At the beginning of this year CDISC began the 360i initiative with the goal of digitizing our standards from the digital study protocol through the analysis and study results by adding necessary semantics to link our information.  By connecting the information digitally, CDISC is creating reusable, interoperable and easier to implement standards, eliminating manual tasks, increasing data quality, enabling AI, and empowering the industry to deliver clinical trial results faster, more efficiently, and at a lower cost accelerating delivery of new therapies to patients. In addition, CDISC believes connected standards and open-source tools to use those standards will help academic research more easily adopt and use the standards which have been a previous challenge in this community.  This presentation will provide an overview of the 360 initiative, the challenges academic research has faced in adopting standards, and opportunities for academia to embrace the standards and technology more easily. 

As clinical research places increasing emphasis on transparency and reproducibility, the secondary use of clinical trial data has become an essential pathway for generating evidence. Vivli is a global data-sharing platform that provides access to clinical trial data from pharmaceutical companies, academic institutions, and research organizations. This presentation highlights a meta-analysis on rheumatoid arthritis conducted using Vivli, which evaluated the real-world effectiveness of biologics and provided insights for clinical decision-making. In addition to presenting key findings, the talk will address practical aspects such as data access procedures. The broader applicability of Vivli across various disease areas will also be discussed, demonstrating how cross-border data sharing enhances international collaboration and accelerates innovation in clinical research. 

Real-world data (RWD), including EHRs, is becoming essential for research and regulatory submissions. Yet, unlike clinical trial data, RWD is notoriously difficult to convert into CDASH and SDTM formats due to its irregular structure and noise, leading to concerns about bias. Existing tools built for randomized controlled trial data often fail in this setting, leading to fragmented, ad-hoc approaches that do not scale. We introduce Hawk, a content-aware AI middleware that connects directly to raw RWD sources and generates harmonized, analysis-ready datasets with full regulatory-grade lineage. By leveraging AI, including LLMs, Hawk enables systematic, scalable, and trustworthy data processing aligned with FDA requirements (see press release about one of Droice’s discussions with FDA on RWD reliability: https://finance.yahoo.com/news/droice-labs-discusses-superlineage-real-150000597.html). We will showcase practical examples of AI-driven RWD transformation from EHRs, demonstrating how automation can overcome longstanding challenges and accelerate the use of RWD in regulatory science. 

We have conducted surveys targeting CDISC beginners in academia (members of the Shin-Ahiru team), organized both online and on-site hands-on seminars, and developed the beginner-friendly search system "Oshiete Neko-chan" app. In this session, we will share insights gained from these initiatives and discuss our upcoming support plans. We will also provide information on how to register for this year’s hands-on seminars.

In Japan, implementation of CDISC standards is required for Investigator-Initiated Trials but remains optional in academic research. Consequently, while many institutions express interest, adoption has been limited. Reported barriers include a shortage of personnel with sufficient expertise and a lack of clarity regarding practical approaches to implementation. The CDISC Japan User Group (CJUG) provides an inclusive framework to support beginners; however, participation is often perceived as psychologically challenging, and limited awareness of CJUG’s activities further contributes to hesitation. To address these issues, we established the “Beginner’s initiative virtual clinical trial ~ New Duck team~” composed primarily of clinical research support staff from an Academic Research Organization (ARO), which has been active within the CJUG SDTM team since December 2022. This session will present the team’s initiatives as a case example, thereby introducing CJUG’s activities and highlighting its potential role as an accessible learning platform for institutions seeking to adopt CDISC standards.

In recent years, the demand for higher quality and efficiency in clinical research has become more critical than ever. For medical institutions like ours, conducting investigator-initiated trials presents a unique set of challenges, particularly the lack of a unified framework for data management. This often leads to inconsistencies across studies and a heavy reliance on the skills and efforts of individual staff members to maintain data quality.

At the National Hospital Organization (NHO) Nagoya Medical Center, we recognized that this reliance on individual effort was a significant barrier to improving our overall research efficiency and ensuring sustainable, high-level quality assurance.

To address these challenges and establish a more robust and scalable research infrastructure, we embarked on a project to standardize our clinical trial operations by implementing the CDISC standards.

This presentation will introduce our practical approach to this initiative. We will detail our pilot project, the strategic development of a user-centric Electronic Data Capture (EDC) system to facilitate adoption, the obstacles we faced, and the solutions we implemented. Ultimately, we will share how this journey is helping us transition from individual-based quality control to a truly organizational-level quality management system.

Real-World Data (RWD) is increasingly used for regulatory submissions. Registry data as well as direct electronic health record (EHR) data sources are used to enhance evidence and drug development processes. When it is used for regulatory purposes, regulatory bodies, including the FDA and EMA, increasingly mandate robust data lineage practices to ensure data integrity, governance and auditability of the data. Data lineage refers to the comprehensive tracking of how data is generated, transformed, transmitted and used across systems, enabling transparency and trust in data-driven decision-making.

Implementing effective RWD lineage involves overcoming challenges in integrating disparate systems, managing data silos, and ensuring metadata consistency. During the presentation, we will dive into the regulatory lineage requirements, interoperability and some experiences with real-world data lineage, highlighting pitfalls and best practices.

[Background] ECRAID - Base is a Europe-wide initiative at the interface of observational and interventional studies. It consists of five studies. We report the development and experience of the ‘central library’ of CDISC-compliant data dictionaries. 

[Methods] We worked with the stakeholders to develop a list of domains and required items across the five studies which served as the core of the ‘central library’. Data items were mapped to CDASH. Master lists were mapped to existing ontologies.  

[Results] We identified seventeen cross-study domains. On average, we had about 85% of variables mapped to CDISC. 

[Discussion] CDISC is quite well suited for studies at the intersection of observational and interventional studies. For better harmonization and standardization in the infectious diseases research community we need the various stakeholders to work together. 

The CDISC Unified Study Definitions Model (USDM), Biomedical Concepts, and the implementation of end-to-end study data automation is hard to envision from model diagrams and conceptual drawings. To make the USDM vision tangible, we will present a technology demonstrator showing how the model can be used as the foundation for:

1. The creation of a digital study protocol including the study design and the schedule of assessments (SoA).
2. Driving data capture artefacts from the SoA
3. Loading data including bulk loads, human entered and from EHR sources (FHIR)
4. The automated generation of SDTM.
5. The creation of submission ready artefacts such as aCRFs and define.xml

The presentation will focus is on what is possible and show how USDM, BCs, and SDTM can be brought together in a seamless manner, allow for the move away from a siloed and processed focused way of working to a data-centric world of seamless integrated standards.

This practicum project, conducted under the supervision of The George Washington University, USA as part of the supervised integration and implementation of educational or clinical health professional leadership program, aimed to bridge the gap between academia and industry in clinical data science education. The CDISC Toolkit for Academic Professionals was developed and piloted at Technological University Dublin, Ireland to enhance faculty capability in teaching CDISC standards. Through a three-part seminar incorporating modular learning, reflective journaling, and evaluation feedback, faculty participants demonstrated significant improvement in knowledge, confidence , and intent to integrate CDISC concepts . Qualitative analysis revealed heightened motivation and practical understanding. This presentation outlines the practicum’s implementation process, outcomes, and sustainability strategies, offering an evidence-based, scalable model for embedding CDISC education in academia and preparing future leaders in data-driven clinical research.