Transforming Real-World Evidence Generation: Challenges and Innovations

The Limitations of Existing Technologies

Enter Generative AI: Changing the Way We Generate RWE

Introducing RWE Agent: A Solution for Democratizing Real-World Data Analysis

Revolutionizing Real-World Evidence: The Future with Generative AI

In the fast-evolving landscape of biopharma, the demand for Real-World Evidence (RWE) is skyrocketing. As organizations invest heavily in building internal capabilities—cloud-based advanced analytics platforms, self-service cohort builders, and dedicated teams of RWE scientists, statisticians, and programmers—the need for effective, scalable solutions grows ever more urgent. However, despite these advancements, many companies still find their capacity unable to meet the increasing demands of RWE. It’s clear: modernization of existing tools and processes is essential for companies to stay competitive.

The Limitations of Existing Technologies

Recent years have witnessed a surge of self-service tool suites across the industry, offering a dual promise: speeding up cohort definitions and automating routine analyses. Their intuitive point-and-click interfaces aim to democratize access to RWE, empowering a wider range of stakeholders to perform analyses without needing coding skills in SQL or R.

While these tools have successfully streamlined cohort definition and basic analytics, they fall short of fully democratizing RWE. Several challenges persist:

• Learning Curve: The user-friendly interfaces sometimes require extensive training, often presenting a steep learning curve.
• Integration Issues: Many vendors provide these solutions as hosted services, which can complicate data control and integration within biopharma environments.
• Scalability Challenges: The per-user licensing models can hamper scalability, preventing wide adoption and use of these essential tools.

With these limitations, the vision of genuinely accessible and democratized RWE remains elusive.

Enter Generative AI: Changing the Way We Generate RWE

As the technology landscape evolves, generative artificial intelligence (GenAI) emerges as a game-changer for RWE generation. It’s transforming how companies interact with Real-World Data (RWD), enabling users to "talk to their data" in a natural, conversational manner.

However, harnessing the power of GenAI isn’t just about asking questions and receiving direct answers. Foundational large language models (LLMs), while adept at natural language processing, face challenges in RWE contexts. They may suffer from inaccuracies (hallucinations) and often lack inherent understanding of:

• Data Schema: The organization and structure of datasets.
• Clinical Code Systems: Essential terminologies critical for data interpretation.
• Temporal Logic: The relationships between different data points over time.

Moreover, LLMs aren’t designed to cater to the auditing and traceability requirements that RWE demands. This highlights the need for a purpose-built GenAI solution tailored specifically for RWE generation.

Introducing RWE Agent: A New Paradigm in RWE Analysis

Deloitte, in collaboration with Amazon Web Services (AWS), has developed RWE Agent—a sophisticated conversational assistant that redefines how stakeholders interact with RWD, generate insights, and support the democratization of RWE.

Recognizing the complexity inherent in RWD and RWE, we have designed a multi-agent architecture. This innovative structure features specialized agents equipped to handle distinct tasks—such as rules, reasoning, and analytics.

Here’s how RWE Agent functions:

1. Natural Language Input: Users submit their questions in natural language, making the process intuitive.
2. Task Breakdown: A supervisor agent dissects the query into manageable components.
3. Collaborative Processing: Each part is assigned to the relevant specialized agent, which collaborates with others to complete the task comprehensively.
4. Seamless Workflow: The interaction between agents ensures a smooth and accurate response, allowing organizations to derive insights efficiently.

With RWE Agent, the goal is to simplify RWD analysis, making it accessible to all stakeholders, regardless of their technical background. This not only accelerates insights generation but also ensures that organizations can keep pace with the rapidly evolving requirements of RWE.

Conclusion

In an era where RWE is increasingly important for decision-making in biopharma, the need for innovative and effective tools is clear. While existing technologies have laid the groundwork, they must evolve to meet today’s challenges. Generative AI, exemplified by solutions like RWE Agent, offers a path forward—one that promises to democratize access to RWE and empower organizations to harness the full potential of Real-World Data. As we move forward, embracing these advancements will be crucial for organizations aiming to thrive in a data-driven future.