Regulations & Standards: Addressing the challenges & opportunities for academia, pharmas & biotechs
Monday 9 Oct, 11:45a-12:45p PT
Shamit Y. Patel, Senior Manager, Public Sector, Medidata
The regulatory environment for clinical trials is becoming more complex. Compliance with Clinical Data Interchange Standards Consortium (CDISC) standards is mandatory for clinical trial submissions to the US Food and Drug Administration (FDA) and the Pharmaceuticals and Medical Devices Agency (PMDA). While this previously only affected pharmaceutical companies and biotechs, academic institutions are now increasingly required to comply with CDISC as a condition of their grant funding. And given this is a new vocabulary for academia, the learning curve is naturally steep.Standards management also poses a huge challenge for the industry. Without standards in place, it’s difficult to quickly design high quality, consistent studies – let alone meet tight study build deadlines. Traditional methods of running clinical trials, whether paper-based or spreadsheet driven, don’t support effective standards management. However, implementing standards is in itself a huge commitment. And doing it successfully, navigating the pitfalls, requires experience.The many processes and requirements around clinical trial design also make study set up problematic. Lengthy approval cycles, legacy data silos, and a lack of traceability and visibility are commonplace. So metadata quality and project timescales are subsequently impacted.But with such challenges comes great opportunity. In this presentation we will draw on real-world use cases, from the perspective of academia, pharmas and biotechs we’re working with. Attendees will hear the common challenges faced regarding regulations, standards and trial design, and see how similar organizations are overcoming these problems and realizing significant benefits with the adoption of technology and the help of industry knowledge.
Adopting an AI Mindset: How to Improve Data Literacy Within Clinical Data Management
Monday 9 Oct, 3:00-4:00p PT
Olgica Klindworth, VP, Data Quality and Risk Management Solutions, Medidata Laurie Callen, Senior Director, Clinical Data Management, Moderna, Amanda Bayer, Associate Director, Clinical Data Management, PPD, Prasanna Rao, Senior Director, Global Head of AI/ML Data Monitoring and Management, Pfizer, Katy Ghantous, Director, Data Science, Medidata
As clinical trials become more complex, artificial intelligence (AI) and machine learning (ML) are increasingly being deployed to support the scale of data acquisition and optimize the efficiency of data management activities. While these innovative technologies hold the promise of reducing data monitoring costs and timelines and allow for the allocation of resources away from manual, repetitive tasks, there are a number of risks and challenges that may impact organizational success with AI implementation. For example, deploying AI without a full understanding of how the underlying algorithms work, the specific business use cases for which the models add value, or that the process is iterative and requires diverse inputs in a feedback loop. Despite the potential benefits of AI and ML, the need for more data literacy among operational teams is one of the biggest challenges. It can lead to frustration, poor adoption, and low return on investment.
This panel session will bring together clinical data management and data science experts to discuss how to develop data literacy that improves the adoption of AI and analytics. Panelists will share strategies and lessons learned from real use cases on how they empowered data management teams with the necessary knowledge to realize the full potential of AI and ML.
Innovation in Action: Experience AI and Automation that Delivers for Clinical Data Managers Today
Monday 9 Oct, 4:30-5:00p PT
Tina Brewer, Solutions Consultant, Medidata
The role of the clinical data manager in ensuring data integrity and quality has never been more critical as higher volumes and velocity of digital data are collected from multiple sources. Traditional manual, retrospective, and exhaustive data management approaches can struggle to scale to meet the demands of today’s and tomorrow’s trials.
See how Medidata’s latest automation, AI, and analytics capabilities can make data aggregation, review, and reconciliation more efficient and effective, powering the transition from clinical data management to clinical data science.
Getting the “science of clinical trials” into the data scientist role.
Monday 9 Oct, 3:00-4:00p PT
Henry Lin, VP, Data Science & Algorithms, Medidata, Chair: Stephen Cameron, Director, Clinical Data Management, ICON, Brian McCourt, Duke Clinical Research Institute
With multiple white papers and Journal of SCDM articles describing our industries evolution into clinical data science, questions surround what differentiates us from classical data science and how our skills empower us to be strategic clinical insight deliverers on a national and international stage. This session will delve further into this evolution and highlight many of the important issues and concerns regarding training, qualifications, skills, contributions, and distinguishing characteristics of the profession and the professionals of this role.
Modern Medical Coding: The Digital Future of Tech-Driven Coding
Monday 9 Oct, 5:15-6:15p PT
Freddy Mendieta, Director, Product Management, Medidata
In an increasingly fast-paced medical review environment, the ability to access data in real-time and in one centralized location is critical to optimizing efficiency and processes. Many life science organizations are still working in data silos resulting in backlogs, as medical monitors and clinical teams are looking at medical coding listings at different times often with manual trackers like excel. When data is reviewed without collaboration and transparency, issues raised are often out of date by the time it comes back to the medical coding team, resulting in inefficiencies investigating issues that have since resolved. However like many of the areas that are rapidly evolving as part of our industry’s digital transformation, coding review is poised for change and cycle times can be greatly improved in the process.
This session will discuss how life sciences organizations are leveraging cutting-edge approaches to elevate and optimize the process for medical coding review. Learn how robotic process automation (RPA) can drive charting and analytics with respect to coding. Find out where the industry is currently and where we are headed when it comes to building out automation and using artificial intelligence (AI) and machine learning (ML) to code even the manual coding terms. The human element of coding remains, but shifts – elevating from routine manual work to the review, training and feedback for these intelligent models. As a result, digital efficiencies coupled with automation use cases will mean continuous oversight, improved quality and safety, and a richer clinical understanding of the patient experience. Join us to explore practical use cases illustrating the future of medical coding.
Community Collaboration: Leveraging Existing Networks to Increase DCT Access for Rural Populations
Monday 9 Oct, 5:15-6:15p PT
Holly Robertson, Senior Director, DCT Advisory Services, Medidata
What does it take to bring clinical trials to underserved patients in rural, southern communities? Collaboration, decentralizing technology, and existing community support. Many potential participants face barriers to learning about, enrolling in, and continuing in clinical trials due to geographical, socioeconomic, and other factors. However, solutions for data collection and management that allow for hybrid and decentralized clinical trials can bring clinical trials to any community where people are interested, but lack the means or opportunity to participate. By bridging gaps in rural access and expanding the pool of eligible patients for clinical trials, sponsors and sites can diversify study enrollment, improve patient satisfaction and retention, and increase the generalizability of study findings.
Join this session to learn more about the ways you can expand access to DCTs and the benefits for both patients and study data. The panel will discuss the importance of working within existing community structures to reach rural and underserved patients, of considering what DCT technologies will reduce burden versus introducing more burden for sites and patients, and of improving interest in and diversity of hybrid and decentralized clinical trials.
Innovation in Action: Experience the Future of CDMS Study Design
Tuesday 10 October, 10:15-10:45a PT
Marco Amorim, Director Product Management, Medidata
Configuring Clinical Data Management Systems (CDMS) in preparation for production operations often involves many people, many organizations, and many systems. Traditionally, this has led to siloed decision making and protocol interpretations. This leads to study set-up inefficiencies and also creates a bigger burden for downstream processes when reconciling deliverables. See how Medidata’s future of study configuration will address these issues and provide a more direct path from the protocol to actionable data for a multitude of roles.
Enhancing evidence generation by linking clinical trials to real-world data
Wednesday 11 Oct, 10:30-11:30a PT
Mehdi NajafZadeh, Senior Director, AI, Medidata
Despite generating a wealth of information, clinical trials only capture a snapshot of a patient’s care journey. Real world data (RWD) offers an avenue for sponsors to not only understand a therapy’s real world usage and impact, but also achieve greater success in their trial itself.
The discussion will focus on the feasibility of linking clinical trials (RCTs) to RWD and how this low-cost, high-yield strategy can be adopted in future trials. We will explore how linkage can enhance trials by extending patients’ follow-up time beyond trial completion, capturing additional effectiveness and safety outcomes, improving methods for handling missing data, and capturing cost and utilization end points. Linked RCTs with RWD can also improve understanding about the underlying reasons for potential discrepancies between RCTs and RWD studies.