Fall 2021 | Boston, MA
This interactive discussion featured Harry Glorikian, Chair of AI in Biopharma Summit and author of MoneyBall Medicine joined by Ülo Palm, Thomas Bock, and Lixia Wang, who were open to discussion and debate about topics including:
• The definition of RWE
• Examples of companies that are effectively working with biopharma to ensure that RWD is thoughtfully collected and analyzed to turn it into a powerful tool
• The ability of RWE to predict future pandemics
• Challenges to adoption
• Privacy concerns
• The potential over reliance on p-values
• RCT’s (random clinical trials) limitations on drug efficacy in big populations
• Working with regulators
Below you’ll find selected talking points, information shared and the recording to watch the full webinar.A productive community has several key components: a shared purpose, a willingness to assist others in the group, a gathering space to learn from each other, and an ability to move forward as a group toward a common goal. On Friday, May 15, 2020 the AI Applications in Biopharma Summit community came together in a virtual space to engage in a discussion about how best to leverage RWE and advanced analytics for drug development in a pandemic and post-pandemic.
Each speaker generously gave their time in preparing for the webinar and shared their thought leadership by providing specific examples of effective uses of RWE, in combination with AI, to manage the quantity of data available during a pandemic to meet the need for speed to develop therapeutics.
Key takeaways:Note: These are summaries of what speakers discussed and not verbatim comments.
Defining RWE and How it Can Be UsedÜlo: RWE is derived from RWD – data collected in a natural setting. This data is becoming more important to payers. They want this data because they are not always sure that the results of randomized clinical trials are always applicable to the broader population.
Lixia: Using RWD is not a new concept and has been used to evaluate the patient journey and to formulate health economic models and social impact. With digital health becoming a reality, RWD has exploded and has now become big data. This is now the priority for everyone as evidenced by the framework established by the FDA that asks pharma companies to use RWE as a supplement, or in a situation where a RCT is not feasible, RWE should be used to support the drug development decision-making process.
Thomas: COVID-19 is putting RWD front and center because of the amount of data out there and the need for speed to develop therapeutics. If RWE is thoughtfully collected and analyzed, it can be extremely powerful.
BlueDot’s outbreak risk software safeguards lives by mitigating exposure to infectious diseases that threaten human health, security, and prosperity.
Use case example: predictive AI paired with RWE in a Virtual Clinical Trial. Biovista conducted in effect a Virtual Clinical Trial with the FDA and a large EHR system in 2015 validating its hypothesis with RWE potentially saving $8B/year in avoiding hypothyroid patients getting diabetes from taking statins, published in ADA’s Diabetes Care.
With Explainable AI, Epistemic AI is working on making it effortless to connect knowledge and formulate hypotheses.
Used during COVID-19 when patients in their 30s and 40s, who did not have pre-existing conditions, were arriving at Mt. Sinai Hospital with strokes and heart attacks. The critical and immediate need was to determine if this was an observation at just one hospital or if it was more widespread. TriNetX has access to the RWD (through EHRs) of over 100m patients and thousands of COVID-19 patients. They were able to review this data and could see that these symptoms were happening all over the place, not just at Mt. Sinai, and this was a widespread risk even for healthy people in their 30s and 40s.