It's time to kick off the launch of Sorcero’s miniseries on building great AI products in life sciences, an all-new Mighty Capital podcast.
In this Sorcero miniseries, join host Sorcero CEO and Co-founder Dipanwita Das as she sits down with industry experts and business leaders to discuss how to build great AI-powered products for Life Sciences.
Sorcero Series Episode 1: Daiichi Sankyo’s Head of Global MSL Excellence on Improving Data, Equity, and Tech Partnerships in Life Sciences
This is the first episode in the Sorcero miniseries on building great AI-powered life sciences products, a Mighty Capital podcast.
With 30 years of experience in the pharmaceutical industry and a wealth of experience in Medical Affairs, Donna is accountable for ensuring the strategic alignment of Medical Science Liaison (MSL) activities across Daiichi Sankyo's regions and for strengthening the MSL capability worldwide.
Listen to the podcast on Apple, Amazon, and Spotify to learn more about what it is that Medical Science Liaisons (MSLs) do and why they are so important in the emerging field of life sciences products.
In this episode, Dipanwita Das and Donna Holder tackle:
1. The importance of knowing your audience
Building lasting relationships with health care providers (HCPs) is one of the foundations to delivering value as an MSL. In this conversation, Donna shares the evolution of how these MSL/HCP relationships were established in the past, and how new analytics and insights tools are helping to drive these relationships forward.
On Profiling tools
Donna says, “One of the areas that we've seen a rapid growth, and that we're starting to use, is the ability to profile the individuals that we're meeting with. There's a number of different profiling tools. In the past, we would go to Google. We would ask through our networks. We would look on the internet to understand who we are seeing and what's important to them.
“Now with the depth of information that we have, we have a better understanding. Through claims data, through new types of data. We see what's important to [the healthcare providers]: who they're seeing, what type of patient populations they have, what level of trials they're involved in. So we have much deeper information, which will really help us provide that contextualization…
“Moving forward, we need even more tools. Those are going to help us define and bring in insights that will get deeper into what's important for them. And then also bring back to the organization insights so that we can uncover some of those unmet needs. And those tools today, we're doing a lot of that [manually]. But if we have tools that can rapidly identify insights and patterns that we might not be able to see manually, or that are taking up a lot of time, it's going to be so useful. Then we can contextualize and bring value to what's most important for them and their patients.”
2. Ethics, artificial intelligence, and equity
Ethical AI is essential to health equity, but what does this look like in practice?
The quality of an algorithm — what it does and does not, what it suggests and does not — is entirely informed by what it has been fed. To use a colloquial phrase: garbage in, garbage out. Thus, product leaders in the life sciences space have a huge role in informing the end result and efficacy of any AI or machine learning.
“When it comes to equity, something that I think has been a big part of the conversation with our R&D organization, is diversity in clinical trials. So ensuring that we've got diverse patient populations, and that we're going to sites that have underserved communities. That have diverse patient populations. That are geographically distributed. And doing that, a lot of times our R&D organization is incentivized to get trials done. We want to get our products out to market as quick as possible. So going to sites that we know will produce good quality results is important.
“But now [we’re] able to use data to understand: What are the patient populations the sites are using? Where do we need to ensure that we're getting data from all the types of patient populations? How to make sure that they're represented? So that we get data that can be representative of the patients who will be using our products."
Hear from Dipanwita and Donna on the importance of data quality in artificial intelligence and how technology can be used to address diversity in clinical trials and other aspects of health equity.
3. The value of partnerships in biotech
"Partnership — and I keep saying that over and over — is incredibly important. And it's going to be important for them to have some knowledge, and I would say ensure that they've got people from the industry on their teams. If I work with a team that doesn't have anybody that has a [relevant] perspective, that's walked in [those] shoes, I don't want to spend a lot of time educating them," shares Donna.
What's the most essential ingredient that enables tech, pharmaceutical, and medical teams to work together to create product solutions? Hear what Dipanwita and Donna have to say by listening to the full episode on Apple, Amazon, or Spotify.
About the Podcast
The Mighty Capital Podcast features interviews with industry experts and business leaders across technology, product and venture capital.
This Sorcero miniseries is hosted by CEO and Co-founder Dipanwita Das and centers on how to build great AI-powered products for Life Sciences.
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