site human participant research by providing a common comprehensive model to eliminate inefficient and redundant processes, promote harmonization, and improve transparency in human research protection program review and oversight of sIRB research. This poster highlights the development and success of SMART IRB in forging the national consensus framework agreement capable of covering institutions and human subjects research of all sizes and types, including numerous federal agencies. This poster also highlights educational initiatives and public events that have been critical to SMART IRB's success and have made it possible for the Version 3.0 agreement to be America's gold standard approach to single IRB.

Version 3.0: America's Gold Standard Approach to Single IRB

With over 1,410 Participating Institutions (including all CTSA hubs) across 49 states, DC and Puerto Rico, SMART IRB has revolutionized single IRB (sIRB) domestic multi-site human participant research by providing a common comprehensive model to eliminate inefficient and redundant processes, promote harmonization, and improve transparency in human research protection program review and oversight of sIRB research. This poster highlights the development and success of SMART IRB in forging the national consensus framework agreement capable of covering institutions and human subjects research of all sizes and types, including multiple federal agencies. This poster also highlights educational initiatives and public events that have been critical to SMART IRB's success in building the single IRB community and have paved the way for the SMART IRB Version 3.0 agreement to serve as America's gold standard approach to single IRB.

As clinical research drives life-changing advancements in healthcare, it is critical to have a team of well-informed, skilled, and dedicated clinical research professionals. Following the COVID-19 pandemic, academic medical centers experienced a noticeable decline in experienced clinical research professionals. WashU leadership had the vision for a U.S. Department of Labor (DOL) Clinical Research Professional (CRP) Apprenticeship, which was developed at the WashU Institute of Clinical and Translational Sciences (ICTS). The CRP Apprenticeship plan outlined a competency-based model that combined structured, on-the-job learning and related instruction in theoretical, ethical, and research skills training. This is a 12 to 18-month program requiring 240 hours of classroom instruction and 2,000 hours of hands-on experience. In 2023, the program was officially registered with the DOL as part of the National Apprenticeship System. It became one of the first university-based collaborations with the DOL offering a CRP Apprenticeship. It served to attract talent from the region and create career pathways for individuals who might not have met traditional qualifications for clinical research roles. The first cohort included four individuals, hired as full-time, benefit eligible employees who were carefully vetted, in coordination with the St. Louis County Workforce Development Office, based on demonstrated aptitude in critical thinking and close reading skills, plus a strong desire to work in clinical research. They rotated through regulatory, recruitment, finance, study management, participant interaction, bioethics, and data management groups. Their sponsoring divisions and supervisors provided infrastructure to support their careers, increase their visibility, and demonstrate institutional commitment to their success. Apprentices completed self-evaluations at six months and end of apprenticeship, rating their confidence in their foundational knowledge of applying the Joint Taskforce Domains and Competencies and hands-on skills. Supervisors evaluated each apprentice’s skills and knowledge at the end of the program. After successful completion of program, apprentices were promoted to a Clinical Research Coordinator I. The program has demonstrated the potential to transform how clinical research professionals are hired, trained and supported. It has helped train and mentor individuals with little to no prior experience to gain the skills, confidence, and professionalism needed to work independently as an entry-level clinical research professional. The program is reducing traditional barriers to entry by valuing aptitude for learning, dedication, and on-the-job training as a successful replacement for the requirement of a formal four-year college education. By creating new pathways into clinical research, WashU is helping shape a more robust and sustainable workforce, one that is ready to support the future of clinical research.

WashU Program Cultivates the Next Generation of Clinical Research Professionals; Department of Labor Registered Clinical Research Professional Apprenticeship

Clinical trial data visualization and conduct of adaptive clinical trials demand extensive coding and time from data analysts and statisticians.  To improve accuracy and efficiency of data analysis, we assembled a team to develop a collection of 14 R Shiny applications to automate and simplify some of the repetitive tasks and to allow investigators to visualize their data and improve data quality. Specifically, we developed 3 Clinical Trial Conduct Applications to streamline the conduct of clinical trials using innovative Bayesian adaptive phase 1 designs, and 11 Data Visualization Applications which provide a selection of novel and commonly used clinical trial plots. All web applications are free and easy to use by investigators and data analysts. Since their development, these applications have been widely used by our team to communicate with investigators and prepare figures for publication.   In 2023 and 2024, we disseminated the suite of web applications at two conferences for practicing statisticians and clinical trialists as well as two workshops, one for junior clinical trials investigators and the other for statisticians working in early phase clinical trials.  These efforts along with the addition of new applications have led to a 93% increase in users from 2023 to 2024, totaling over 1,500 users across the US and internationally.

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2025 Fall CTSA Program Annual Meeting

Advancing Discovery: Bridging the Gap between Clinical Imaging Acquisition, Management, and Research Analysis