Black Opal Ventures Founder on Building for Equity in Life Sciences Products

In episode three of this series, Dipanwita Das talks with Black Opal Ventures Founder Tara Bishop. The conversation touches on the discrepancy (sometimes) between buyers and users in life sciences, and what product people need to know about that. They also speak about equity and representation in life sciences. And the potential of data (and AI) to remedy those problems through increased personalization. Finally they discuss Tara’s venture capital mission of investing with an equity lens. Increasing the diversity of people involved in building life sciences is important. That is because, as Dipanwita says, if you’re not seated at the table — you’re on the menu.

On the buyer-user dichotomy in life sciences markets

When building products for life sciences markets, a key consideration is the discrepancy between buyers and users. Users are most interested in technical features, and whether those things will help them do their work. On the other hand, buyers are looking more at dashboards, reports, and expertise in the professional services that will make it easier for them to evaluate the business benefits of adopting that particular solution. So how does a product manager deal with this when designing products for life sciences and healthcare?

Tara says, “In a hospital system, with many of the software and the technical products that are sold to healthcare systems, the users are nurses and doctors. People on the front line who are actually doing the work. And these tools are meant to help them become more efficient or effective at their work. 

“When you think about the buyer of that product, though, it’s someone who is at the decision-making level. There has to be a value proposition. Can they actually cut costs by using this kind of product? Can they generate more revenue? And it's not always about revenue and costs and financials. It may also be, can we actually get to better care and put ourselves at less malpractice risk? Or less risk of making mistakes? Can we improve quality? There are lots of factors in there. But one of the most important is the ROI…

Users vs. decision makers

“That's the dichotomy that I see with the users versus the decision makers. And as you said, the decision makers may or may not be monitoring things on a daily basis or a weekly basis. But at the end of the day, there's some evaluation -— quarterly, annually -— where they would say, Is this product actually working? Is it generating the ROI that we expected? …

“The end user is the person who you're building the technical and services product for. But you always have to have an eye on the decision maker of that organization and the needs that they have, too.”

On the potential of data to produce more personalized healthcare outcomes

As discussed in previous conversations in this series, a major problem with any machine learning system is that it is only as good as the data inputs. Garbage in, garbage out, as the saying goes. When it comes to healthcare datasets, a common problem is that data is not representative of population diversity. So could more data be the solution? Possibly, says Tara Bishop.

Tara explains, “We know that a lot of the data that are used for guidelines, that are used in clinical trials, are not representative. They do not always include all groups. And we're potentially getting results, seeing outcomes, and [making recommendations] based on biased samples. The more and more we integrate data, the more and more risk there is that we just continue to propagate the issue of lack of representation in all things healthcare. 

“That being said, I actually think there's a real silver lining and opportunity here. When I think about clinical guidelines and the way they are structured, the ability to personalize while still being very evidence-based, is difficult to do in healthcare. And particularly if that evidence is based on biased samples, on non-representative samples, making those decisions almost feels like you're going against the grain because the guideline says something. 

Unbiased data

“If we get to a point where, in fact, we can generate more unbiased data, we can actually generate more effective and more personalized decision making. Whether it's to do a test or to do a treatment, and base it on much larger sets of data, a lot of real world evidence — data, things that are sitting in the healthcare systems and the payer systems and the pharma companies. We actually might be able to use AI to really improve and reduce the kinds of disparities that we do see in much of the trial world in healthcare. 

“So there's a potential for both opportunities. The risk of it actually getting perpetuated when datasets remain very focused on particular populations and lack of representation from other populations. But there's also potential where, in fact, we can actually get much more data and then get much more pinpointed in what people are doing and what recommendations are on a very personalized level.”

On investing with an equity lens

In the final portion of their conversation, Tara speaks about her diverse experiences as a physician, an academic, an entrepreneur, and now a venture capitalist. In this latter role, a main focus is on equity when it comes to funding portfolio companies.

“We have an equity lens in terms of our fund. We think about the success of companies. But we also think about the impact that we're having on global health outcomes. Are the kinds of problems that our portfolio companies are solving pushing the needle to improve healthcare? To improve access to care? To reduce disparities in care? These are all important things that we look at when we make a decision about an investment. 

“Investing in venture capital isn't the most representative field. There aren't very many women in it. There aren't very many women of color in it. There's a lot of momentum and work being done to increase representation in venture capital. But we also feel that that's an important area where we can have impact, whether it's our ability to sit on boards, or to tap into our networks to bring in management teams or board directors who represent different parts of our demographics.”