When it comes to the topic of drug discovery and development, scientists are busy furrowing their lab-goggled brows trying to understand what’s real and what’s hype when it comes to the power and potential of AI.
This *Resonance Test* conversation perfectly dramatizes the situation. In this episode, Emma Eng, VP of Global Data & AI, Development at Novo Nordisk, and scientist and strategist Chris Waller provide a candid view of drug development in the AI era.
“We're standing on a revolution,” says Eng, reminding us that “we've done it so many other times” with the birth of the computer and the birth of the internet. It’s prudent, she cautions, not to rush to judgement guided by either zealots or skeptics.
Waller says, of the articles about AI and leadership in *Harvard Business Review,* one could do “a search and replace ‘AI’ with any other technological change that's happened in the last 30 years. It's the same kind of trend and processes and characteristics that you need in your leadership to implement the technology appropriately to get the outcomes that you're looking for.”
Which means, for pharma, much uncertainty and much experimentation.
“I think experimentation is good,” says Eng, who then adds that we need to always keep track of what is it that we're experimenting on. She says that the word “experimentation” can “sound very fluid” but in fact, “It's a very structured process. You set up some very clear objectives and you either prove or don't prove those objectives.”
Waller references the various revolutions (throughput screening, combinational chemistry, data, and analytics revolutions) that pharma has seen and says: “We've all held out hope for each and every one of these revolutions that the drug discovery process is going to be shrunk by 50% and cost half as much. And every time we turn around, it's still 12 to 15 years, $1.5 to $2 billion.”
Will AI make the big difference, finally?
“Maybe we need to be revolutionized as an industry,” she says. “It can be hard to make much of a difference as long as there are few big players.” Just a few big players, she says, is “the nature of pharma.”
Of course, our scientists are measured in their assessments about industry change. After all, as Waller says, the systems involved—the human body, the regulatory environment, the commercial ecosystems—are all “super-complicated.”
Eng notes that an important side-effect around the AI hype is corporate interest in data. “Now it's much easier to put that topic on the table saying, ‘If you want to do AI, you need to take care of your data and you need to treat it like an asset.’”
Listen on as they test topics such as regional and regulatory challenges in AI adoption, change management, and future tech and long-term impact (watch out for quantum, everyone!).
In the end, Eng returns to the idea of revolutions. “You think you want so much change in the beginning which you don't get because it takes time,” says Eng. This makes us underestimate what will happen later. Having such a farseeing mindset is significant, she says, because “these technology shifts will have a large impact on the long term.”
Host: Alison Kotin
Engineer: Kyp Pilalas
Producer: Ken Gordon
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When it comes to the topic of drug discovery and development, scientists are busy furrowing their lab-goggled brows trying to understand what’s real and what’s hype when it comes to the power and potential of AI.
This *Resonance Test* conversation perfectly dramatizes the situation. In this episode, Emma Eng, VP of Global Data & AI, Development at Novo Nordisk, and scientist and strategist Chris Waller provide a candid view of drug development in the AI era.
“We're standing on a revolution,” says Eng, reminding us that “we've done it so many other times” with the birth of the computer and the birth of the internet. It’s prudent, she cautions, not to rush to judgement guided by either zealots or skeptics.
Waller says, of the articles about AI and leadership in *Harvard Business Review,* one could do “a search and replace ‘AI’ with any other technological change that's happened in the last 30 years. It's the same kind of trend and processes and characteristics that you need in your leadership to implement the technology appropriately to get the outcomes that you're looking for.”
Which means, for pharma, much uncertainty and much experimentation.
“I think experimentation is good,” says Eng, who then adds that we need to always keep track of what is it that we're experimenting on. She says that the word “experimentation” can “sound very fluid” but in fact, “It's a very structured process. You set up some very clear objectives and you either prove or don't prove those objectives.”
Waller references the various revolutions (throughput screening, combinational chemistry, data, and analytics revolutions) that pharma has seen and says: “We've all held out hope for each and every one of these revolutions that the drug discovery process is going to be shrunk by 50% and cost half as much. And every time we turn around, it's still 12 to 15 years, $1.5 to $2 billion.”
Will AI make the big difference, finally?
“Maybe we need to be revolutionized as an industry,” she says. “It can be hard to make much of a difference as long as there are few big players.” Just a few big players, she says, is “the nature of pharma.”
Of course, our scientists are measured in their assessments about industry change. After all, as Waller says, the systems involved—the human body, the regulatory environment, the commercial ecosystems—are all “super-complicated.”
Eng notes that an important side-effect around the AI hype is corporate interest in data. “Now it's much easier to put that topic on the table saying, ‘If you want to do AI, you need to take care of your data and you need to treat it like an asset.’”
Listen on as they test topics such as regional and regulatory challenges in AI adoption, change management, and future tech and long-term impact (watch out for quantum, everyone!).
In the end, Eng returns to the idea of revolutions. “You think you want so much change in the beginning which you don't get because it takes time,” says Eng. This makes us underestimate what will happen later. Having such a farseeing mindset is significant, she says, because “these technology shifts will have a large impact on the long term.”
Host: Alison Kotin
Engineer: Kyp Pilalas
Producer: Ken Gordon
The Resonance Test 87: Health Equity Solutions with Dr. Djinge Lindsay & Arianne Graham
The EPAM Continuum Podcast Network
31 minutes 49 seconds
2 years ago
The Resonance Test 87: Health Equity Solutions with Dr. Djinge Lindsay & Arianne Graham
Equity is not easy. It’s tough enough to talk honestly about equity in US healthcare, and troublingly difficult to create solutions that can, and will, be implemented.
But trouble—good trouble, as Representative John Lewis once put it—is precisely what interests the people on the latest episode of *The Resonance Test.*
Dr. Djinge Lindsay, MD, MPH, and Arianne Graham are here to talk about their stalwart attempts to solve for equity. Johnathon Swersey, Senior Director of Innovation at EPAM, creates all kinds of necessary trouble by putting tough questions to our two guests.
Swersey begins by addressing the idea that given the less-than-optimal state of US healthcare, why are we focusing on equity?
“If we don't fix the health disparities in our country, those will spread to the rest of the population and it becomes not just a public health issue, but frankly, a national security issue,” says Graham.
Or conversationalists agree that there has been much talk about equity but that talk is far too insufficient.
“There has been a lot of verbal acknowledgement of the fact that structural racism is pervasive and all of our systems, including healthcare, but that verbal acknowledgement hasn't necessarily been followed with action,” says Dr. Lindsay.
To move things forward, we need to align our moral imperatives with proper levels of funding. Dr. Lindsay asks us to consider the history of social change: “There have been few successful social change movements that haven't been aligned with some financial incentive for those who hold power.”
One such alignment must just be possible with the new Health Equity Index that's being introduced to Medicare Advantage. Healthcare players, says Dr. Lindsay, will now be required to not only “look at their data, not only stratify their data but to action on closing disparity gaps. Closing differences in health outcomes for people based on their race.”
Ultimately, it’s about beginning from what Graham calls “an asset- as opposed to a deficit-based approach.” She says that understanding the history, pain, and ramifications of structural racism is important but also “there is space to celebrate what makes different cultures unique and use that as a leverage point to engage folks in conversation about health. To tap into the trusted messengers, trusted leaders, and representative figures within communities that are delivering messages of self-actualization and self-efficacy in managing our health.”
Let’s hope we can manage to take a step in a healthier direction. Begin by listening.
Host: Alison Kotin
Engineer: Kyp Pilalas
Executive Producer: Ken Gordon
The EPAM Continuum Podcast Network
When it comes to the topic of drug discovery and development, scientists are busy furrowing their lab-goggled brows trying to understand what’s real and what’s hype when it comes to the power and potential of AI.
This *Resonance Test* conversation perfectly dramatizes the situation. In this episode, Emma Eng, VP of Global Data & AI, Development at Novo Nordisk, and scientist and strategist Chris Waller provide a candid view of drug development in the AI era.
“We're standing on a revolution,” says Eng, reminding us that “we've done it so many other times” with the birth of the computer and the birth of the internet. It’s prudent, she cautions, not to rush to judgement guided by either zealots or skeptics.
Waller says, of the articles about AI and leadership in *Harvard Business Review,* one could do “a search and replace ‘AI’ with any other technological change that's happened in the last 30 years. It's the same kind of trend and processes and characteristics that you need in your leadership to implement the technology appropriately to get the outcomes that you're looking for.”
Which means, for pharma, much uncertainty and much experimentation.
“I think experimentation is good,” says Eng, who then adds that we need to always keep track of what is it that we're experimenting on. She says that the word “experimentation” can “sound very fluid” but in fact, “It's a very structured process. You set up some very clear objectives and you either prove or don't prove those objectives.”
Waller references the various revolutions (throughput screening, combinational chemistry, data, and analytics revolutions) that pharma has seen and says: “We've all held out hope for each and every one of these revolutions that the drug discovery process is going to be shrunk by 50% and cost half as much. And every time we turn around, it's still 12 to 15 years, $1.5 to $2 billion.”
Will AI make the big difference, finally?
“Maybe we need to be revolutionized as an industry,” she says. “It can be hard to make much of a difference as long as there are few big players.” Just a few big players, she says, is “the nature of pharma.”
Of course, our scientists are measured in their assessments about industry change. After all, as Waller says, the systems involved—the human body, the regulatory environment, the commercial ecosystems—are all “super-complicated.”
Eng notes that an important side-effect around the AI hype is corporate interest in data. “Now it's much easier to put that topic on the table saying, ‘If you want to do AI, you need to take care of your data and you need to treat it like an asset.’”
Listen on as they test topics such as regional and regulatory challenges in AI adoption, change management, and future tech and long-term impact (watch out for quantum, everyone!).
In the end, Eng returns to the idea of revolutions. “You think you want so much change in the beginning which you don't get because it takes time,” says Eng. This makes us underestimate what will happen later. Having such a farseeing mindset is significant, she says, because “these technology shifts will have a large impact on the long term.”
Host: Alison Kotin
Engineer: Kyp Pilalas
Producer: Ken Gordon
