Exclusive interview: Diane Lacroix talks innovations in clinical data management

By Liza Laws

- Last updated on GMT

© Getty Images
© Getty Images

Related tags Clinical trials Data management Patient centricity patient engagement Research Patient recruitment

In celebration of Clinical Trials Day last month, OSP sat down with Diane Lacroix, vice president of clinical data management at eClinical Solutions, to delve into the latest advancements and future directions in clinical trials.

Diane shares her expert insights on how creative data solutions are transforming the landscape of clinical research, enhancing efficiency, and improving patient outcomes.

What significance did Clinical Trials Day hold for your company?

Each year, Clinical Trials Day is an opportunity for eClinical Solutions to celebrate the people involved in clinical trials and the advancements in processes and technology that are enhancing efficiency and data quality and reducing cycle times to bring life-saving treatments to patients faster. We’re passionate about helping our clients address the data complexities of modern trials and our teams are proud to make this contribution to the important work of clinical research.

How does your company contribute to advancing clinical trials and medical research?

eClinical Solutions’ data and analytics platform and biometrics services experts help bring together the people who work in research with their trial data, so they can improve and accelerate clinical trials. Our elluminate Clinical Data Cloud and biometrics services enable biopharma researchers to manage trial complexity faster and more efficiently by centralizing data into a single source of truth with data applications they can access for trial conduct and decision-making. This enables our clients to access accurate and timely data insights, empowering them to make informed decisions, streamline collaboration with colleagues, and adopt risk-informed approaches to scale their data operations. As a result, they can significantly reduce cycle times, improve productivity, and ultimately drive the development of tomorrow’s breakthrough treatments using today’s resources.

How has the landscape of clinical trials changed over the past few years, and how has your company adapted?

The science behind drug development has changed, and as a result so have clinical trials. We’ve seen groundbreaking and promising strides in drug discovery, from mRNA and gene therapies to synthetic biology, along with new, tech-driven methods for patient data collection such as wearables and trackers – all leading to greater volumes of clinical data. Personalized medicine, digital trials and decentralization increase non-EDC data volume from external data sources and exacerbate data challenges. Data can easily become siloed and manual methods of data handling can’t scale to adapt to this new data ecosystem. Meanwhile, organizations face intense pressure to manage trial complexity in less time and with fewer resources. At eClinical, our company and the elluminate platform were created in anticipation of this new data landscape of digital trials. Our technology has adapted and grown to serve stakeholders across the data lifecycle. As a tech and services organization, our biometrics services team has also transformed significantly along with the industry, evolving our processes around technology.

Innovation and Technology: What role does technology play in your clinical trials?

Trials routinely use at least six external data sources, with many incorporating over 10. Meanwhile organizations remain focused on accelerating cycle times. Removing fragmentation and silos is critical. End users need data access and tools that facilitate risk-based approaches, collaboration and automation in place of inefficient, manual methods for interacting with data. In addition to addressing the data challenges of today, modern research demands modern, scalable technology that supports future breakthroughs. In our eClinical Solutions biometrics services team we use our own technology, elluminate, and other technologies to provide our clients with increased data democratization and accessibility in a regulated environment – created and informed by deep expertise in clinical data.

How are you utilizing digital tools to enhance the efficiency and accuracy of your clinical trials?

Digital tools are necessary to streamline today’s data and analytics pipelines. With the influx of systems and sources for data, connecting your digital data ecosystem is essential to facilitate data-driven decision-making, as well as gain oversight of critical data for quality outcomes. With data transformation, automation and analytics capabilities from ingestion to submission, we use technology to serve stakeholders from clinical data management and medical review to biostatistics, statistical programming and data science. Our platform is refined with ongoing input from eClinical’s Biometrics Services organization, and in services, we use our own technology and others to accelerate data processes and workflows for clients. We bring automation to various processes including EDC build so our clients gain program level efficiencies across studies and utilize AI to speed data review for our data management services clients. Our life sciences clients have seen 50%+ improved efficiencies for data ingestion, standardization and review. Real-time analytics help our clients reach important KPIs and improve data quality, trial diversity, time-to-market and ROI.

Are there any upcoming technological innovations or tools you are excited to implement in future trials?

Earlier this year, our annual Industry Outlook Survey revealed that AI and ML technologies are expected to significantly impact efficiency and outcomes in clinical research. Fortunately, we’re positioned to help our clients make this shift from AI hype and anticipation to realistic adoption. Artificial intelligence is no longer hype; it’s here today and data managers are a key ‘human in the loop’ for the powerful application of these techniques. It starts with strong tech foundations, but once you have comprehensive trial data across systems and sources, this enables data management teams to use AI assistance to conduct data review in a more efficient, scalable way. We can surface outliers and further speed time to insights. AI can also help us in the critical path to adoption of risk-based approaches for data by performing tasks at scale on non-critical data, freeing up time for a focus on the data that matters. I’m looking forward to helping my team in data management continue to adopt AI models that provide value for our clients.

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