Episode Thumbnail
Episode 1  |  29:42 min

How AI Drives Innovation for the Life Sciences Industry (with Derek Choy)

Episode 1  |  29:42 min  |  06.17.2020

How AI Drives Innovation for the Life Sciences Industry (with Derek Choy)

00:00
00:00
This is a podcast episode titled, How AI Drives Innovation for the Life Sciences Industry (with Derek Choy). The summary for this episode is: <p>In this episode, we’ll be discussing&nbsp;<a href="http://www.aktana.com/ci" rel="noopener noreferrer" target="_blank" style="background-color: rgb(255, 255, 255); color: rgb(2, 128, 254);">Contextual Intelligence</a>&nbsp;and how it has been applied to one of the most complex industries in the world, Life Sciences.</p>
Takeaway 1 | 02:52 MIN
Derek Choy shares how early missteps actually led to a greater understanding of the importance of context for all areas of the business
Takeaway 2 | 04:11 MIN
We cover the importance of incorporating human experience and data when creating tools for sales reps
Takeaway 3 | 03:52 MIN
We also chat through the considerations companies need to make when transitioning from a legacy commercial model to one that is data-powered and the pitfalls of thinking that AI alone can deliver what’s needed
Takeaway 4 | 01:50 MIN
Plus, we learn a bit about Derek the person, including his secret passion for stage magic

In this episode, we’ll be discussing Contextual Intelligence and how it has been applied to one of the most complex industries in the world, Life Sciences.

Guest Thumbnail
Derek Choy
Co-Founder and Chief Operating Officer
Derek Choy spent the last decade developing and refining Aktana’s product, evangelizing the brand's vision in the life sciences industry, and delivering product and services across four continents. Derek is responsible for strategic definition and scalable execution and he oversees strategy, product, customer success, and services.
Derek's Linkedin

Clay Hausmann:
Hi, I’m Clay Hausmann, CMO of Aktana, and the host of this podcast, Contextual Intelligence. So in a world infatuated with artificial intelligence, we will be talking about the element that doesn’t get a lot of attention but matters, in some ways, most of all, and that is context. Contextual Intelligence can be defined as taking what you learn and making it work from one situation to another. In our world of life sciences, the emphasis on Contextual Intelligence is even more important. This is an industry where commercial efforts can vary based on so many factors from data availability, legacy tech systems, team behaviors, regulatory requirements, therapeutic area specifics, any number of variables. And the intelligence effort that works best will take all of that and more into context.
My first guest to help us unravel this topic is my colleague Aktana Co-founder and COO, Derek Choy. To give you a little background about Derek, he is simply one of the hardest working and most intelligent people I’ve ever worked with in my career. He has got a kindness streak that is both motivating and a little intimidating at the same time, because it is always there. And he is a surprisingly talented and efficient PowerPoint designer, if the Aktana founder thing doesn’t entirely pan out.
So Derek, now that I have pumped you up appropriately so, let me start by knocking you down a peg or two. Why don’t you start by telling me about how Aktana, in the very early days, when it was just you and a couple others, failed miserably and what you learned from that.
Derek Choy:
Sure. Thanks very much Clay for that intro. And yeah, it’s true. Like a lot of early stage companies, Aktana definitely went through our share of challenges in the early days, but I think there is one example that particularly stands out. It was back in 2011 and our first engagement with a large pharma company in Japan. We weren’t familiar with the pharma industry at all, or the rep workflows. We didn’t have any experience working in Japan and we didn’t even really speak or understand Japanese. And so in retrospect, it’s unsurprising that this first endeavor failed. But I do remember sitting in the room of Japanese medical sales representatives after we’d spent, I think, 12 months of blood, sweat, and capital into developing and piloting this solution that we had to help them optimize their day to day activities using intelligence. And I remember after about 30 minutes of pleasantries and silence, one rep actually finally stood up and admitted this tool that we had built wasn’t valuable for them at all and they hated using it. And then the flood gates opened and everyone was kind of complaining.
I think, the problem was that we spent so much time optimizing for what the brand and the sales operations teams wanted and using models and analytics to identify theoretically optimal priorities, but we didn’t spend enough time paying attention to the context of the people whose workflows we were affecting. So we were ignoring their day-to-day realities, the constraints and their preferences. And if I think back to it, I remember that realization and how tough it was on our team, but also believing that we’d identified still a real industry challenge that needed solving. It was just really difficult. And so we convinced our investors and our customer list, try again. We went back to the drawing board. We brought together a group of pharma reps who they actually became part of our advisory board. And we worked with them to develop an approach for how we would deliver intelligence to the field.
It was designed by them and it was built for them. And it really had at the heart of it, the need to incorporate their context and make sure that we had their input from day one. So we had their buy-in. It also equally focused on change management and adoption and what it meant to be really valuable and meet their needs.
This experience definitely led through a breakthrough innovation for the company, and it really has become a core to our success, this balancing of human and machine intelligence. As we’ve deployed this now, the same concept, and we’ve taken it and we’ve productized it and we’ve got as part of our solution, we now see this concept of intelligent engagement that we kind of pioneered in those days and that we learned coming out of that failure, be a strategic priority now for every major pharma company and many of those best practices that initially came out of that first failed implementation. We now see as best practices used in the industry as a whole.
Clay Hausmann:
Great. That’s fascinating and I think probably pretty common for a lot of companies in their early stages. It doesn’t get talked about a lot, but I know we talk about it a good amount. What did that teach you about the importance of context, especially as topics like AI and machine learning became more prevalent with Aktana’s customers?
Derek Choy:
Yeah, it’s a really good question. I think it really taught us that for AI and machine learning to be impactful, when you apply it to a very specific business problem, you really have to consider every relevant factor to that business problem. So whether it’s the company factors, the industry factors, human factors, all of those things make up the context that decision makers and the process that we’re trying to influence need to think about. And so if you don’t consider them, then the output that you come up with, it’s just not going to feel right, it’s not going to be adopted and it’s not going to have impact.
So I think when it comes to our approach and our technology, we’ve learned that even though analytics and machine learning is important, by itself is not enough. You really need a combination of things to really drive successful impact through intelligence.
For example, we realized that business logic and business rules are really critical. You have to be able to capture and reflect the expert knowledge from the brand teams and also the best practices from the best reps and use that context to seed the intelligence, to set the constraints that you want the solution to operate within, and also to drive short-term impact when maybe the data that you have, you don’t have enough of it to make good predictions with machine learning yet. But machine learning also has an important role to play because you have to identify patterns in the data and predict outcomes. Like when physicians are most likely to engage with us, or maybe when reps are most likely to be close to a physician and what their routes look like. That sort of context is needed so that whatever your intelligence is, it feels right to both the representative if you’re making suggestions to them or directly to HCPs that you engage with them in other channels.
Optimization is also something we learned is really key because both reps and physicians have limited bandwidth, they have limited attention span, they have real-world constraints that they’re dealing with. So whatever you do, you really have to prioritize only the most important recommendations and make it simple for them. And I think the other thing we really learned was the importance of making things explainable, because if you’re going to influence a decision maker, what we really learned is you have to help them understand why if we give them all the data and the context they need, so they make the right decision, which might not be exactly what you’re recommending. And I think those are some key lessons and some components that really came out of that experience, but also what we’ve learned since.
Clay Hausmann:
So Derek, I want to dig into that a little bit for a minute. So you mentioned making it feel right and making it explainable. There’s a long history of technology tools that get rolled out to sales reps and never get adopted because either it’s a cynical audience that feels like they know their environment the best and the IT department keeps giving them new shiny tools, one after the next, or they don’t have the right kind of user experience or they don’t respect the knowledge of that user well enough. So I know that’s been a key element for Aktana. What are some of the things that Aktana has been able to put into that key delivery point in terms of making a suggestion feel very relevant and very credible, as you think about what is typically a solution that’s heavily influenced by technology and doesn’t have that kind of empathy to it? How is Aktana able to build that sensitivity into what is put in front of the sales rep?
Derek Choy:
Yeah, it’s a great question. There’s a couple of elements. One of them is actually captured in the expert knowledge and executed through the business rules. So one thing for example is, as we set up our system and the intelligence, we make sure that we bring in the best reps to really understand what are the best practices that they are doing and how are they thinking. And if you use some of that to kind of seed the engine, then you can make sure that what comes out is always going to be, at the least, be framed and constrained by things that representatives believe are important because they see their peers and their best reps are doing those things. So that’s one aspect of things is making sure the system is seated with that right team intelligence and that right experience.
The second thing I think is how you would use machine learning to even, and this is something interesting that we do, to make sure that execution does feel right at the end of the day. This is considering things like the practicality of, if you’re a sales rep, often you’re in the field and you have a schedule that you’re following, and as a result, you have a territory that you have to navigate. There may be days when you are going to be close to a particular customer and days when they’re further away. If we’re making suggestions purely based on priority, you might be suggesting, to see somebody that’s just out of your route today. And so being able to use technology like machine learning to predict where a rep is going to be, if they haven’t entered it into the system, which is a common thing.
You expect sometimes, you hope that a representative is going to enter their whole schedule into a system. But the reality is we don’t do that as people. Sometimes you add some things, sometimes you miss some things and if you can use technology like machine learning to make predictions and fill in gaps and make things feel right, that’s another example.
The third example is something which I think is really interesting in what we do. It’s the idea that if you’re going to give somebody a recommendation, you can use machine learning to predict whether they’re going to adopt it or not, because you’ve got historical behavior you can see around whether you’ve given suggestions or recommendations like this in the past, and kind of what’s similar to the recommendations you’re making now, and what’s the adoption level been for those things. And this is interesting because if you give a representative a theoretically optimal recommendation that they don’t take and they don’t take that multiple times, if you give that to them the fifth and the sixth time, they’re probably not going to take it.
And so instead of giving them that theoretically optimal thing, what if you gave them something that was maybe fifth or sixth in the priority list, which is still really important because it’s out of a hundred different things they could potentially do. And that thing happens to be the one that they would engage with more because we’ve seen historically that they tend to believe and see that this is important. And then they start seeing those. They adopt those. And as a result, they get more buy-in into the system. It feels more right to them. And over time you start introducing some of the more theoretically optimal ones. That’s another example where using machine learning for change management and for adoption itself.
Clay Hausmann:
Well, if we think about, out in the field, if we think about one of the other essential actors in this process, it’s the brand manager or the marketer. And what’s most important to them is that the brand strategy is incorporated. That context is driving everything. And that’s going to change obviously from project to project every single time. So how do you address that side of it to make sure that that need, that critical need is addressed for the brand side of the exercising commercial?
Derek Choy:
Yeah. It’s key because the field is only one part of what we’re trying to optimize and really it’s the brand that’s coming up with what role the field should play as part of a omnichannel journey and experience for our customer. And so I think it’s the ability to make sure that, going back to those business rules, you can take the brand strategy and codify it has tactics connect it to data within a configuration that represents the strategy. And you can do that at scale and uniquely for every brand that exists. Making sure you can really codify that allows to make sure that that context is appropriately there so the experience is right for the customer at the end of the day.
And then of course, you need to then be able to have that transparency back so that once you’ve got this plan being able to be executed through the field and through other channels, you can monitor and see what’s working and what isn’t. And then you can learn what needs to improve by having that right transparency into the system, the right reports, the right business intelligence.
Clay Hausmann:
So it’s been about 10 years since that story you were telling about early days of missteps. What have you seen, maybe on both sides of it, what has changed over those 10 years the most, and maybe what has surprised you a bit that it really hasn’t changed much at all with regard to the dialogue with customers and what they’re trying to accomplish with this sort of approach?
Derek Choy:
We have been fortunate over the last 10 years because we’ve been working with top life science companies now in every major region and supporting over 250 brands. I think as we have developed our perspective on intelligence and we’ve worked with all those brands in different countries, there’s a couple of interesting things that have changed. It’s changed in our thinking, I would say. I would not say it’s necessarily changed in the industry, but the way we’ve approached it has changed. I think what we’ve started to realize is we can do all the things we mentioned earlier. You can have the right technology components like business logic, machine learning, optimization, explainability, but actually it’s even more critical or just as critical to have the right team experience from our customers to be involved in the engagements. Because early on, when you think about that team intelligence and the experience, and you focus that on codifying business rules so that the brand component is included in the field, like best practice are included, that’s important.
But what’s even more important is actually the expertise in terms of managing an ongoing program, in terms of evolving the program, in terms of all the connected pieces within a commercial organization that need to transform to be able to let this happen at scale.
And one thing we’ve also experienced and learned is that the Aktana team’s experience that we’ve had over the last couple of years, supporting our customers is really critical to be able to support this scale. It’s kind of the lessons learned. It’s the best practices that now we’ve codified and templatized into tools and we can roll out for our customers. Those things are really critical.
On the other side, when I think back to the vision that we were evangelizing back in the early days, it was all about making data and analytics actionable for a field sales rep and helping them personalize experience for physicians. But then also starting to transition to a commercial model, which leveraged the right channels, at the right time, with the right message. And interestingly, at that time, when we were evangelizing this vision, if you talk to an executive, all of them would agree and say, “This is the direction we need to move as an industry.” And that really hasn’t changed. The fact that everyone believes that it’s a direction we need to move as an industry.
What has changed, however, I think is the readiness of life science companies to start to make that vision the reality. Because not only now is our platform mature enough to make sure we can help our customers make that transition at scale, but the companies themselves now have the right data and analytics infrastructure in place, they’re starting to turn on the right digital and non-personal execution channels at scale. And so that transition from a purely field focused, face-to-face mode, to a mix between face-to-face and digital has become more important, especially given recent times with COVID-19 and the shift to this new mixed model.
Clay Hausmann:
Interesting. So I think you touched on some of these things a little bit, but I want to revisit them in a little bit more focus. When companies are considering or rolling out AI, what do you think is the part that they understand or appreciate the least in terms of what’s going to affect success?
Derek Choy:
Yeah. I think, it’s very easy to underestimate how difficult it is to make AI both impactful and scalable at the same time. And it can easily become a trade-off if you don’t use the right approach upfront and you don’t build a solid foundation. We sometimes see companies use machine learning to build predictive next best action models. And typically they invest a lot of time and effort into preparing the data, refining the model, and then they spend less time thinking about how to deploy the output of those models to the field or to various other multichannel actions that they want to happen. Maybe they just do it as a simple trigger or they display a score within the CRM.
What we see is that most companies then would see that that’s not that impactful because, number one, the models themselves are not reflecting a lot of the context and the workflow for the end user. But just as importantly, some of those models, the analytic outputs, they kind of quickly get out of date because the process of developing and refreshing those models and then the process of getting them to production and getting them into the workflow is not scalable.
And so I think people sometimes underestimate how important it is to be able to make sure that you are building analytics and machine learning on the same data set, using the same standardized factors and considerations that you’re going to be testing and deploying those models on. And incorporating the right other elements of the business rules, the kind of optimizations so that it is impactful. And then also focusing on making sure it’s scalable and it’s sustainable. And I think, we are now starting to learn that, as we do this with our customers, thinking about all that upfront and investing in a platform and kind of an approach that considers that all the way from day one is critical to allowing whatever you invest in upfront to then be able to be used on an ongoing basis.
Clay Hausmann:
So this may be the flip side to that question a little bit, but are there certain characteristics that you look for in early conversations with life science companies about their teams or their process where, when you see them, you go, okay, these guys, they already have a headstart in achieving success here as they try to move from a legacy commercial model to one that’s driven and powered by AI and data. And obviously, teams and processes, it takes a while to change your processes. It takes a while to go out and hire the talent to makeover your team with new skills. So what are the characteristics that, when you see those in an organization, you say, all right, they already have a headstart?
Derek Choy:
Yeah. I think you’re right that this transition that you’re talking about is not an easy one. It requires a transformation of all parts of a commercial organization from the brand strategy development to content, to regulatory, to sales operations, to marketing ops, to training, to sales. And so as a result, I think, one of the most important things is just having that executive buy-in and support as well as the local willingness to change. So I think those characteristics in the organization, when you have that executive buy-in and the company realizes it’s a mission for the company to go through this transformation, but also it’s not something that’s just something that people talk about. It’s something that even at the local levels, at the local markets, people are feeling the need for it and they’re excited about it. Those two things when they come together, I really think that allow companies to be successful in navigating that change.
Clay Hausmann:
I want to get into some topics that address the life sciences industry, but are a little bit broader as well. One common topic around AI in any industry is that the notion that maybe it will replace human workers, that it’s an either or situation. And in the case of life sciences, that conversation centers mostly around the sales rep. What is your perspective on that topic?
Derek Choy:
To me, it really comes down to the definition of AI. We define AI at Aktana as technology that achieves human level or human enhancing intelligence, that solves specific business problems. And I think we really focus in on this human enhancing part of this definition because we do fundamentally believe that it’s important to note that there’s things that machines do really well and there’s things that human beings will also continue to do really well, that machines won’t. And the combination of the two is where you get the most impact.
When it comes to the field and sales reps and their role in the future, we do recognize that their role is always going to be shifting. In the future, it may be that they have more responsibilities, they have to deal with more complexity, they may be different numbers or ratios of sales reps that exist today, but we believe that the role that they play will always be there. It just may be slightly different. And as it becomes different it’s more likely to become more complex. It’s more likely to be that the sales representative is going to play more of a coordination role. They’re going to have to deal with a larger number of factors, whether it be channels or physicians in their territories or messages or content that I need to deliver. And as a result, they’re going to need more help when it comes to synthesizing and prioritizing that. And that’s where the machine element of AI can really help in combination with their own intelligence and their perspective and their relationship factors that can then drive that through.
Clay Hausmann:
So Derek, as we’re recording this, obviously, we’re all still in our homes, sheltering in place due to COVID-19. What is it about this current environment that we’re in, both related to the pandemic, but also just about the way the world is evolving in terms of digital habits and consumption of information, what is it about the environment and the landscape that makes this idea of Contextual Intelligence, not just intelligence in a linear form, but being able to take in the full context of a situation, what makes that so critical right now?
Derek Choy:
Yeah. COVID-19 just introduces more complexity in the future. We’ve had this interesting situation where, at this time, as we’re all sheltering in place, for life science companies, you’ve got the fact that their engagement model has had to shift to more digital and remote models almost entirely for a period of time before they start to shift back to a mixed model. But when you start having that experience that you’re giving to physicians now, you kind of end up with different number one experiences and expectations for the future where you’ve also introduced more complexity, because there’s different ways that you’re asking your customers to engage, and we as individuals we’re engaging differently now. We are using remote platforms, we’re using digital platforms a lot more. And when you do that, I think it really raises the bar in terms of expectations of how to cut through the noise.
When you have so many more digital interactions happening, you’ve got a mix of different engagements, you’ve got, on top of that, the complexity of our personal lives and different people being affected by COVID-19 differently. All of that means that you really need to, if you want to cut through that and you want to deliver something that’s helpful for someone, a physician, you really have to take into account all that context or else what you deliver falls flat. So I think that’s why Contextual Intelligence is so much more important now, is really that complexity that we’re all going to be living with in the future.
Clay Hausmann:
So as you look to that future, knowing that this is such a unique time, but it is catalyzing a lot of change that I think has been waiting in the wings for a little while, do you think we’re looking at significant change in the way that life science companies and HCPs interact, and then the next connection between HCPs and patients? Or do you think this is all momentary? Quite honestly, it feels very real in the moment, but once it passes, we all will revert back to the things that we’re used to and familiar with.
Derek Choy:
I think we are going to have… There’s going to be some level of reverting back, but definitely not to where we were before. As I mentioned earlier, we have seen in the industry for a while, a desire and a need to move to a different way of operating where you are engaging physicians through multiple channels, both the field and digital channels and find the optimal mix. During this period, we’ve almost reverted entirely or gone entirely to more face-to-face and digital world. And it’s led to a lot of acceleration. We’ve also seen different experiences that physicians and patients have had with digital and remote interactions. Some of them positive, some of them negative. And we’ve seen different effectiveness depending on therapeutic area or purpose. And so, as a result, we do see this new normal now being a mix of face-to-face and digital. So things will come back to somewhere in the middle. So that’s going to lead to intelligence having a even more important role in the future.
Clay Hausmann:
Well, listen, I think one thing that may not change, at least until hair salons reopen, is that I know you and I both will revert to cameras off as much as we can until the hairstyles can be corrected. But one thing that we like to do on this podcast is also talk about the context of the guests. So obviously we’ve talked a lot about the context of the business situation.
Clay Hausmann:
I’ve got a series of questions here. If you are a game, you’re going to reveal a little bit about yourself here so we can get a better understanding of what we’ll call Derek Choy in context. Are you game for that?
Derek Choy:
Sure.
Clay Hausmann:
All right. You might regret it, but here we go. Who has been an influence on your business career that might surprise us?
Derek Choy:
Probably not surprising the person, but I’d say my dad and also a lot of my extended family have been. Interestingly, they are all physicians. And so growing up as part of an Asian family, it’s a common perception that Asian parents want their kids to grow up to be doctors or lawyers. But despite that, my family all really encouraged me to take a risk and do something different. And so that was a huge influence for me.
Clay Hausmann:
Excellent. Okay. So second question, if money was not a factor, what profession would you most want to pursue? And it can’t be the one that you’re currently in. Because we know that’s your dream job.
Derek Choy:
I really love stage magic. So I will be a magician.
Clay Hausmann:
Really? What type of magic or would you be an all-purpose, versatile magician?
Derek Choy:
Probably like illusion.
Clay Hausmann:
Excellent. Have you done any illusion work as a side hobby?
Derek Choy:
Yeah. A bit nerdy, but when I was in university I was part of the magic club. And it’s also the trick I did for my wife on one our first dates that maybe hopefully sealed the deal.
Clay Hausmann:
Really? Okay. All right. Note to self, Derek’s wife will be guest number two on this podcast so we can get that story validated or not. Okay. So what profession would you most not want to pursue?
Derek Choy:
Probably being a politician.
Clay Hausmann:
All right. What is the best book you’ve read recently and why?
Derek Choy:
I guess the most recent book I read, and this is common, is Harry Potter and I’ve actually read it about a hundred times. The reason why is probably because it helps me go to sleep and it helps me take my mind off things.
Clay Hausmann:
Nice. I see also a little link to the stage magic too.
Derek Choy:
Yeah.
Clay Hausmann:
Okay. You are at a family gathering and your eight year old nephew does not ask you to do a magic trick. He asks you what you do for a living. What do you tell him?
Derek Choy:
So you know how Batman doesn’t have any superpowers but has really cool gadgets so he can beat all the villains that do have superpowers? So Aktana gives ordinary business people gadgets and makes them super heroes.
Clay Hausmann:
Wow. Why are you just breaking that out now on the podcast? We’ve had many conversations around this. Okay. That’s good.
All right. Last question. Derek’s ultimate dinner party for four… These can be living or dead. Who’s there and what is on the menu?
Derek Choy:
Okay. I love pizza. So I would bring together my favorite pizza chefs from around the world. So Tony G from Tony’s Pizza Napoletana, San Francisco, Tomakisan from PST in Tokyo, and Johnny Francesco from 400 Gradi in Melbourne. And I would get to try my pizza and give me their expert opinion on how I can improve it.
Clay Hausmann:
Excellent. I had a feeling I was going to have something to do with pizza. And I still have not been to that one in Tokyo. I tried to go last time I was there because I know you’ve raved about it. So it’s still on the bucket list.
Clay Hausmann:
Derek, thank you so much for joining us for this episode. I want to keep going for another 20, 30 minutes, but with that we’ll thank you for your time. It was great to have you on.
Derek Choy:
Thank you. It’s been fun.
Clay Hausmann:
That’s it for this episode of Contextual Intelligence. I’m your host, Clay Hausmann. And we’ll be back in two weeks with a new episode. In the meantime, you can find all our episodes on Apple podcasts, Stitcher, Spotify, or wherever you get your podcasts. And please leave us a review or a comment or a question or all the above. So we can make sure that this podcast brings the proper context to your work. Thanks everybody for joining us.

More Episodes

Decision Intelligence and Data Storytelling with Ganes Kesari

Putting Customer-Centricity into Practice with Grünenthal’s Florent Edouard

Prioritizing Speed: Lessons from Emerging and Mid-sized Pharma with Veeva’s Doug Caldwell

When Design, Data Analytics and AI Go Hand-in-Hand: A Conversation with Robert Redmond of IBM Watson Advertising

Mastering Omnichannel through Change Management with Bayer Canada’s Marc Mollé

From Star Wars to the Convergence of Healthcare: Facing the Future with pharmaphorum’s Paul Tunnah