Objectives/Goals: Diffusion of innovations (DoI) posits that new health-related ideas spread through communities over time and across stages of adoption. We will apply DoI to understand a paradigm shift toward seeing participants as partners in clinical research, specifically through delivery of plain language summaries of results. Methods/Study Population: The return of results in lay language (plain language) to clinical trial participants represents a paradigm shift in the EU and now the USA. We will conduct a systematic review of the implementation of “lay summaries” or “plain language summaries” in different jurisdictions to understand current regulatory influence. We will then review PLS samples and published studies to determine the rate of adoption by industry and non-pharmaceutical company sponsors. Using the DoI framework, groups will be placed in an adopter category. Finally, we will employ an implementation science approach to understand the diffusion process and the translation to participants, laying the groundwork for a culture of change in medical product development. Results/Anticipated Results: Our search on PubMed using key terms “Diffusion of Innovation” and “Plain Language Summary” did not produce any relevant results in the context of clinical trials in the USA, which illustrates a gap in the literature and application of this theory in this context. In the future analyses, we will examine factors influencing the adoption stage and outcomes, such as regulatory action, what best practices have been defined/implemented (if any), culture shifts in the context of clinical research, health communications, and inclusion of patient voices in clinical research. Our analysis will include a network analysis to evaluate characteristics that influence adoption of PLS in clinical research. We hope to identify who is at the forefront of innovation and why. Discussion/Significance of Impact: Novel application of DoI theory will help lay the groundwork for a culture of change in patient-focused drug development, specifically for the dissemination of results to patients. In future studies, we plan to develop a tailored framework for the inclusion of PLS as part of a paradigm shift in the patient-focused drug development process.

Objectives/Goals: This literature review examines the current landscapes of plain language summaries (PLS), which are used to make research accessible to nonexpert audiences. It aims to identify gaps in their implementation by focusing on challenges related to consistency, accessibility, and quality across fields. Methods/Study Population: A systematic search was conducted using databases such as PubMed and Google Scholar, employing key search terms like “plain language summaries,” “scientific communication,” “health literacy,” “patient education,” “knowledge translation,” “accessibility in research,” “public engagement,” “lay-person,” and “lay summaries.” Literature from multiple sources (pharmaceutical companies/industry, nonprofit organizations, private–public partnerships, and government) was compared to assess the gaps in current PLS best practices. Results/Anticipated Results: Search results yielded 95 articles. Of those, 37 articles fit the criteria, highlighting critical gaps in PLS implementation for clinical research. Preliminary findings suggest a lack of standards and guidelines, as well as a need for more research on the effectiveness of PLS for improving knowledge transfer and patient engagement. Key limitations were identified for investigator-initiated trials (IITs). A best practice table, comparing recommendations from each group of sources, was developed with suggestions for writing effective PLS. While there is consensus on some principles (i.e., importance of simplicity), differences emerge regarding optimal length and the use of layperson glossaries and graphics. The table aims to serve as a guide for creating effective and standardized PLS across fields. Discussion/Significance of Impact: There are limited PLS best practice resources tailored for IITs. These findings could lead to more practical tools and a streamlined approach to enhance communication strategies for lay audiences. This would benefit trial participants and community members who rely on this information and bridge the gap between scientific communities and the public.

Objectives/Goals: How do we care for a patient whose mental health is deteriorated such that their decision-making capacity may be compromised? The high-potency opioid crisis in Ontario demands that we provide effective care for the affected population. We must also avoid patients having a subjective experience of coercion and must protect their human rights and dignity. Methods/Study Population: Ontario’s legislation governing mental health care will be explored: the Ontario Mental Health, Healthcare Consent, and Substitute Decisions Acts. We will identify best practices/learning across locales. Patients who have been involuntarily treated/confined will be welcomed to voice what the law should contain. International strategies for: coercion reduction practices, advanced care directives, less prohibitionist care culture, and supports for social determinants of health (SDH) may also help Ontarians. Patients, family members, law enforcement, judiciary, community agencies, and healthcare professionals will be invited to contribute via focus groups to drafted mental health care legislative improvements. Thus, we ensure law enforces patient-defined quality care and practical workflows. Results/Anticipated Results: We hope to emerge from our focus group consultation with draft legislative and procedural edits for Ontario’s mental healthcare laws to ensure that the laws protect human rights and that the laws reflect patient-defined needs. We must ensure controls are in place to de-risk power imbalances and limit the incidence of potential procedural misuse. We intend to design legislated procedures to ensure that people don’t get inappropriately involuntarily confined/treated. We will incorporate the perspectives and lived experiences of patients who have experienced involuntary treatment and/or involuntary medical confinement (locally in a focus group(s)) and internationally (in literature) to inform this legislative development. Discussion/Significance of Impact: We will learn from engaged stakeholders about how to shape Ontario’s legislation to support quality mental health care. We hope to identify and draft legislation improvements that voice what patients and their families’ value, drafts informed by evidence-based best practices and informed innovation. Via inclusion, we create a policy that serves.

Objectives/Goals: To identify the most impactful regulations regarding the approval and marketing of TMs in the U.S., E.U., Japan, Australia, China, and India enacted between the years 2000 and 2022. To explore TM-related regulations in new countries, Japan and Australia, for their novel regulatory approaches in intersecting Western and Eastern medical practices. Methods/Study Population: International regulatory bodies included in this study were chosen based on the country’s long history with TM and/or the existence of review processes specific to TM. TM-related regulatory changes that were enacted between the years 2000 and 2022 were identified and categorized into special departments, changes in clinical trials, or specialized tracks. The impact of these TM policies was measured via the number of TM-related INDs, approved applications, marketed drugs, and rejected applications per decade since 2000. This data was then organized alongside policies to draw conclusions about the influence of these regulatory changes. All data was collected using official government websites and journals published by independent, external research institutions accessed via USC’s library services. Results/Anticipated Results: Previous research revealed each country made efforts to integrate TMs into existing drug practices, such as clinical trials and safety requirements, although the extent and methods for the integration differed. Countries with a longer history with TMs are predicted to have regulatory systems that are more accommodating to the unconventional nature of TMs, making the approval and marketing of TMs much easier in these countries. As TM-related policies are more refined and increase in number in a given country, the number of TM-related applications and TMs marketed as legitimate, prescribable medications will also increase. Discussion/Significance of Impact: Timelines and charts displaying the impact of new TM-related regulatory changes will help identify a successful model for increasing TM IND submissions, approvals, and marketed TM drugs.

Objectives/Goals: The purpose of this pilot study is to conduct a survey interview to understand the knowledge and attitudes of people experiencing homelessness (PEH) toward clinical trial participation, provide insights for future engagement strategies, and begin bridging the gap between clinical researchers and this marginalized group. Methods/Study Population: A 14-question survey, developed in collaboration with the street medicine (SM) team at the University of Southern California and other key stakeholders in PEH research, was administered in a survey–interview style to PEH in SM’s East Side Los Angeles area of operations. The questions were designed to assess PEH’s knowledge and attitudes toward the significance of clinical research and their potential participation. No identifying information was collected. To ensure diversity in responses, the surveys were conducted in different SM healthcare providers’ areas of operations. The responses were then analyzed both quantitatively and qualitatively, taking into account the broader perspectives that PEH may have toward clinical trial research. Results/Anticipated Results: A total of 9 PEH were surveyed. When asked to share their thoughts on “clinical trials” (CTs), most described the need to travel on-site to a practitioner to be studied/tested. They indicated that they knew what CTs were and that the FDA’s official definition was easy to understand. Only one respondent reported being part of a diagnostic trial, and one declined to answer. Four respondents indicated a willingness to participate in a CT. Four answered “maybe” and one answered “no.” Of note, most believed that CTs have a positive impact, and only one responded that they think researchers do not maximize health benefits and minimize risk. Outside of the allowed answer choices, respondents also shared that there should be payment for participation in a CT, even if they don’t expect any. Discussion/Significance of Impact: The survey results could significantly influence the future of clinical research, guiding it to be better tailored to PEH’s unique circumstances, thereby potentially improving their participation rates. A key objective for the future will be to increase the sample size of the survey to improve the applicability and impact of the results.

Objectives/Goals: The World Trade Center (WTC) Health Program (Program), a limited federal healthcare program for eligible people exposed to the terrorist attacks on September 11, 2001, expanded telemedicine services during the COVID-19 pandemic. We analyzed service use trends from 2020–2021 to describe how the program implemented telemedicine services. Methods/Study Population: We estimated use rates of telemedicine-eligible services and telemedicine services by all included program members and by subgroups of members defined by member type (responder or survivor) and selected characteristics for the study period 2020–2021. We described the use trends of total telemedicine-eligible visits, telemedicine visits, and in-person visits, respectively, by quarter. We calculated the quarterly rates of telemedicine use per 1000 living members. We used a multivariable logistic regression to examine associations between member characteristics and telemedicine use rates. Results/Anticipated Results: About 75% of telemedicine visits were related to mental health services. In the second quarter of 2020 (April–June), telemedicine use rate (367 visits/1000 members) increased, exceeding in-person service rate (152 visits/1000 members) by 1.4 fold. Telemedicine use rate decreased gradually in the rest of the study period but still represented 38% of total visits by the end of 2021. Regression models showed differences in telemedicine use rates by member type and by demographic characteristics. Survivor members (vs. responder members), those self-identified as non-Hispanic Other races (vs. non-Hispanic White), those with preferred non-English language (vs. preferred English), and those not living in the New York metropolitan area (vs. living in the New York metropolitan area) were less likely to use telemedicine. Discussion/Significance of Impact: The expansion of telemedicine service provided members uninterrupted access to necessary health services during the COVID-19 pandemic. It underscored the importance of extensive partner collaboration, the capacity to rapidly develop necessary technical guidance, and the flexibility to timely address frequent regulatory guidance updates.

Objectives/Goals: MyCTSC harmonizes data from many sources into one database for evaluation, marketing, and CTSC member management to address current disparate collection and siloed use of data. It simplifies report creation with real-time dashboards so administrators and leadership can view progress quickly and plan for improvements based on real-time data. Methods/Study Population: MyCTSC is built using open-source software like Python, Django, MariaDB, Bootstrap, and Chart.js, and integrates data from sources such as systems for consult requests, pilot/voucher applications, and REDCap surveys. Each data source is imported into the warehouse either automatically via API, if available, or via manual file upload. De-duplication and other data cleaning are performed as well. Customized, real-time dashboards are developed based on the needs of administrators and leadership. While MyCTSC does not have a study population, it does have the stakeholders mentioned above as well as leadership. The development timeline spans three years: initial development and data warehouse population in Year 1, data cleaning and dashboard creation in Year 2, and full rollout to all CTSC members in Year 3. Results/Anticipated Results: MyCTSC aims to create a seamless data and member management system to resolve issues stemming from multiple data sources. Demographic challenges are addressed by implementing data cleaning and consolidating duplicate identities into a single profile. The initiative will enhance stakeholder buy-in by presenting evaluation use cases that show the impacts of CTSC resources. For example, workforce development needs will be met through surveys and integration of a course catalog. MyCTSC will also facilitate targeted resource and event advertising, and support investigator outreach and collaboration by utilizing dashboards and reports. Furthermore, it will serve as the consolidated data source for all CTSC modules, promoting greater interaction and collaboration across administration and modules. Discussion/Significance of Impact: MyCTSC integrates multiple sources, consolidates identities, and simplifies reporting for outreach and collaboration. It enhances interaction with researchers and community members, advancing translational science by linking projects and publications. MyCTSC, built with open-source software, can be made available to other CTSA hubs.

Objectives/Goals: Our goal is to develop a CRM platform to streamline support for investigators, optimize resource use, and enhance service delivery within a CTSA institution. This platform will measure outcomes, improve transparency, and ensure compliance, creating an efficient, supportive research environment. Methods/Study Population: Beginning with the design concepts and collecting technical requirements, ensuring alignment with the needs of investigators, administrators, and service units. Apply project management methodologies to iteratively design, prototype, and test features. Focus on user-centric interfaces and seamless integration with institutional systems. Incorporate stringent data security protocols in compliance with regulatory standards. Conduct pilot studies to refine functionalities and gather constructive feedback. Facilitate extensive user training programs and offer continuous support. Regularly monitor performance metrics, iterating based on empirical data and user feedback to ensure continuous enhancement and adherence to institutional objectives. Results/Anticipated Results: YCCI successfully designed, developed, and implemented a CRM platform for managing the intake, navigation, consultation, service delivery, and outcomes tracking of the clinical and translational science activities that are supported by and beyond the CTSA-supported infrastructure. The CRM platform has been live in production since roughly July 1, 2024, and YCCI is excited to share data, results, and outcomes with the ACTS community. Discussion/Significance of Impact: YCCI’s CRM platform advances the CTS mission by streamlining PI support, enhancing resource utilization, and fostering inter-team collaboration within the research ops and administration teams. Measuring outcomes and service efficiency enables data-driven decisions, ensuring continuous improvement, and impactful translational science.

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.