Greg Kefer: (00:07)
Welcome to Digital Conversations. I'm Greg Kefer, and I'm joined again on the phone by Nik Kolatkar, Vice President Scientific Operations at US Medical Affairs at Genentech. Nik, it's great to have you back on Digital Conversations. How are you doing today?
Nik Kolatkar: (00:24)
Great. Thank you so much, Greg. Great to be here.
Greg Kefer: (00:26)
Yeah, so I think it was June when we were last on, and we had a good chat about some of the things you're doing in clinical trial innovation. So maybe before we get into it, kind of for a check-in, you can just reset, let everybody know who you are and what you do over there at Genentech?
Nik Kolatkar: (00:44)
Sounds good. Yeah. So as you mentioned, I'm Vice President of Scientific Operations within Genentech, US Medical Affairs. So my group really focuses on the US and on medical affairs studies, which are essentially post-marketing trials, real-world evidence types of things. And really, we are all about trying to help fill some of the evidence gaps that may remain after we do our registrational studies, after we get FDA approval, there are still many questions left to be answered for practicing clinicians and also for payers for reimbursement. So my group really helps design studies that can help fill some of those, some of the evidence gaps. And my group in scientific operations also really has end-to-end capability beyond just the trials. We also cover the full gamut of pharmacovigilance and patient safety medical information, where we get information from our call center from, from external customers, as well as project management and biostatistics. So we really have kind of the end-to-end data generation engine for Genentech, US Medical Affairs.
Greg Kefer: (01:48)
Okay. And I guess when you say evidence in data generation, is that primarily being pulled or gleaned from patients, or consumers, as part of these trials that are participants?
Nik Kolatkar: (02:00)
Yeah, exactly. So these are either patients that are participating in our clinical trials, or patients just who are seeing practitioners and we're getting real-world data from the medical records. So for example, one part of the Roche group called Flatiron is a company that's focused on real world evidence and they have the largest oncology electronic medical record in the country. And what they're able to do then is take the anonymized data from patients who are actually being seen in real practice, and then to be able to use those and mine those data to gain new insights. And that's very complimentary to clinical trials because sometimes clinical trials, they're designed to create a lot of consistency and uniformity, but they lack some of the real world applicability and generalizability. So I think using both clinical trials and real world evidence, they compliment each other.
Greg Kefer: (02:54)
That's a good segue here. So as we record this, we're kind of rounding out 2021, getting into 2022. And there's a lot of chatter out there in the healthcare news about predictions for the new year and how things are gonna be different and better going forward. We're pretty much kind of year two of COVID, there's this huge worker shortage., there's a press for more diversity. Without getting too far into it, as you kind of look ahead, what are some of the key themes that you're worried about? Are those them? Ae there some other things that you're thinking about as you kinda lay out your 2022 strategy?
Nik Kolatkar: (03:32)
Yeah, it's a great question. I think a lot of the factors you mentioned continue to be at play. The pandemic is still with us with, omicron, but I think one of the things that it's doing, it's really creating a forcing function to help us think differently about how do we conduct clinical research while at the same time meeting the needs of customers, meeting patients where they are, kind of reimagining studies from the viewpoint of the patient. You know, how do we make it more convenient for them? And I think now the advantages, we have so much advancement, too, in digital technologies, that we're really able to bring that to bear in service of the patient. So we're trying to continue along this journey of things that were accelerated during the pandemic, and trying to make those are more of a routine part of the way we do studies even once we hopefully get beyond the pandemic at some point, because I think all of that is very meaningful for patients and can really increase patient engagement and their desire to participate in research and can also help broaden our desire to bring research to a more diverse population.
Greg Kefer: (04:36)
Yeah. I was talking to somebody the other day and I said, boy, if there's ever a golden age to get patients, consumers, people, to interact digitally with healthcare, it's right now. For the past two years, you couldn't get a COVID vaccination unless you got an app, or you had to do virtual visits with your physician because of social distancing. So it seems like, if there's a silver lining because of COVID, it's that that there's been a big strong push for more virtualization on the patient experience side. Is that what you see happening on the life sciences side of things?
Nik Kolatkar: (05:15)
Mm-hmm , yeah, I think just as we all of us have become accustomed to meeting with various people we interact with on a day-to-day basis through zoom, through video conferencing and real time, I think we're trying to bring all of that into our clinical trials to not only make things more efficient, but also make things more convenient for the patient. I mean, if a patient is able to do a screening visit from the comfort of their home via zoom, just as effectively as making the trek into a hospital, paying for parking, trying to find the place, wasting hours of time. That can be a great, win-win not only for us, but for the patient. So we're trying to do more things like that. We're also really trying to take this concept of decentralization, which, you see it all over the place with blockchain and a lot of discussion in the finance world, for example, about decentralized finance: How do you create a more democratized finance that's more accessible to the general population? So we're trying to think to about decentralized clinical research,.. how do we make clinical research more accessible to a broader set of patients? How do we try to use technology to really be more efficient in terms of how we're gathering data? All those types of things. And then also, I think importantly, to also use technology to try to get real-time feedback from patients in our trials, because I think too many times the old approach had been to do a study, have patients complete the study, then at the end of the study, we send them a five page survey, both to the investigator and to patients, on how their experience was. Which is fine, but then, at that point, it's a little too late to do anything about it, because the study's already over. And obviously in order to get kind of feedback in real time, you need it to be simple for people. So that's where we're using AI-based chatbot technology, for example, in one of our trials now, where we've done two waves of real time feedback, we started with investigators getting feedback from investigators, just three questions, one to five scale. What is their experience like in the trial with us, with our CRO, and their overall experience? And that has been extremely enlightening because we were able to find in general, reassuringly, our investigator experience is quite good, in the range of four and a half out of five, but it's allowed us to see, are there a few sites that are not having as good of an experience? We've had a few sites that may rate us a one or a two, and then we're able to follow up with those specific sites, better understand how can we help them? How can we create a better experience? You know, what's going wrong in real time, which I think is very key. So that's one way we're using technology as well now, in new ways that we hadn't before.
Greg Kefer: (08:01)
Right. And you talked about decentralized reaching out to patients, kind of orchestrating these conversations. On the last podcast, you use the concept of digital navigators, chatbots, AI to do this, which is using a mobile device, which we see are borderline ubiquitous these days, roughly 85% of US consumers, adults across all demographics and regions have smartphones. Yet, a lot of the strategies of asking them to download apps and learn apps and set up accounts, which presents a lot of friction, so usage isn't as high as you would expect at this time, yet a chatbot operates outside of an app environment. Right?
Nik Kolatkar: (08:44)
Exactly. Yeah. No, the nice thing about the chatbot, it uses very familiar — essentially text messaging — technology that virtually everybody's very familiar with. I mean, even my kids are using text messaging and they're seven, eight years old. It provides a very low-friction, very easy-to-use, fast way for people to engage. And, for things like a survey where you're looking for very simple information, I think it doesn't need to be a very sophisticated app. It can be something very simple. And the nice thing is we're able to also test the time it takes for people to complete the survey. And we're finding that people can do it in basically like 30 seconds or less, which is really the aim. We don't want people wasting a lot of time on this. It's really just to be able to quickly get us feedback and then for us to be able to follow up with that.
Greg Kefer: (09:32)
Yeah, I think really great innovation is simple, especially when you're just dealing with people that maybe aren't necessarily super interested in spending a lot of time learning and setting up accounts and downloading things. They just want it to work, and I think when you talk about chatbots and conversational AI, where it's just using language as the means to communicate and the idea that it's very easy during the context of a workflow to ask, how's it going? Are you happy? Versus hitting 'em the next day via email to fill out some long, gnarly survey monkey poll or something like that. That's gotta be very handy to get that in real time. And I would imagine you get higher engagement rates when you do it that way, right?
Nik Kolatkar: (10:13)
That's right. Yeah. When it's very easy for people to do something, I think you're gonna get higher engagement. I think the other piece that drives engagement is, as I said, is closing the loop on the follow up. So if we're asking people to fill in a survey, then, if they mark down, they're having a poor experience and they see that we then, that day or the next day, follow up with them to say, we noted you only rated your experience a one out of two in our last survey yesterday, we want to follow up with you to see what can we do to help... I think that drives engagement too because people see they're not just filling out a survey and the data going into kinda a black box, they're filling out a survey and the company's getting back to them immediately. So I think both of those two things together are needed.
Greg Kefer: (10:57)
Has the labor shortage hit you guys? Again, I work with a lot of healthcare providers and obviously there's a very well publicized scenario where the healthcare industry, mostly hospitals have lost 500,000 workers since the beginning of the pandemic. And they're really struggling to do a lot of the outreach through call centers and human teams where this technology that we're talking about makes a bunch of sense. Have you experienced a worker shortage dynamic on your side?
Nik Kolatkar: (11:27)
Yeah, I think in general, both the worker shortage and the supply chain issues are impacting clinical research, and also, just the ongoing clinical demands of the pandemic. So the hospital ICUs and emergency rooms still continue to be busy with patients coming in, especially with the new variants. So that's impacting our ability to do research. The supply chain crisis, believe it or not, even simple things like clinical trials, supply kits that have test tube vials and blood collection vials, things like that are in short supply. And if you don't have those, you really can't do trials either. So the supply chain crisis is affecting things. And then as you said, other worker shortages, especially clinical trials operations staff are also in short supply. Because there's also been quite an increase in trial demand due to all of the trials going on with COVID vaccines, therapeutics, diagnostics, et cetera, there's been a huge explosion in clinical trial demand, and that's also leading to a shortage of clinical operations personnel. So all of those forces are also driving us to think differently about how can we use technology to be more efficient. And we're trying to do whatever we can to be able to conduct research and continue to complete our trials in a timely fashion, despite all of these disruptions.
Greg Kefer: (12:50)
That's an interesting segue because I think one of the things I see a lot of, and back in the days when we went to conferences, you saw it there, there are a lot of companies that have a lot of big talk, but have a hard time executing. And a company like a Genentech it's a big company. It's not easy to move the ship, but boy, it sounds like you're actually doing things! You're not just talking, going on podcasts and talking to people like me about how glorious it is... But how do you do that? How do you move a ship like Genentech to reimagine, business as usual in times like these?
Nik Kolatkar: (13:25)
Yeah, it's a great question. I think one of the things we've done is really, we've created something called Innovation Hub. And the idea behind this is that we've realized that, and especially now with all the factors, you just mentioned all the, the challenges in doing clinical research right now, that it can be quite challenging for teams, within a big enterprise who are really very much tasked with trying to deliver evidence generation projects on time to deliver a large volume of projects. It can be hard for those teams executing those projects to really be focusing on innovation, right? Because there isn't as much natural incentive for those teams to want to innovate anything because the natural tendency can be to say, okay, we have this many trials we need to finish. We need to finish them on time. We have all of these supply chain, worker constraint issues...Let's just stick with our tried and true approaches. Let's not do anything too different. Let's not innovate too much. We need to just get this done. And that's very natural for that to happen. But I think the consequences then is the company doesn't continue to push the envelope on innovation. So what we've done is created something called the Innovation Hub, which is essentially a separate team who are not directly focused on the day to day delivery of studies, but a separate team who actually have a cross-therapeutic area or a cross-portfolio agreement. And they're looking at the sort of the horizontals, if you will. So if the therapy area teams who are delivering the studies are kind of the verticals, these are sort of the horizontals and what this Innovation Hub is really trying to do is, is some horizon scanning, looking at what are clinical trials of the future gonna look like five years from now, how do we reimagine the kind of patient experience from the ground up? How do we take the different pieces of the patient journey through a trial? And then how do we break those down into where the pain points are for the patient? And then how do we match up those pain points with technology that can help solve those? So really it fundamentally starts with the patient journey, the and pain points, and then technology is really a means to an end. And the idea is then this innovation hub can vet kinda the best of breed technologies that are in the marketplace, right? So we would meet with a number of different startups, with academia, with small companies, with CROs, and try to see, for these different pain points, who in the marketplace has the best technology. And then the Innovation Hub would kind of vet all of those and then make sure they meet all of our standards. They're protecting patient privacy, they're meeting all the regulatory requirements, those types of things. And then once they've done that, then they have a few different options for teams. Then teams who are executing studies can then take those pieces, and then that way they can access innovation without having to spend a lot of time on it. And that's something we're actively doing right now at Genentech.
Greg Kefer: (16:23)
How do you take those ideas,... Innovation Hubs,... Go through all of what you just described... They come up with some fantastic concepts or innovations. How do you take it from the whiteboard to the field? Like what kind of charter do they have to take a digital navigator that's a chatbot, that does all the stuff you were just talking about, and get it implemented, in these five studies where they're stretched, and it would be very easy for them just to not innovate because that's a faster means to an end in a worker shortage. What do you do to kind of give it the horsepower to go from the drawing board to the field?
Nik Kolatkar: (16:58)
Yeah. I think we try to use a lot of kind of startup mindset in the Innovation Hub to really start small, to try to get some quick wins and then use rapid prototyping to really try to iterate as you're going. So, for example, in the project we're doing right now, where we're using conversational digital technology and AI-based chatbots to administer kind of real time pulse surveys, we started doing that on just one study, we said, okay, let's just, we want to pilot this. Let's just try it in one study, keep it very simple for the team. We're not gonna slow down the trial. It's gonna be not very complicated. And then we can build on that as we go. So we did one wave, we then tweaked a few things for the second wave. So I think it's important to start small and then also to just keep things simple, because I think sometimes there could be a tendency to try to do the whole thing. And I think you really need to break it up into pieces. So that's one thing we're trying to do, kind of consistent with how startups sort of work. And then once you've gone through a number of iterations and done some rapid prototyping, then you can scale the technology to the portfolio. So the work we're doing on the AI chatbots to get real time customer feedback is a very, it's not specific to a, a given study, so it's something that's very scalable. We're running 500 trials in US Medical Affairs, Genentech. And some of those are investigator initiated studies, but even out of those 500, about 30 of them are US Medical Affairs-led Genentech studies. So we could easily then take this experience from one trial and then scale it to our health portfolio. And that would really drive a lot of new insights for us as a company.
Greg Kefer: (18:39)
Yeah. As you were talking too, this is maybe a slightly different topic, but if you're in the business of collecting information from people — evidence generation PROs, whatever — do you find that the conversational modality, where you're pulsing, you're checking in moment versus the next day, are you seeing a different level of insight from patients and participants that maybe you wouldn't had you done the poll two days after where you're doing it in flow?
Nik Kolatkar: (19:09)
Yeah, I think currently, like I said, we're kind of in our early stages, so we've purposely kept the information we're collecting very simple. So right now we're really just getting information from people, essentially on just sort of one to five scale. We've limited free text entry purposefully, just to keep things simple. I think the value though, is the real time collection of that information. It's really valuable because not only can we close the loop, as I mentioned immediately with sites, we can also look at trends over time. So we can also look at not only just how did they answer the survey at this snapshot in time, but what has been the trend, and is their experience falling over time in the trial? And if so, that's a signal to us, we really need to better understand what's happening. And all of this is really, as I said, in service of really trying to create an amazing experience for both our investigators and our patients participating in our research.
Greg Kefer: (20:11)
Yeah. It's all about giving those patients or participants a delightful consumer experience, right? Because, we have these smartphones we're out in the world using 'em for travel and banking and the experience should be in the same zone as that. And if it's not, they're gonna let you know, right? They're gonna tell you in real time that they're not liking,... that just like they don't like the chicken at the restaurant and they go on Yelp and give it a one star, right?
Nik Kolatkar: (20:35)
Yeah, yeah, exactly. I mean, it's like when you take your airline flight somewhere, then you get a survey from the airline and they're trying to do the same thing, right? They're trying to better understand in real time. I mean, for the airline, the best thing would probably be to be able to get surveys from people while they're on the flight. So I think a lot of industries are moving toward, trying to not only get more customer insight, but get it in real time.
Greg Kefer: (21:04)
Yep. I feel like the medical industry has to understand the whole notion of peer review, which is so common in dining and shopping. And it sounds like you're onto something here. And with the innovation approach that you're talking about, maybe there's a platform for you to reimagine how that's done, and you can come on Digital Conversations in June and tell me how it's going.
Nik Kolatkar: (21:27)
Yeah exactly. And especially I think with studies, you're kind of on a longitudinal experience with people. Like some of our studies may last a year. They might even last a few years. And over that period of time, people are not just interacting with us on a snapshot, just transactional basis. We want people to feel like they're on journey with us. That is a great experience for them throughout the journey. So I think that there's a longitudinal aspect of being a patient in a trial. I think it's also important for us to be able to get feedback from them during that journey.
Greg Kefer: (22:02)
Yeah. That's the magic consumer model, right? And if you can crack that code, do some of the things you're talking about, boy, you're really off to the races. Impressive. Well, Nik, thank you so much for sharing your insights today. I definitely gonna have to come back again in six months and find out how some of this is going, because it is really fascinating.
Nik Kolatkar: (22:23)
Sure, we'd love to share. It's all very fascinating and we're kind of continuing to innovate as we go. So, looking forward to sharing more on the Innovation Hub and bringing some more of what we're doing in real time to you next time.
Greg Kefer: (22:35)
Okay. All right. Well, Happy 2022. We'll talk to you soon.
Nik Kolatkar: (22:39)
Thanks Greg. All the best.
Greg Kefer: (22:41)
Okay. This has been Digital Conversations.
Greg Kefer: (22:45)
Thanks for listening to digital conversations. If you like show, you can always subscribe on iTunes and feel free to like retweet and share on your social networks. This and other episodes are available on iTunes, Spotify and LinkedIn.com. We'll be conversing again soon with a new episode. So long!
