Objectives/Goals: To explore how generative AI and chatbot technologies can transform clinical research administration by improving operational efficiency, reducing administrative burden, and thereby enhancing overall productivity and accuracy in clinical research environments. Methods/Study Population: This explores AI’s application in enhancing clinical research administration. We specifically address AI’s role in QCT/MCA activities, charge master data cleaning, and generating IRB consent forms from award documents. AI algorithms optimize charge master data for accuracy and compliance. Generative AI models are employed to produce IRB consent forms efficiently, incorporating key grant documents. AI also conducts thematic analyses of historical CTSA aims to identify trends and recurring themes. Furthermore, AI-assisted tools enhance study design through innovative approaches to hypothesis generation, sample size calculation, and protocol development. Integrating these AI methods aims to significantly improve efficiency, accuracy, and overall quality in clinical research administration. Results/Anticipated Results: Incorporating AI into clinical research administration will yield improvements in efficiency and accuracy. AI-driven QCT/MCA steps are expected to reduce human error and enhance data integrity. Chargemaster data cleaning via AI prompts will likely result in optimized, error-free data, ensuring compliance with regulations. The use of genAI for creating IRB consent forms from grant documents should significantly streamline the IRB approval process, reducing preparation time and administrative burdens. Thematic analysis of CTSA aims by AI will provide deep insights into historical trends and recurring themes, aiding in strategic planning. AI-assisted study design tools are anticipated to optimize sample estimation, protocol development, and advance the quality of clinical research administration. Discussion/Significance of Impact: The significance lies in enhancing efficiency, accuracy, and quality in clinical research administration. By streamlining processes, reducing errors, and providing strategic insights, AI supports the CTSA mission to accelerate translational research, thus improving public health outcomes and scientific innovation.

Objectives/Goals: Digital recruitment can improve participant engagement in medical research, but its potential to introduce demographic and socioeconomic biases is unclear. This study investigates pathways participants took during a digital recruitment workflow in neurology, examining potential associations with socioeconomic and demographic factors. Methods/Study Population: As part of an ongoing study aiming to remotely capture speech from patients with neurologic disease, most participants seen in neurology on our campus are invited to complete a self-administered speech examination. We exported participant data from Epic (semi-automated identification and invitation), Qualtrics (eligibility screening), the participant tracking database (consent), and the recording platform (completion) for March to July 2024. Data visualization was performed using a Sankey diagram. Socioeconomic status was assessed using the housing-based socioeconomic status (HOUSES) index and area deprivation index (ADI) national rank. Kruskal–Wallis and Wilcoxon rank-sum tests were used to compare the median age, socioeconomic indices, and time taken to reach different steps of the study. Results/Anticipated Results: Of the 5846 invited participants, 57% were from urban areas, 23% from rural areas, and 20% from urban clusters. Most did not read/respond (2739) or declined (1749) the initial invitation via Epic. Of the 1358 interested participants, 415 completed the study. Participants from urban areas completed enrollment steps faster than those from rural areas and urban clusters, though the variance was large (42.6 ± 41.4 days vs. 50.6 ± 42.2 days and 50 ± 43.9 days, respectively; p  =  0.030). Female participants took longer to complete enrollment than males (48.7 ± 44 days vs. 40.5 ± 38.8 days; p  =  0.026). Participants who successfully finished the study had significantly lower ADI national ranks compared to other common pathways (40.6 ± 19; p  =  0.0021). No associations were found with the HOUSES indices. Discussion/Significance of Impact: Our findings support differences in participant engagement, with urban participants and males more likely to complete enrollment steps. Those who finished the study were less disadvantaged suggesting potential bias in digital recruitment. These findings can inform strategies to improve digital recruitment in neurology research.

Objectives/Goals: Conduct an evaluation of the Clinical Research Support Center (CRSC) model using a structured methodology, leverage insights to drive continuous improvement and evolution, and broadly disseminate outcomes to promote knowledge sharing and best practices for similar translational science initiatives. Methods/Study Population: We will utilize a structured case study approach, including adapting a translational science case study evaluation approach to assess impact as well as support practices, barriers, and facilitators that influence research translation. We will collect data from diverse sources. Primary data will come from structured interviews with stakeholders and a survey of a random sample of faculty and research staff. Secondary data includes grant applications, reports, and publications; public stories/media related to research supported by CRSC; scientific publications; and organizational documents. Results/Anticipated Results: The case study will identify the CRSC model’s impact on the research enterprise. Findings will articulate the specific strategies and practices the CRSC implemented to support clinical research; key factors, people, and resources that helped develop, improve, and promote CRSC services; significant milestones in evolution of the CRSC; and specific ways in which support services impact clinical research infrastructure and outcomes. The findings will highlight both strengths and areas for improvement. Early results show historical challenges with operational silos and resource limitations. Findings suggest CRSC facilitators include a team science approach with institutional support. Discussion/Significance of Impact: This case study will provide insights related to benefits, challenges, and facilitators of a translational science support model. Insights will guide the CRSC’s evolution and be broadly disseminated to promote knowledge sharing and best practices for future translational science applications.

Objectives/Goals: This continuous quality improvement project focuses on the efficiency and effectiveness of social media campaigns for clinical trials and other health research. We analyzed data from 160 studies that recruited via social media campaigns on Meta and used the results to make improvements to MICHR’s Participant Recruitment social media campaigns. Methods/Study Population: Data on 440 ad buys purchased for Meta advertising campaigns designed for 160 studies were collected from U-M’s Research Data Warehouse, Meta, and other administrative sources. MICHR’s participant recruitment team systematically reviewed these data and rated the relevance and effectiveness of the recruitment strategies for each study. Stepwise linear regressions were used to test predictors of the efficiency and effectiveness of social media campaigns on Meta, as measured by the 1) total reach, 2) total clicks, 3) the “click-through rate,” and 4) cost per “click” of the campaign over time. Differences between the impact of campaigns for clinical trials and other clinical and translational research were found. Improvement initiatives informed by these results are underway and their impact is being evaluated. Results/Anticipated Results: 64 clinical trials and 94 non-trials were identified, with an average of $1,635 spent on social media campaigns for trials and $950 spent on non-trials. Across all social media campaigns, an average of 121,500 people were reached at a total cost of $1,220 per campaign, returning 4,288 clicks (4% click through rate) at $0.38 per click. The campaigns for trials reached more people than non-trials (152,998 vs. 101,261) and they attracted a larger number of clicks (6090 vs. 3106). The resulting average click-through rate was higher for clinical trials (4.9% vs. 3.7%), and the cost per click was lower ($0.35 vs. $0.39). Campaigns for clinical trials cost significantly more (Mean  =  $1,635, SD  =  $1,473, p  =  .020) but returned more clicks (Mean  =  6,090, SD  =  5,105, p  =  .007), and higher click-through rates (Mean  =  4.9%, SD  =  2.2%, p  =  .017). Discussion/Significance of Impact: There is great variation in the efficiency and effectiveness of social media advertising campaigns for recruitment into clinical trials and other clinical and translational studies. While the size and cost of these campaigns were found to be higher for clinical trials than for non-trials, the effectiveness of trials’ campaigns can also be greater.

Objectives/Goals: Clinical and Translational Science Award hubs will be the primary investors in advancing translational science until academic reward structures and funding agencies incentivize these efforts. As such, hubs will benefit from systematic methods to strategically identify and efficiently solve challenges in the translational process. Methods/Study Population: Translational science (TS) problems are abundant, complex and typically reside within systems of interconnected processes and people. These characteristics are informing efforts at the Michigan Institute for Clinical & Health Research (MICHR) to create a framework that guides how we select the right translational science problems to invest in solving and how we solve these problems in user-centric, efficient, and effective ways. Our framework leverages methods from the fields of design and systems thinking. Design thinking is a human-centered approach to problem solving and innovation that is ideal for tackling ill-defined and complex problems. Systems thinking methods help us situate and analyze problems within broader dynamics, structures, and perspectives, ultimately informing key levers for change. Results/Anticipated Results: Applying design and systems methods, we created an eight-step TS framework that centers on the diverse perspectives of those experiencing a TS problem and those implementing solutions. The first four steps guide in defining the TS problem within its context, understanding why previous solutions have not worked, and determining the value and generalizable knowledge that a solution would create. The last four steps are solutions-focused, with iterative brainstorming, testing, and refining of potential solutions before they are implemented locally and disseminated widely. Each step is underpinned by guiding questions, methods, outputs, and metrics to ensure a scientifically rigorous approach to defining, prioritizing, and solving TS problems. We are currently prototyping the framework with various case studies. Discussion/Significance of Impact: A framework that guides strategic investment in TS should ensure resources are allocated to the most well-defined and pressing problems that are important to the community and should speed up the process of creating solutions. Engaging myriad viewpoints leads to more viable solutions that foster a commitment to real change within the research ecosystem.

Objectives/Goals: Lack of comparative data limits research operations quality improvement (QI). The Northwest Participant and Clinical Interactions (NW PCI) Network, a group of 17 unaffiliated university and health system-based research centers, built an operations dashboard to track efficiency and enable multisite QI projects. Methods/Study Population: A Data Governance Working Group was assembled to establish shared data governance, draft nondisclosure (NDA), and data transfer and use agreements (DTUA) suitable across organizations and standardize research operations metric definitions. Sites in the NW PCI Network were recruited to participate in a pilot program to assess data sharing and governance infrastructure, data collection, upload procedures, and data visualization tools. The NW PCI Coordinating Center developed an analytical data dashboard of research operations metrics and conducted semi-structured interviews with participating sites to understand barriers and facilitators of program success. Results/Anticipated Results: Four sites (2 health systems, 2 universities) were recruited for the pilot and reviewed and executed NDAs and DTUAs. Three of the sites have submitted data for a total of 1,405 studies. Of the 24 requested data operations metrics (e.g., study startup, recruitment, implementation, and basic study information), 71% of the metrics were submitted by all three sites (n = 17), 25% were submitted by at least one site (n = 6), and 4% were not submitted by any site (n = 1). Interviews with sites after data submission found areas for improvements (clarification of data definitions, efficiency of data upload process) and positive effects for sites (e.g., process improved insight into own data operations). Discussion/Significance of Impact: Unaffiliated research centers created data governance procedures to enable sharing of operations data. Pilot sites successfully loaded most but not all operations data to the dashboard. Interviews identified process limitations and opportunities for improvement to inform expansion to all NW PCI sites.

Objectives/Goals: Translational researchers often struggle to navigate a complex constellation of institutional resources spanning the IRB to bioinformatics units. We had two aims 1) Systematically map all institution-wide research support units and 2) leverage this database within a generative AI virtual concierge tailored to local investigator queries and needs. Methods/Study Population: This study leveraged mixed methods approach. First, we conducted needs assessments of local study teams to identify barriers to translation, revealing that research resources are often unknown to study teams. Second, we identified all investigators, institutional units, and offices offering such resources that we call research support units (RSUs). RSUs were surveyed, collecting contact information (leadership, website, physical location), services provided, type of research supported, and performance metrics. Third, the resource database was integrated into a large language model (LLM, e.g., ChatGPT4o) using a retrieval augmented generation (RAG) system within an R Shiny application called virtual concierge. Queries and responses are recorded for quality improvement. Results/Anticipated Results: Needs assessment focus groups consisted of clinical and basic science investigators, study team members (e.g., clinical research assistants), core directors, and administrators (n = 26). Six sessions were conducted in Spring 2024. A major resultant theme was difficulty finding RSUs “by trial and error” and lacking a “clear defined pathway” for accessing RSUs. This prompted a survey-based environmental scan to identify institutional research resources. There were 122 diverse RSUs ranging from the IRB, to grant writing, to single cell sequencing. Each research unit offered a median of 6 service types, totaling 410 service types overall. The resultant Virtual Concierge meaningfully responds to investigator resource queries with appropriate contact and access information. Usability testing is underway. Discussion/Significance of Impact: Linking researchers with translational resources requires mutual understanding, timely communication, and coordination across teams. We systematically filled these information gaps between investigators and institutional resources. Our Virtual Concierge AI bot can help researchers navigate resources through the translational process.

Objectives/Goals: Mayo Clinic Florida’s Clinical Research Units develop over 200 clinical studies on average annually. Almost 30% of these projects are developed and then are unable to activate due to a variety of operational factors. To increase the success rate, a scoring tool was created to assess the risk associated with the development of these research projects. Methods/Study Population: A project team comprised of members of research administration and physician leadership developed a rapid project management (RPM) scoring tool to assess operational risk factors. The scoring algorithm was embedded into an existing REDCap database, using a combination of identified variables and calculated fields. All noncancer industry sponsor-initiated clinical studies were scored at intake. According to the following categories: enrollment timelines, study team capacity, and previous experience with the Sponsor. Studies with a score greater than the established threshold were referred to physician leadership for transparent discussions with the principal investigator regarding the identified study development-related risks. Results/Anticipated Results: The RPM tool has assessed close to 200 projects since implementation in June 2022. An interim analysis is being conducted of all projects assessed by the RPM tool dating from implementation to May 2024 to compare the outcomes of these studies with the given RPM score. We anticipate based on anecdotal evidence gathered during the course of this pilot project that the RPM tool will show a correlation between risks identified and study outcomes as defined as successful activation of trials, or rationale of project development failures. We anticipate a reduction in the amount of time elapsed and effort expended developing projects with scores reflecting identified project development-related risk factors. Discussion/Significance of Impact: The RPM tool provides an opportunity to allocate resources to studies with the greatest potential for successful activation. In the future, the RPM tool may be used to identify risk factors associated with enrollment and accrual of participants.

Objectives/Goals: Mayo Clinic’s Clinical Trials Beyond Walls™ (CTBW) program collaborates with study teams to implement decentralized elements in clinical trials, enabling participation from home or local settings. In cancer treatment trials, traditional paper pill diaries are replaced with real-time digital tracking solutions to monitor chemotherapy adherence. Methods/Study Population: The CTBW team developed a solution to deliver electronic pill diaries to research participants using the electronic health record (EHR) system Epic and patient portal MyChart1. The solution includes a portal message to remind participants to take chemotherapy. Medication dose, date taken, and reasons for missed doses (e.g., “I forgot” or “side effects were bothersome”) are captured. An automated in-basket notification system alerts the study team when predefined conditions are met. Configurable medication schedules ensure diaries are sent according to the prescribed frequency. Reports were generated to allow study teams to monitor all participant diaries. Results/Anticipated Results: The CTBW team implemented this digital pill diary in neuro-oncology trial NCT066250472. We anticipate the pill diary in the patient portal will enhance chemotherapy adherence by capturing real-time data in a platform widely used by Mayo Clinic patients. This patient-reported data is stored in the EHR, where it is accessible to providers and study teams. This allows for continuous monitoring, which facilitates a streamlined review of potential adverse events, improved compliance visibility, and timely treatment adjustments compared to paper-based or external solutions. The system also streamlines data entry, reducing human error and eliminating manual transcription. The created language and workflow templates allow the CTBW to scale this approach to future cancer trials Discussion/Significance of Impact: Decentralized clinical trial participants may never visit Mayo Clinic, making digital recording essential. The EHR-based digital pill diary enables continuous monitoring within a familiar system for providers and patients, increasing study team visibility, and allowing for earlier intervention in cases of non-compliance or adverse events.

Objectives/Goals: The operation of a clinical trials unit involves multifaceted tasks and stakeholders. A competent information system is critical to daily operations while ensuring smooth conduct of clinical research. We share 15 years of experience in the design and implementation of such a system at Mayo Clinic to inform other institutions with similar interests. Methods/Study Population: The Informatics team collaborated closely with nurse leaders and elicited input from additional stakeholders including nurse unit coordinators, lab managers, schedulers, investigators, study coordinators, and regulatory specialists throughout the phases of system design, development and continuous enhancements, and expansion. The stakeholders offered insights on the corresponding requirements throughout the study life cycle, from engaging with the study sponsor, operational review for protocol execution, development of study budgets, human subject protection and risk mitigation, data management and integration, to outcome monitoring, and regulatory reporting. The activities were then translated into functional components and implemented as a seamless and effective solution. Results/Anticipated Results: Patient safety, scientific rigor, operation automation, efficiency, and regulatory requirements were all considered in developing an integrated system, or the clinical research trials unit (CRTU) Tools. Our institution has leveraged the system for essential tasks from the study start-up, visit scheduling and execution, specimen collection and tracking, to individual protocol metrics and billing. We adopted a measure-as-we-go methodology so that data such as visit census, resource usage, and protocol deviation are tracked and collected during routine use of the system. Specifically, an issues/concerns/exceptions (ICE) tool is used for quality control and patient safety. Moreover, data quality greatly benefits from a task dictionary, standardizing the study activities that can be ordered and executed. Discussion/Significance of Impact: The implementation of a well-rounded clinical trials unit information system not only improves the operation efficiency and team productivity but also ensures scientific rigor and contributes to patient safety. We believe the experience can be informative to other institutions. More details will be shared in the poster.

Objectives/Goals: Create opportunities for early-stage research apprentices to obtain real-world knowledge expand float pool to meet unmet research staffing needs, and decrease investigator burden Increase operational efficiencies, decrease start-up time, establish metrics, and ensure transparency responsible fiscal stewardship to approach cost neutrality Methods/Study Population: The Research Coordinator Support Service (RCSS) is a pool of research staff available for hire on an hourly basis by Johns Hopkins University (JHU) investigators. RCSS consists of Apprentices we train on the job as well as Coordinators and Senior staff who have completed the apprenticeship program. Started in 2012, RCSS was placed under new management in November 2020. An expansion proposal was submitted to senior leadership for additional financial and human resources. After approval new systems were implemented and additional hires were made. Several efficiencies were introduced in start-up, study assignment, transparency, invoicing, and overall operations to address the waitlist of 25 studies. Senior leadership now required extensive metric reporting to evaluate program success. Results/Anticipated Results: To address the waitlist, current staff was redirected from purely educational to study-related activities and several new hires were made. The waitlist reduced steadily over time and more research occurred. Average hours of research support per month more than doubled from under 500 to over 1,000. When our Administrator left, we implemented an automated hours-based reporting and invoicing tool resulting in substantial cost-savings over rehiring the position. Apprentices, now with rapid onboarding and early study assignments are reporting high satisfaction and many have been promoted to Coordinator positions. Detailed spreadsheets with relevant metrics were created which are accessible, and regularly reported, to senior leadership for decisions on promotions and additional hires. Discussion/Significance of Impact: Budget belt-tightening requires organizations to reduce expenses while continuing to provide the essential services investigators need. This focus has caused RCSS to examine our program and add efficiencies. We hope others looking to build or expand their float pools will benefit from our experiences and the specific efficiencies we implemented.

Objectives/Goals: To identify clinical trial teams that are at risk of not meeting their recruitment goals as early in the recruitment period as possible, this project aims to provide timely accrual information and projected forecasts for accruals by the end of the recruitment period across all trials at USC. Methods/Study Population: This project aggregates recruitment accrual data periodically from OnCore to create per-study accrual pages that contain an up-to-date accrual chart, metrics like expected and actual accrual per month, and projected recruitment based on an X-month moving average (3 months by default). Trials at risk are identified as early as possible by using these projections to classify risk. In this initial phase, we’ve classified trials as medium risk (80%–99% accrual) or high risk (less than 80% accrual). The dashboard is currently available for all clinical trials at USC and users are automatically restricted to the studies that they administer or work on depending on their role. Results/Anticipated Results: The dashboard will provide visibility across the institution for the current accrual for all clinical trials in a standard, user-friendly format and use the same metrics and definitions of risk for trial accruals not meeting their targets. This will allow the institution to identify trials that need intervention to get back on track using a single set of criteria across all research teams. Users in different roles, whether department heads, principal investigators, or study coordinators can view the current accrual for all the trials that they administer or work on in one central location. The dashboard will also help to identify quality issues in OnCore by performing data quality checks nightly. Discussion/Significance of Impact: By providing a central location for role-based access to timely clinical trial accrual for the institution, the dashboard helps to identify trials at risk of not meeting their recruitment targets as early as possible to provide corrective advice/measures.

Objectives/Goals: Collaborations between Academic Medical Centers (AMCs) and Historically Black Colleges & Universities (HBCUs) are critical to addressing health disparities and building research capacity. Herein, we examine the Duke-NCCU Collaborative Translational Research pilot funding program [2018–2023] to identify opportunities, challenges, and lessons learned from querying key stakeholders. Methods/Study Population: The Duke-NCCU collaborative pilot funding program was launched to support new inter-institutional collaborations that aim to accelerate research discoveries into testing in clinical or population settings. Eight one-year, $50,000 collaborative grants were awarded. Each funded team was assigned a CTSI Project Leader (PL) for project management support. To evaluate the program, we developed surveys targeting principal investigators (PI) and PLs. Questions covered collaboration motivation, goals, outcomes, operational processes, project management support, institutional differences, and challenges. Qualitative analysis will be employed to evaluate the responses and identify common themes. Results/Anticipated Results: The PI survey examines aspects of inter-institutional collaborations, focusing on common themes, such as authorship, definition of success, and institutional culture. The PL survey prompts feedback on managing inter-institutional teams, expectations, and challenges. Select questions were shared between both surveys to capture both perspectives. Surveys were reviewed by members of the Duke CTSI evaluation and team science teams. The PI survey will be disseminated to 16 investigators, while the PL survey will reach 5 project leaders. Built on Qualtrics, each survey takes 20–30 minutes to complete. To encourage participation, incentives will be offered as two $100 gift card drawings. Respondents can choose to complete the survey on Qualtrics or through a recorded and transcribed Zoom session. Discussion/Significance of Impact: AMC-HBCU inter-institutional collaborations drive innovation, workforce development, and equitable dissemination of outcomes. This study exemplifies collaboration, offering insights into translational research collaborations critical to advance equitable healthcare and improving population health.

Objectives/Goals: In January 2023, Mayo Clinic set a goal to have 10% of studies open for six months or more without accrual. At that time, Mayo Clinic Florida had 19% non-accruing studies and 18% non-accruing clinical trials. Research administration implemented strategies to improve accrual outcomes. Methods/Study Population: Two strategies were developed to address non-accruing trials: a clean-up approach and a proactive approach. The clean-up approach involves escalating studies that haven’t been enrolled in over 6 months, identifying barriers, and escalating communication with the principal investigator (PI) and research administration alongside a physician partner. The proactive approach targets studies at the 3-month mark to address issues before reaching 6 months without accrual. Both strategies aim to reduce the cost and effort of non-accruing studies by either creating an enrollment plan or closing the study. Results/Anticipated Results: Since implementation, Mayo Clinic Florida’s non-accruing study portfolio decreased by 10%, and its clinical trials non-accruing portfolio decreased by 7% as of October 2024. Research Administration tracks key metrics (reasons for no enrollment, justifications, and actions) to identify trends and mitigate future accrual risks. A REDCap electronic data capture tool hosted at Mayo Clinic (supported by CCaTS grant UL1TR002377)1 notifies principal investigators when their studies are non-accruing. Future plans include establishing an API with Mayo Clinic’s portfolio management system to streamline the process while maintaining awareness and collaboration. Discussion/Significance of Impact: Through increased monitoring, enhanced communication, and deeper collaboration, Mayo Clinic Florida effectively reduced non-accruing studies in its research portfolio. This approach minimizes effort and costs associated with under-enrolled studies while tracking key metrics to inform future study development.

Objectives/Goals: Historically, the Univ. of Missouri (MU) Sch. of Medicine (SOM) is known for its strong clinical and education programs. In 2020, MU recruited an Executive Vice Chancellor (EVC) for Health Affairs and subsequently Dean of the SOM who initiated programmatic steps to develop and establish a centralized clinical and translational research (CTR) infrastructure. Methods/Study Population: In order to develop and establish a CTR infrastructure, the EVC/Dean of the SOM created and recruited to the combined position of the Associate Dean (AD) for CTR and Associate Director (ADR) of clinical research (CR) for the Ellis Fischel Cancer Ctr. (EFCC) in 2021. The AD CTR was appointed the Chair of the Clinical and Translational Science Unit (CTSU) Steering Committee with the charge of establishing a 10,000 sq. ft. CTSU to be housed in the newly built $225M Roy Blunt NextGen Precision Health Bldg. and for re-building the Clinical Trials Support Office (CTSO), and the Clinical Trials Office (CTO) at the EFCC in his other role as the ADR CR. The AD CTR was also charged with implementing the OnCore clinical trial management system (CTMS) to centrally track and fiscally manage SOM clinical trials. Results/Anticipated Results: Between 2021 and 2023, a CTR infrastructure was developed and established at the MU SOM. A total of 25 new clinical research staff (CRS) and leadership were hired that included a Sr. Dir. of CR Operations, clinical research nurses (CRNs) and coordinators (CRCs), Regulatory/Data/Fiscal/Project/Compliance/Coverage Analysis Coordinators between the CTSU/CTSO and the CTO of the EFCC. The CTSU was built with 10 FTE CRS [CRNs  =  5, CRCs  =  2, administrative staff  =  2, Sr. Lab. Tech.  =  1, and a manager]; the CTSO was re-built with 9 FTE CRS [Fiscal (n = 3), Project (n = 2), Compliance (n = 2), Coverage Analysis (n = 1) and Recruitment (n = 1) coordinators]. The EFCC CTO was re-built with 8 FTE CRS [CRNs  =  4, Fiscal (n = 1), Data (n = 1) & Regulatory (n = 2) coordinators]. The implementation of the OnCore CTMS tracking function was also completed. Discussion/Significance of Impact: Overall, the development and establishment of the CTR infrastructure has led to a significant increase and enhancement (e.g., capacity) to conduct clinical trials at the MU SOM. For example, this has led to a significant increase in the average annual enrollment to interventional oncology clinical trials [n = 82 (2021–2023) vs. n = 42 (2016–2020), p = 0.004].

Objectives/Goals: Our goal for this project is to develop a metric that integrates the intersectional social and structural determinants of health and well-being into the existing policy development framework to impact the integration of such considerations on population mental health. Methods/Study Population: This project was developed from a case study module offered by the Translational Research Program at the University of Toronto. This course was designed to sharpen contextual inquiry skills and further develop a case through employing strategies, including outreach engagement with stakeholders, conducting informational interviews and formulating potential pathways forward based on the integration of insights from interdisciplinary perspectives. Results/Anticipated Results: The anticipated outcome would be improved mental health outcomes as measured by the Mental Health Commission of Canada’s Mental Health Indicators (Mental Health Commission of Canada, 2015) Discussion/Significance of Impact: Although there are established mental health indicators and policy development framework the two operate independently of each other. Our proposal bridge the gap between the sectors so that one may inform the other, and they can collectively formulate reflective and representative policies.

Objectives/goals: This project will focus on identifying the barriers that result in low adherence to quality care indicators that establish effective and efficient pediatric emergency care. The overall objective is to understand motivations behind adherence (or lack thereof) and find solutions to facilitate compliance. Methods/Study Population: This study will use a mixed-methods design to investigate the barriers. Quantitative data will be collected through a survey provided to healthcare providers involved in pediatric emergency care, including physicians, nurses, and administrative staff in both pediatric and general hospitals. Qualitative data will be collected through semi-structured interviews with a group of respondents to gain insight on their experience regarding compliance. Quantitative data will be analyzed using statistical analyses while qualitative data will undergo a thorough thematic analysis. Both sets of data will be reviewed to identify themes and differences in barriers across hospital types and healthcare roles. Results/Anticipated Results: We will have gathered insights and perspectives from key stakeholders that are relevant to our study to ensure a comprehensive understanding of any potential implications that may arise from our study. We anticipate that the specific results will highlight key differences in adherence between pediatric and general hospitals. The study is expected to identify specific barriers hindering compliance with established guidelines in both settings. The results may be used to increase adherence to critical quality indicators and improve patient care. Discussion/Significance of Impact: Pediatric injury care prioritizes the immediacy of care for children with acute illness and injury. With certain hospital protocols not being adhered to, there is a risk of wasting crucial time and resources that can affect patient care outcomes. The results would provide recommendations to improve and increase efficiency in pediatric injury care.

Objectives/Goals: To identify gaps in policy that influence enrollment trends of patients with multimorbidity in Phase III clinical trials. We aim to propose policy recommendations for increasing use of real-world evidence (RWE) that increases safety and efficacy information for patients with multimorbidities. Methods/Study Population: Conduct a systematic policy analysis on the current regulatory landscape of RWE, referencing the 2020 FDA Guidance “Enhancing the Diversity of Clinical Trial Populations.” Evaluate guidelines using the Department of Health and Human Services’ (HHS) 2016 Regulatory Impact Assessment (RIA) Framework. Utilize the Center for Drug Evaluation and Research’s New Molecular Entity Database to identify novel hypertensive drugs approved after 2006, and assess clinical studies’ alignment with the 2020 Guidance. Review additional policies, FDA guidelines, and ICH documents to establish baseline compliance. Two case studies will evaluate past policy impacts on drug development. Assess costs and benefits of increasing multimorbid patient enrollment to inform a policy framework. Results/Anticipated Results: Anticipated results include all components of the HHS’s RIA and a policy framework informed by the assessment. To identify problems, an analysis of clinical trial exclusion criteria in novel hypertensive drugs will be conducted to show diversity and enrollment gaps in regulatory policy, referencing the FDA’s 2020 Guidance. The RIA’s cost–benefit analysis will highlight costs faced for utilizing RWE and expanding enrollment criteria in Phase III studies. The cost–benefit analysis, RIA, and case studies will inform a policy framework that explains dynamics between stakeholders and outline policies that increase clinical trial representation in ways that are less burdensome to sponsors and patients. Discussion/Significance of Impact: By understanding the barriers to enrolling participants with multimorbid conditions, we can outline incentives to increase diverse trial populations, helping healthcare providers choose more treatments for complex conditions. This research supports policy recommendations to make drugs more representative of conditions the population faces.

Objectives/Goals: To present findings from a focus group study that evaluate clinical research professionals’ (CRPs) team science learning preferences. The study aims to better understand CRPs’ experiential perceptions of team science skills, training gaps, team cohesion, conflict, and contributions for their preferred team science training. Methods/Study Population: This study targeted CRPs across various roles in Academic Health Centers via focus groups. The focus groups will assess current skills, identify training gaps, and share experiences on team cohesion, team conflict, team contribution, and their thoughts and perceptions about clinical research professional team science training. The focus groups will be held via Zoom in the Autumn of 2024 with volunteer participants from an initial survey that was conducted earlier in 2024. We will report on combined data from multiple 90-minute focus groups, with approximately 6 participants per session. Results/Anticipated Results: The focus group facilitator’s guide includes questions informed by the CRP team science learning needs assessment results and other questions on team issues that would benefit from focused training. Focus group methods and demographic characteristics of the participants by role and experience level will also be presented. Qualitative analyses of recorded focus-group discussions will present key themes by demographic groups, and as a whole, these data will contribute to the development of CRP team science educational programs and toolkits. Discussion/Significance of Impact: CRPs are vital members of clinical translational science teams. Overlooking CRP team science training can negatively affect the efficiency and effectiveness of the clinical translational science enterprise. CRP team science skills will foster a more collaborative and productive research environment.

Objectives/Goals: This research aims to harness electronic health records (EHR) combined with machine learning (ML) to predict necrotizing enterocolitis (NEC) in preterm infants using data up to their first 14 days of life. We aim to provide interpretable results for clinical decisions that can reduce infant mortality rates and complications from NEC. Methods/Study Population: Through a retrospective cohort study using data from the University of Florida Integrated Data Repository and One Florida, we will develop machine learning models suitable for sequential data to predict NEC. Our inclusion criteria include very low birth weight (VLBW; < 1500g) infants born < 32 weeks gestation and EHR data availability from the first 14 days of life. We will include infants with NEC and infants without NEC to train our ML model. Exclusion criteria include infants diagnosed with spontaneous intestinal perforation and severe congenital anomalies/defects requiring surgery. Results/Anticipated Results: We anticipate that our model will provide an accurate and dynamic prediction for the risk of NEC in neonates using data up to the first 14 days of life. Our model will be interpretable to identify key risk factors and can integrate real-world clinical insights to increase early detection and improve patient outcomes. Discussion/Significance of Impact: The development of a model to predict NEC could be used in neonatal intensive care guidelines and protocols and could ultimately decrease mortality, reduce complications, improve the overall quality of care, and lower healthcare costs associated with NEC.