In just a few years, artificial intelligence (AI) has become a trusted tool in early drug discovery. Researchers using models like AlphaFold 3, RoseTTAFold All-Atom and others, can predict protein structures, design potential compounds and simulate drug–target interactions that once took years to confirm in the lab.1 These breakthroughs have reshaped how scientists explore biology upstream; now, the same kind of AI-driven acceleration is reaching the other end of the pipeline with the rise of no-code analytics platforms for real-world evidence.2
Agentic AI tools like HealthVerity eXOs bring accelerated efficiency and transparency to traditional epidemiologic studies that long predated the term real-world evidence. So imagine running cohort studies, case–control studies, cross-sectional analyses, and time-to-event models in minutes instead of weeks or months. AI based tools bring that speed to help researchers understand incidence, prevalence, adherence, persistence, and outcomes in real-world populations.
Yet, who benefits the most from a tool like HealthVerity eXOs? Emerging biopharma and mid-market pharma companies face unique challenges as they balance limited resources with their demands for faster data driven insights. To address these challenges, practical AI solutions like HealthVerity eXOs, created through our partnership with Medeloop, are speeding up how research gets done. HealthVerity eXOs is an AI evidence studio that lets researchers ask plain-English questions and quickly generate transparent code and reproducible analyses that inform confident RWE decisions.
We spoke with Alexa Woodward, Ph.D., epidemiologist and Director of Strategic Accounts at HealthVerity, to explore the transformative role of AI in real-world data analysis, and how new no-code AI tools are helping researchers without extensive technical backgrounds. Dr. Woodward, who earned her Ph.D. in Epidemiology from the University of Pennsylvania and holds a master's in Bioethics from Columbia University, helps her clients identify optimal real-world data sources, design rigorous research protocols, and guide strategic decision-making across epidemiology, health economics, and outcomes research.
The rise of agentic AI and intuitive no-code platforms has changed how researchers interact with data. Instead of spending hours on manual coding and troubleshooting, they can now focus on interpretation and strategy.
“Epidemiology has always had a standard set of tools in its analysis toolkit — largely descriptive and inferential statistics — but it’s a limited set. Historically, that consistency made analyses comparable across studies because we all used the same methods. Only recently, in the last decade or so, has that changed. Now we have a full tool cabinet, two or three ways to approach an analysis. The question becomes, what’s the right balance?”
- Alexa Woodward, Ph.D., Director of Strategic Accounts at HealthVerity
This rapid expansion of available AI tools presents a paradox. You still need to know where and how to use them effectively. The ease of access and flashy features can lead to quick insights but a great no-code AI tool should be able to:
When we designed HealthVerity eXOs, we built those principles in from the start.
HealthVerity eXOs is designed not just as a query engine but as a smart assistant that guides users through each phase of data analysis by prompting users to ask the correct research questions and confirming each step before executing a query. This ensures transparency throughout its analytical process by clearly outlining each step, including the logic, methodology, and underlying code used to generate answers. At every stage, the actual code is accessible to users, who can review and export it for validation with other professionals or traditional analytical tools and methods.
"HealthVerity eXOs helps evaluate whether your research question is truly worth investing your time and budget. It acts as your analytic partner, confirming your criteria every step of the way."
- Alexa Woodward, Ph.D., epidemiologist and Director of Strategic Accounts at HealthVerity
This guided approach is critical for emerging and mid-market biopharma companies. Teams that may lack dedicated epidemiologists or advanced data science resources can still produce rigorous, actionable insights. We bridge the expertise gap by offering step-by-step guidance and ensuring researchers ask well-structured, impactful questions.
One important aspect of deploying AI effectively in healthcare analytics involves clearly defining what constitutes accurate, reliable data, often termed "ground truth." Dr. Woodward emphasizes, "We frequently discuss the concept of garbage in, garbage out, but spend less time talking about the data characteristics like representativeness and completeness that are required to generate reliable and generalizable models."
HealthVerity constantly addresses this by aligning data definitions and mapping across comprehensive datasets, which include closed and open claims, lab results, electronic health records (EHR), and more. This foundation helps ensure AI-generated outputs are trustworthy, traceable, and consistent. The next critical step is selecting the right data from these diverse sources to ensure the insights generated are both meaningful and relevant. HealthVerity eXOs has the ability to pull that relevant data and suggest the analysis steps while giving you full transparency into the procedure and diagnosis codes as well as the actual data analysis code.
The future of AI in real-world evidence is full of possibilities. Dr. Woodward anticipates a future where tools like HealthVerity eXOs will expand from hypothesis generation to managing comprehensive protocol execution from start to finish.
Want to request a demo of HealthVerity eXOs, connect with a team member today to get started.