A Collaborative Approach to Incorporating AI in RWE

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Summary

Stakeholders can collaborate to ensure the safe, ethical, and effective use of AI-based tools to bolster RWE and improve patient outcomes.

Artificial intelligence (AI) has the potential to significantly improve patient outcomes. The use of AI and machine learning allow practitioners to collect large data sets and pull meaningful insights from real-world evidence (RWE) efficiently. Additionally, researchers and healthcare providers can identify patterns and trends in real-world data that can inform clinical decision making, optimize drug development, and accelerate clinical trial design. It is not surprising, then, to know that regulatory agencies are exploring ways to incorporate RWE into their decision-making processes. 

However, the wide application of AI in healthcare raises questions about trust, privacy, and safety. How do we make sure that the data used for analyses are accurate and fit for purpose? Does AI reflect new or different biases compared to conventional methods? Can trial results data be reproduced without compromising privacy? Lastly, are the standards applied in the US consistent with those used in Europe? Until these challenges are addressed, adoption of AI in RWE will falter. 

Like any fast-evolving technology, regulations and standards lag significantly behind adoption of AI and how it is used in the life sciences industry. Medical organizations, professional societies, and technology groups all have created their own interpretations of AI-enabled tools, and every organization utilizes its own approach.   

One solution to this conundrum could be shared responsibility—stakeholders collaborating to address the challenges to support transparency in how and where data are collected, analyzed, and interpreted. Having a unified set of standards could mitigate potential bias and improve the use of RWE, accelerating the delivery of innovative therapies to patients who need them. 

Consultancies with expertise in federal and state policies, market access, and data strategies can serve as the bridge to connect manufacturers, plans, and providers to AI-enabled RWE. Experts in building interdisciplinary teams can encourage members with differing perspectives to come together for a common goal and fit-for-purpose use. To achieve the promise of AI improving health and patient outcomes, healthcare organizations will need to ensure the safe, ethical, and effective use of AI-based tools. 

To learn more about the role of AI in RWE and Avalere’s work supporting healthcare stakeholders interested in exploring and harnessing the potential of AI, connect with us.

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