Improving clinical trial transparency enhances the credibility of research, allows for more informed decision-making by healthcare professionals (HCPs) and patients, and contributes to the advancement of scientific knowledge. To drive this, regulatory agencies such as the US FDA, EMA, and Health Canada, have implemented policies and regulations covering the registration of clinical trials through to publishing of clinical trial documents and data sharing. It is important to evaluate the real-world impact of these initiatives with key stakeholders. Are HCPs and patients aware of them and how might they be using the information? Do the regulations enable researchers to conduct an independent analysis of clinical trial data?

To assess this, we are conducting a cross-sectional study using survey questionnaires and interviews. The main objectives are to evaluate the:

• Current level of awareness and understanding of clinical trial transparency initiatives (Focus group: HCPs, patients, clinical trial researchers)

• Challenges with data sharing requests (Focus group: Clinical trial sponsors)

• Responsiveness of clinical trial sponsors on data sharing requests (Focus group: Clinical trial researchers) Data collection and analysis is ongoing.

The results of the study will be presented descriptively.

The Helping to End Addiction Long-term® Initiative, or NIH HEAL Initiative®, is an NIH-wide effort to speed scientific solutions to stem the national opioid public health crisis, funding over 1,000 projects nationwide. The HEAL Data Ecosystem is an important part of this effort, consisting of partners working together to: 1) aid investigators in making their data findable, accessible, interoperable, and reusable (FAIR); 2) develop the HEAL Data Platform so researchers can discover and compute over HEAL datasets; and 3) translate HEAL research discoveries back to communities and other stakeholders. By empowering researchers to make their HEAL-generated data FAIR, the HEAL Data Ecosystem promotes data sharing. In this presentation, representatives from the HEAL Data Stewardship group will discuss the data sharing practices of the HEAL initiative, the model used for the HEAL Data Ecosystem and how it helps to enable data transparency, and the tools that are available to make HEAL data FAIR.

DICOM (Digital Imaging and Communications in Medicine) is a standard for the communication and management of medical imaging information and related data. It is widely used in healthcare for exchanging information of X-rays, CT scans, etc. DICOM plays a crucial role in the field of medical imaging, enabling the standardization of digital medical images and associated information. Anonymizing DICOM images is a critical step to protect patient privacy and share DICOM files for research and development purposes. However, there are several challenges such as: editing information of DICOM files, transfer of large data, anonymization of thousands of files, anonymization in sync with study dataset and documents etc. To overcome the above challenges there are some solutions available such as: open-source libraries to read and view DICOM files, automation of DICOM editing, Encryption, Raise Awareness, etc. This presentations will be discussing on these challenges and possible solutions to these challenges.

This presentation explores the challenges and insights gained from our experience with anonymizing complex health data. The primary focus is on the need for robust technical and organizational measures, such as a secure processing environment, to ensure controlled data access. Implementing these control measures allows us to maximize data utility while minimizing risk. We discuss specific technical challenges encountered in the data anonymization process and assessing the risk of re-identification, particularly in handling longitudinal and various genetic data types. Notably, the lack of clear guidance on identifiability aspects of healthcare data is highlighted. Our findings underscore the critical role of establishing clear internal governance, with well-defined roles and responsibilities, to facilitate a seamless data anonymization process. Additionally, we emphasize the importance of providing guidance and training for anyone working with anonymized data to dispel common misconceptions surrounding anonymization.

As AI capabilities advance, there is tremendous potential to accelerate and enhance clinical trial transparency through automated plain language summarization of study documents. However, without proper governance, AI could also enable generation of unofficial documents lacking appropriate context, qualifications, and sponsor approvals. Humans must remain in the loop to ensure AI is used responsibly. Models should be carefully validated to generate accurate, balanced representations of trial data that avoid exaggeration. Sponsors and technology vendors must collaborate to establish clear policies and auditing mechanisms. When thoughtfully applied, AI-enabled simplification can make vital trial information more accessible and understandable to patients and healthcare providers without compromising compliance. We will demonstrate a tool we’ve developed that uses natural language processing to automatically condense a sample protocol into a patient-friendly synopsis. This showcases the benefit of AI while respecting the requirement for human review.

The secondary use of clinical trial data can significantly increase its value beyond the original analysis and help find new insights, signals, and ultimately treatments. A major hurdle to data reuse is a lack of transparency and discoverability. SDTM and ADaM datasets have reliable, strict naming conventions and consistent structure within and across studies. These qualities make it possible to create generalized programs which stack and process datasets in an automated manner, regardless of their contents. The output is descriptive metadata that is useful, searchable, and not constrained by privacy concerns. Such metadata can be made open and searchable while access to data remains restricted. It allows to find tests, events, visits, and characteristics across datasets and studies before requesting access to relevant data assets. This paper describes the technique for leveraging CDISC standards and simple statistics to unlock the value of clinical data and increase its transparency and discoverability for researchers and data scientists. It also discusses the benefits and challenges of this approach and provides practical examples of how it can be implemented.

The presentation will provide a summary on the Good Transparency Practice (GTP) working group deliverable. The group created a GTP guidance document that defines a set of best practices for data transparency. By outlining the distinct roles of the Data Controller, Data Anonymiser, and Data Recipient, the project aims to provide a means to achieve accountability and traceability through the anonymisation process, while providing assurance that privacy requirements are upheld.

How do we ensure the integrity and usefulness of health data when the legislative and regulatory landscape is rapidly changing before our eyes? Debates over data protection & privacy legislation are being influenced by the evolving perspectives of AI, in particular due to the feats of Generative AI. And this on the heels of the ongoing debates over data protection while producing useful data for the European Health Data Space. Drawing on insights from my testimony before a parliamentary committee, I will discuss the impact of data protection laws on health research and analytics. Emphasizing the importance of privacy-enhancing technologies, the talk will address how to maintain useful data while ensuring compliance with global privacy standards. The focus will be on the development of responsible data access models that support international collaboration and research, particularly in a post-COVID-19 world where global data sharing has become paramount for public health advancements. The session aims to offer a perspective that balances privacy concerns with the need for efficient and effective data sharing across borders, a critical aspect for advancing global healthcare initiatives.