Key Takeaways from SCOPE Summit for Clinical Operations Executives 2024
Tobi Guennel, PhD, QuartzBio’s Senior Vice President of Product & Chief Architect, energized the Biomarkers & Precision Medicine track at SCOPE 2024 by showing how generative AI can address challenges faced biospecimen and biomarker operations teams, as well as clinical and translational teams, when gathering insights on samples across all parts of a complex biomarker data ecosystem.
Sponsor teams need faster answers from disconnected data streams.
Sponsor teams want more clarity. The current chaotic state of biospecimen management hinders speedy decision-making. Because of the influx of specimen (and their associated data) coming from hundreds of varied locations (in just as many varied formats), teams can feel overwhelmed with the sheer volume of information to manage.
How is the current system holding businesses back?
Quality data is the foundation of precision medicine but most of this critical data is siloed, inconsistent, and inaccessible. In fact, half of all translational scientists in our industry struggle to access the insights they need to complete their work effectively. Further, many of these scientists report only 60% confidence in the accuracy of this data when manual data entry is considered, and the revenue loss due to data ecosystem challenges is over $31B annually. But, for the companies who can overcome these data and technology ecosystem challenges, the reward is great: these companies are 23x more likely to be profitable than those who cannot.
Generative AI overcomes data challenges
As discussed at the SCOPE Day 1 plenary panel on generative AI, there is now broad awareness of the potential for AI to accelerate drug development. Specifically, we heard how AI is already enabling universal data structuring, harnessing large language models to tackle clinical research tasks, and integrating multimodal data.
Generative AI empowers drug development teams to overcome data and technology ecosystem challenges because it can automate data management tasks and break down data silos, helping businesses gain a holistic view of their operations, and free teams to make more informed decisions, faster. Instead of requiring an extensive understanding of coding and statistical modeling, generative AI now allows users to ask requests in plain language. Ask a question, get an answer.
Generative AI applied to both data management AND business intelligence drives precision medicine at scale
With the AI-enabled Biomarker Intelligence Platform, QuartzBio is giving sponsors a smart and scalable platform for deploying generative AI to enhance decision-making throughout the precision medicine R&D lifecycle. This platform is built on a foundation of high-quality data, because insights mean nothing if you don’t first ensure quality data. QuartzBio’s AI-powered data management tools are tailor-made to streamline data ingestion, QC, and mapping for drug development workflows, helping to improve data quality and reduce errors by leveraging automation to surface data issues to stakeholders.
Layered on these data management tools, the platform contains a suite of business intelligence tools that we call biomarker intelligence tools because they were purpose-built to address the challenges in biomarker-guided drug development. These tools are designed by subject matter experts to improve business agility through data exploration, dashboards, analytics, and reporting.
QuartzBio Biomarker Intelligence Platform
Leveraging AI across the platform makes our data management and business intelligence tools smarter, easier to use and more efficient to configure. To create a true force amplifier and decision accelerator, we’ve built our AI-powered Virtual Assistant. This assistant transforms the way users interact with our platform to shorten the time from having a question to receiving easily consumable information. The Virtual Assistant is your personal guide through our Biomarker Intelligence Platform, allowing you to quickly navigate to the right tool and streamline your data management and business intelligence.
The presentation highlighted four common use cases of the AI Virtual Assistant as applied to QuartzBio’s virtual Sample Inventory Management SaaS product:
- Sample monitoring for collection, testing, shipping and storage
- Monitoring vendor performance
- Tracking clinical trial site performance
- Monitoring query trends in real time and surfacing root causes