Addressing Growing Research Data Complexity with Practical, Technology-Driven Solutions

Key Takeaways from Engage 2023

Each year, eClinical Solutions invites thought leaders and experts in the life sciences industry to join together with eClinical customers and share innovative ideas at our annual Engage conference. This year, attendees were excited to join us in Boston to discuss how we can work together to make clinical data management more efficient, both in terms of new technologies and improved processes. The following is a summary of the key themes and takeaway messages from the event.

1. Effective Adoption of Mega Innovations (like AI) Require Evolved Thinking Around Data

We now have more ways to gather clinical research data than ever, along with technologies that help us to expand the value of that data. But to realize the full benefit without being overwhelmed, we must build more data-centric organizations. This means that we need to prepare the right infrastructure to be able to effectively leverage innovations like generative AI.

This was highlighted in the vision shared by eClinical’s CEO, Raj Indupuri, and reaffirmed by Dr. Nimita Limaye, Vice President, Life Sciences R&D Strategy and Technology at IDC in her keynote presentation. Dr. Limaye shared invaluable insights and original research on the use of generative AI and the digital revolution in our industry and how they are redefining what it means to be data centric. These conversations pointed out the importance of looking beyond the hype associated with these once-in-a-lifetime technologies and creating solid foundations (with guardrails) to facilitate success.

2. Technology Integrations Take Time – We Must Remain Patient

After all, we are the industry that took two decades (if not longer), to move from paper-based trials to electronic trials. Hence, it is not surprising to learn that innovations like AI/ML, wearable devices, sensors, etc., will take time to take root and grow beyond the hype to yield the outcomes we are looking for. It is essential for enterprises to embrace innovation to advance clinical research. This was showcased in the presentation from Ken Getz, Executive Director and Research Professor, Tufts Center for the Study of Drug Development.

3. Trials are Becoming More Complex – and More Expensive. Technology is Critical to Lowering Drug Costs

Getz’s presentation also covered the growing cost of drug development. According to Tufts CSDD, the cost to bring a new drug to market is now over $2.1B. This is a result of the increased failure of drugs plus the increasing complexity of clinical research. The talk from Getz was truly sobering, opening eyes to the challenges we face as an industry and how technological innovation must play a big part in reducing costs and speeding development, which in turn, will help us to bring more affordable treatments to patients.

4. We are Getting Closer to Successful, Meaningful Integrations of EHR/EMR Data into Clinical Research

Using data captured as part of a healthcare setting to drive clinical research without resorting to double data entry and a plethora of systems and applications has been a long-standing need in the industry. Companies like Flatiron Health are pioneering the technology required to make this a reality, and doing so can lead to significant reductions in effort and cycle time. Improved outcomes seem closer than ever and, in some cases, may already be happening. Flatiron Health General Manager, Clinical Research Alex Deyle‘s presentation and subsequent conversation with Raj Indupuri provided insights into the need for technology innovation to be embedded into standard clinical processes, emphasizing the subsequent benefits to clinical research.

5. Data Management Must Evolve to Meet Growing Complexity and Volume of Data

A panel discussion led by Diane Lacroix, VP, Clinical Data Management, eClinical Solutions, with participation from Sherry Volk, Principal Portfolio Lead, eClinical Solutions, Bethann Schrader-Giancarlo, Director of Data Management, Pharvaris, and Emily Pereira, Director of Clinical Data Management, bluebird bio highlighted the ongoing transformation of clinical data management into clinical data science.

Per Tuft’s CSSD study results, the number of data points collected in a typical Phase III study has increased from 900K+ in 2011 to approximately 3.6 million in 2021. That huge jump renders traditional approaches to clinical data review, analysis, and decision-making highly inefficient. Innovative technology is needed to not only clean and validate the data but also to aggregate and analyze it for easier review and scientific decision-making. The expectation is to have the data at the fingertips of the research staff in as close to real time as possible. This drives the demand for tools like eClinical Solutions’ elluminate, a leader in Clinical Data Analytics platforms per Everest Group’s 2023 PEAK Matrix Assessment.

6. Better Change Management and Validations will Unlock Better ROI for New Technologies

Most failures to successfully realize the value of new technologies can be traced back to mistakes made early in the planning stages. This was highlighted by a panel discussion led by Ken Getz with PwC Partner Ian Shafer, Igor Proscurshim, Head of Clinical Development at DynamiCure Biotechnology, and David Evans, Associate Director, Clinical Data Systems at BioMarin. This discussion focused on the importance of building a compelling business case along with thoughts on gaining buy-in and adequate budget for new integrations.

Additionally, I led a panel discussion with my colleague Evan Grunbaum, VP, Quality and Compliance, eClinical Solutions, and Karen Travers, Principal at Halloran Consulting Group that highlighted the challenges enterprises face when proactive approaches to change management are not adopted. This may result in poor platform/technology adoption and a lack of ROI. Also, enterprises seem to struggle with the right approach to validation of SaaS platforms and tend to reinvent the wheel instead of simply trusting their SaaS platform partner and patiently following the implementation process.

7. The Time for Waiting on Automation is Over

As the number of data points collected in clinical trials grows, the source of this data is also undergoing a huge shift. Traditionally, Electronic Data Capture (EDC) used to be the source of 60% or more data. However, with the advent of decentralized clinical trials (DCT) and digitalization in general, EDC is now the source for only 20% of the data, and more than a dozen other data sources like sensors, and labs are providing the rest of the data.

This calls for platforms and tools that can aggregate diverse data sources with ease, automate data cleaning and validation, generate insights and analysis by using rules engines or AI/ML, and even standardize the data (to internal and industry standards like CDISC), preparing it for submission. Demonstration of eIQ Review, an AI-driven data review tool, and the overwhelming attendance to our ‘AI-driven data management approach’ workshop drove home just how top of mind these issues are for our customers.

8. Risk-Based Quality Management (RBQM) and Statistical Computing Environments (SCE) are Critical to Simplifying, Scaling, and Speeding up the Clinical Research Process

Tuft’s CSSD report highlighted the trends in RBQM adoption across the industry where more than 50% of companies continue to invest and use RBQM process. Centralized monitoring, using RBQM, will simplify the clinical research operations, reduce costs, and accelerate the study closeouts.

Implementing an SCE to continuously analyze the data using programs written in diverse languages like SAS, R, and Python to monitor safety and efficacy will accelerate time to database lock, data standardization, and eventual regulatory submission. Sessions led by my colleagues on these two topics found a number of audiences that were keen to not only understand the challenges in the implementation but also learn best practices and lessons to apply within their organizations.

9. Biometrics Services are Delivering Great Value for Research Teams

eClinical Solutions provides a significant amount of Biometrics Services to global life sciences organizations. Katrina Rice, Chief Delivery Officer, Biometrics Services shared a number of benchmark key performance indicators (KPIs), demonstrating what has been achieved through leveraging elluminate, garnering a lot of positive interaction with attendees.

Those in attendance were also excited to receive practical advice from Demi Niforos, VP, Biostatistics and Statistical Programming, on her biostats and SAS programming teams’ experience in adopting elluminate SCE, including lessons learned from the challenges they had to overcome. Best practices shared by Nathan Johnson, VP, Digital Innovation at eClinical Solutions from his team’s product development and engineering journey were profound and well-received.

10. Best-of-Breed, Interoperable Clinical Data Infrastructures are Key to Effective Tech Integrations

In my interactions with clients, partners, and colleagues, one thing that stood out for me is that the escalating complexity of clinical research is also leading to more complex technology infrastructures. This calls for the adoption of tools, technologies, and platforms that are not only best-in-class, but also highly interoperable. The case studies and practical experiences shared by many pharma and biotech companies at the conference, along with the thought leadership and insights from research analysts and industry experts reinforced this aspect for me.

Innovative technology platforms like Snowflake are being leveraged by many sponsors to modernize their data architectures and improve data sharing and governance. They will continue to push the industry forward by helping better manage large volumes of data while putting the right guardrails to govern, share, and use the data with novel technologies like AI/ML. Clinical data and analytics platforms like elluminate will provide the lakehouse architecture required to make this happen within the life sciences R&D value chain.

This year’s Engage event has solidified that research industry stakeholders are working hard to address the growing complexity of clinical trial data, including challenges posed by the exponential increase in data sources. Attendees were energized and encouraged to learn best practices and informed points-of-view from a broad range of eClinical Solutions experts and respected thought leaders from leading sponsors, academic institutions, technology leaders, and more. Over the course of the two-day event, it became clear that researchers are looking for practical guidance and ready-to-go solutions to help them integrate tools such as AI/ML, enact more efficient processes and workflows, and, in general, adopt approaches to help them work with trial data more efficiently.

Learn more about the annual Engage conference and watch session recordings.

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