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Daiichi Sankyo’s Head of Global MSL Excellence on Improving Data, Equity, and Tech Partnerships in Life Sciences

This is the first conversation in Sorcero’s podcast series on building great AI-powered life sciences products. Here, Sorcero CEO Dipanwita Das speaks with Donna Holder, Head of Global MSL Excellence at Daiichi Sankyo. They begin with the question of what MSLs do, exactly, and why they are so important in the emerging field of life sciences products.

MSLs (medical science liaisons) are highly technical individuals who work in the field with researchers and healthcare providers. They usually comprise the biggest part of medical affairs teams. MSLs play a critical role in representing companies with physicians and gaining insights around unmet needs. In her role, Donna works with the leaders of medical affairs organizations within Daiichi Sankyo. This includes region heads and MSL leads to uplift MSL capability. 

First, Donna speaks to the importance of raw data and the fact that algorithmic solutions are only as good as the information they are fed. Second, Donna addresses bias and equity, and the efforts that the industry is making in designing more diverse equitable clinical trials. And third, Donna urges the importance of partnerships and the idea of co-creating life science product solutions. Life sciences is a changing and emerging field. Nobody has all the answers — and that's a big part of what makes it exciting.

On the importance of knowing your audience

Any product manager understands the importance of knowing the customer. And one of the main customers for the medical affairs function is the healthcare provider. 

When Donna began working as a medical science liaison (MSL), her team had limited information about healthcare providers. Now there are tools providing profiles and other data to help them have a rich conversation with their customers. 

Another set of useful tools is in the area of insights. We hear this word all the time. But insights are only as useful as they can be tracked and understood, or as the patterns and trends can be extracted. Previously, Donna and her colleagues would do this manually. But that is not just slow, but can leave out important information. Tools are now being designed to surface those trends and patterns early on and help Donna and her team make excellent decisions.

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.”

On ethics, artificial intelligence, and equity

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.

As Donna says, “We need to help feed the AI so that it starts to recognize the patterns and it builds on that. And so I want to understand better how we don't create bias in the algorithms as a pharmaceutical company. So that it is always pointing to our product [as] the best one. … MSLs, while they do have those deep conversations, they are regulated. That's why it's important for medical affairs teams to work with a partner, or an external partner, in building these tools. To make sure that we don't have that bias and really understand how to do that. 


“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 incented 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."

On the importance of partnerships in biotech

In life sciences, partnerships are truly important. Tech companies are working on novel solutions to novel problems in a changing market. Consequently, it is essential to have input from multiple perspectives. The best case scenario is for tech, pharmaceutical, and medical teams to work together and co-create product solutions together.

Donna says that, above all, tech companies committed to transforming healthcare and patient outcomes must listen. “Listen to the pharmaceutical company,” she says, “what's important and what their needs are. And really understand the roles and what it is that the medical teams do. If [tech companies are] trying to push their own agenda, it can work in the beginning. I hear [about] all of these fancy tools, and it sounds great. But once you start to scratch the surface, that's when you really need to partner. And deliver and develop something that is going to work, [and] that's going to be compliant. 

“So I'd say that that 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.”