Generative AI (GenAI) is quickly proving itself to be one of the most transformative technologies of our time with its ability to synthesize data and create new content as well as (or sometimes better than) its human creators.
But GenAI needs a powerful partner if it’s going to wow users with high-quality, trustworthy, bias-free results: the right training and reinforcement data to help it learn and grow over time. In the healthcare and life sciences, real-world data (RWD) from electronic health records, claims, images, socioeconomic data, and medical devices, is the fuel that allows GenAI to burn its brightest.
When this dynamic duo comes together in the right way, it creates opportunities to produce actionable insights that reduce manual work, speed up research, improve care delivery, save costs, and create better outcomes across the entire care continuum.
The RWD/GenAI combo is so promising that McKinsey estimates it will create between $60 and $100 billion in annual value across the pharma industry.
With such potential to rewrite the way life sciences interacts with its vast data assets, what are some of the specific use cases for bringing RWD and GenAI together in innovative and effective ways?
Accelerating research and discovery of new therapies
Life science companies spend a huge amount of time and resources simply trying to figure out if molecules have the potential to solve biological problems. Leaders in the field are already showing that large language models (LLMs), a key component of GenAI, can design new molecules with high therapeutic potential hundreds of times faster than humans in a wet lab.
This gives researchers a leg up on where to focus their resources, dramatically reducing the investments required and improving the long odds of a successful drug making it into the hands of patients.
Bringing RWD into the mix can make the process even more efficient by informing the risk-benefit profiles of new therapies as they move through the R&D pipeline, thereby reducing the potential for negative events in patients and making expensive clinical trials more likely to succeed.
Personalizing medicine to enhance equity and maximize positive outcomes
Therapies are only effective if they get to the patients who are most likely to respond to them. RWD and GenAI are speedily becoming vital tools for understanding how to personalize treatment recommendations in increasingly diverse patient populations – a key component of the industry’s collaborative push toward health equity.
Using RWD from diverse and representative patients, life science companies can apply GenAI tools that accurately analyze patterns in individuals and communities to identify gaps in care, highlight access or affordability issues, and match underserved populations to clinical trials that might be appropriate for their needs.
This can lead to more precise applications of treatments tailored to the individual’s biology, such as the case of “Paul,” who found an effective treatment for his blood cancer by letting AI run a sample of his cells through multiple therapy options simultaneously instead of having to try each medication in succession over weeks or months. With the help of GenAI and RWD, Paul was able to enter remission after finding an option that worked on his aggressive cancer, reported the MIT Technology Review in 2023.
Enhancing approvals, commercialization, and post-market activities
The benefits of the RWD/GenAI combination continue throughout the drug development lifecycle, including during the complex processes of approvals, commercialization, and post-market surveillance.
McKinsey believes this is where the bulk of the financial benefits may lie, with up to $30 billion a year in savings at stake for the companies that adopt the right tools and processes.
RWD is already becoming much more widely accepted as part of the drug approvals process, with the FDA and other international regulatory agencies now encouraging the industry to collaborate on effective ways to integrate new data strategies into the conversation.
Operational and commercial teams also stand to benefit from this movement, with GenAI and RWD assisting with manufacturing optimization and quality assurance, as well as the creation of personalized content for provider and patient education.
For example, using GenAI and RWD to monitor and manage patient experiences, both clinical and administrative, can help to optimize engagement, improve adherence rates, inform strategic relationship decisions, and connect with new markets to enhance equity and maximize the financial return on safe and effective therapies.
To make the most of this emerging ecosystem, life science companies will need to access high-quality, expertly curated real-world data and robust, reliable artificial intelligence models that work together seamlessly to produce meaningful results.
Choosing a partner with experience in both areas, such as COTA, to support the infusion of RWD and GenAI into the drug development process can lead to faster, more actionable insights for companies that translate into better outcomes for patients.
With rich, reliable, representative RWD on more than 2 million patients and a leading-edge suite of artificial intelligence tools to unlock the potential of these and dozens of other use cases, pharma companies can harness the full range of benefits that the new AI environment has to offer.
To learn more about how COTA is helping visionary life science companies accelerate the synergy between RWD and GenAI visit cotahealthcare.com.
