Towards Health Episode 5: AI and the Pharma Revolution

Historically, the pharma and biotech industries have lagged behind in adopting new technologies like machine learning and artificial intelligence (AI), even lacking in the availability of up-to-date and real-world data. Big data is one thing: combining it with tech for advancing interoperable intelligence is another.

‍A 2020 Deloitte report surveyed nearly 150 executives from pharmaceutical companies and medical device manufacturers to find out how they used AI and how they expected it to be used in the future.

‍About 60% of respondents said their companies invested more than $20 million in AI initiatives in 2019, and more than half said they expected to boost those investments by more than 50% in 2021.

‍So two years into the pandemic and after this survey, how is the industry delivering on those predictions and how is AI actually being used for precision medicine and to improve outcomes and trials?

‍In the last year alone, many pharma companies announced tech and big data investments, including third party platforms and novel data feeds to surface insights and help get drugs to market faster. 2022 is closing in so let’s see how we fared in adopting the new tech this year.

‍In this episode of the H1 Podcast, Towards Health, Ariel Katz, Co-Founder & CEO at H1 joins to discuss how AI can transform the way Pharma engages thought leaders.

‍Listen to discover:

‍Why healthcare, and more specifically Life Sciences/Pharma, tend to lag in adopting new technologies like AI.

• If the data we’re receiving now is better, or if we just have a different way to apply it via the new tech.
• H1’s recent announcements about how how they are changing the game with new AI powered capabilities within the HCP Universe product.
• The change management approach LS companies should employ to help Med Affairs and MSL move forward in this new era of AI and big data.
• If Katz thinks it will make measuring activities, ROI, metrics, and impact for Medical Affairs more efficient and effective.

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