RWD and Impact of Using AI
Date Submitted: 24 August 2023
Strategic Priority: Harnessing AI to Expedite R&D
The increasing importance of RWD and the impact of using AI
Regulators, payers, patients and researchers have an increasing interest in the use of real world data and evidence in order to:
- Accelerate drug development innovation,
- Better incorporate the voice of the patient in clinical drug research and development,
- Reduce timelines and cost
Consistent within the direction and intent of the 21st Century Cures Act, the FDA has published guidance for the use of real-world evidence (RWE) in regulatory decision making. This guidance focuses on the use of RWE to support approval of a new indication for an already approved drug and to help support post approval study requirements in order to accelerate the overall approval process.
In addition, regulators and pharmaceutical researchers want to drive more patient focused drug development and are eager to understand how and what types of real world data can best support this goal and drive innovation consistent with patient benefit.
Idea Proposal and Value Proposition
Opportunities in the Pharma industry
As stakeholders are looking to diversify sources of RWD and to understand how best to leverage a wide variety of real world data types, many are wondering if artificial intelligence (AI) can successfully:
- Collect and integrate data from various sources like electronic health records, wearable devices and genetic information data sources,
- Predict patient outcomes, identify potential AES and design more efficient clinical trial,
- Identify patient sub-groups with specific characteristics or responses to treatment that could enable more personalized medicine and targeted drug development,
- Identify new indications for already approved drugs,
- Improve quality and consistency of real world data improving reliability of research outcomes
- Accelerate discovery of new drug candidates.
Challenges/questions to be addressed
While the promise of the use of real world data in combination with AI technologies is exciting, the use of AI is still not well understood and may be negatively perceived by citizens and patients. As a result it is essential that we explore patients’ and non-patients’ perceptions of the use of AI in combinations with real world data. Questions that we believe need to be answered in order to understand the concerns, to build trust in the use of this type of data and technology, and to foster collaboration within the patient communities include:
- How would a patient feel about data sharing?
- Would they find it intrusive or dangerous?
- Are they supportive of the use of AI in research and clinical development? If not, what are the concerns?
- What are perspectives from people working in the research field vs those of patients?
- What ethical, legal, and regulatory considerations need to be made?
- How do we use human-centric evidence to achieve right target, right molecule, right patient?
Proposed Goal of the Pistoia Alliance project
Provide overview and recent advances of use of AI in healthcare, evaluate the perception from patient groups and patient experts, provide insights into several use cases of AI in leveraging RWD, deep dive into open challenges and describe possible solutions including oversight of upcoming legal frameworks on use of AI in Healthcare industry.
Project scope considerations:
- Clinical Development
- Exclude Medical Affairs