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Insights in Neurological and Neurogenerative Diseases

Adam Jenkins, Associate Director Global Data Science at Biogen

Insights in Neurological and Neurogenerative Diseases

Helping Biogen deliver worldwide innovative therapies for people with neurological and neurodegenerative disease

Insights in Neurological and Neurogenerative Diseases

Recommended Books:

Good to Great

Great by Choice

A Curious Mind: The Secret to a Bigger Life

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Insights in Neurological and Neurogenerative Diseases with Adam Jenkins, Associate Director Global Data Science at Biogen (transcribed by Sonix)

Welcome to the outcomes rocket podcast where we inspire collaborative thinking, improved outcomes and business success with today’s most successful and inspiring health care leaders and influencers and now your host, Saul Marquez.

Saul Marquez: And welcome back to the podcast. Really appreciate you tuning in again. I have a special guest for you. His name is Dr. Adam Jenkins. He’s the Data Science Lead at Biogen where he works on optimizing commercial outcomes through marketing patient outreach and field force infrastructure utilizing data science and predictive analytics. As we turn the corner on the year I think a lot of companies and organizations are starting to focus on this. So it’s a timely topic a little bit on Biogen they’re a leader in the treatment and research of neurological diseases. They’ve been doing it for 40 years prior to being a commercial lead, Adam was part of their digital health team where he worked on next generation applications of wearable and neurological tests. He’s got a PHD and genomics. And he also teaches management skills for Data Science and big data initiatives at Boston College. So it is a privilege to have Adam on the podcast. Adam, thank you.

Adam Jenkins: Glad to be here.

Saul Marquez: Absolutely. Now Adam let’s talk about what got you in the health care to begin with?

Adam Jenkins: Yes. So kind of as my I’ll say my history kind of runs for itself. I was always really interested in science from a young age and up through undergrad and grad school. But when it really gets down going through a PHD you kind of realize that doing research it it’s really difficult to make a large impact in the world. I always say that I’ve had really a couple careers in my life. You know I wanted to be a scientist at one point that I want to be in government at one point. It’s always how can you really impact the world. I think that that’s where most people who get into the medical sector have a very similar outlook. Everyone really wants to have an impact on the world. So you know coming to kind of a biotech and a pharma career really allows me to have the biggest impact on that and the largest swath of patients and people in general and that’s really what I’ve always really wanted to do is how can I impact the lives of people and really have a meaningful lasting impact not just kind of a necessarily a one off. So the medical sector for me is really a way to do that to connect with people to make a to make an impact on people’s lives and really makes me kind of feel good about what I’m doing with my skills and my knowledge.

Saul Marquez: That’s awesome. I’m yeah and there’s no doubt that the transition from the bench to the books to the where the rubber meets the road and health care is is a big one. And you took the jump so kudos to you. I’d like to hear from you. You know you’ve been doing it for a while now. What’s a hot topic that needs to be on health leaders agendas today and how are you and the folks at Biogen approaching them?

Adam Jenkins: Yeah. So I think that a really hot topic you know and kind of feel that I mean in terms of kind of data science artificial intelligence is really how is it going to impact and kind of change how health care is run. So we see you know the big thing with the medical sector is that it’s not just one group kind of formatting what the future is it’s not just the government. It’s not just biotech it’s not just researchers. It’s everything from payers to HD and it’s how everyone interacts with each other. So the hot topic that I think is really you know how is artificial intelligence really going to change how operationally all those players really interact with each other so for instance artificial intelligence machine learning is playing a big impact on the provider and the payer side of things. Just in terms of getting paperwork through recognizing incorrect forms things along those lines that are really operational and I’ll say aren’t necessarily the sexy side of things. They’re having a huge impact on that side. But even how, what the impact is on patient and physicians and how they interact with each other – that’s a big one that really hasn’t been kind of identified yet or really been addressed by a lot of different groups. So what my group was really looking at is you know what, how does this change if we really do want to kind of implement an algorithm in the field per say what does that actually do that patient physician interaction? Does it change? How they talk to each other? How they feel about each other? How they get the information? All those things that you don’t really think about those are the things that really impact a patient’s lives and really how the physician and the patient work with each other in the field I think that’s neat.

Saul Marquez: And you’re right. I mean as health leaders we’re always thinking about, hey what are we going to do to operationalize this better.? How can we lean this out a bit? How can we get better improve outcomes? I recently had a guess that’s working on a company whose core technology is blockchain. And she brought up something really interesting she said, Who cares what it is as long as it gets you the outcome? And she found that focusing on the outcome that customers wanted instead of what the technology was really helped gain momentum with customers and providers using their technology. Do you do you find that that’s the same with A.I. I mean or do you feel like hey you know by saying hey this is a guy we could get more traction?

Adam Jenkins: Well I’ll say that A.I. is definitely a way to get viewers in the door you know and really to take people’s interests. And how often you know when I talk about this you know whether it be at a conference or with students I always say you know raise your hand if you if you dealt with A.I. in a health care sector before whether it’s going to the doctor or dealing with an insurance company and almost no one raises their hands. You know we’ve been I don’t say we’ve been promised but we’ve been talking about it for good. More than a decade now that it’ll be changing know how how doctors do their work. Kind of the information they get. But we really haven’t seen it yet. I think that one of the biggest hindrances is because no one’s been able to kind of crack that that nugget of how will a patient or a physician interact with such an algorithm. Now I’ll say that the medical sector is very set up a certain way. So you know everyone is held accountable. Everyone is comfortable and that makes it very difficult to implement new technologies.

Saul Marquez: Sure.

Adam Jenkins: So I’ll say that yes I really as a way to pique people’s interest but we still a long way to go in terms of actually kind of breaking into the industry at any large scale.

Saul Marquez: Yeah yeah. That’s a good call out. And so if you guys you know lift the veil you get you get the folks in the door. I talked to us about some of the examples of what you and your organization are doing to improve outcomes by doing things differently?

Adam Jenkins: Yeah. So I say that where a lot of companies not just ours is making investments is really kind of the electronic health records and claim space. The nice thing about I’ll say the US at least is you know we have a pretty open as a community in terms of getting data from third parties. So whether be a company like ITV someone who is kind of a health record aggregator.

Saul Marquez: Yes.

Adam Jenkins: We can buy all these these records relatively easily and it gives you a very good kind of population outcomes type of data set. And so what we can do is you know especially in terms of making patient outcomes better we can really start to use A.I. and machine learning to really see what’s driving an outcome. So what you know our our disease spaces usually where diseases and neurological diseases. Well we’re able to do is we’re able to really see where our physicians misdiagnosing. Where are they missing diagnosis is just because they don’t know that a disease exists or where do we know that that they are missing signals, for instance. So what we’re able to do is we’re able to use those algorithms to really tease out those those those nuggets of truth that normal physicians and PCTs miss to both better a patient’s outcome and when we’re looking at patients we really want to be able to say you know early on in the disease we are going to have some type of intervention so that it doesn’t get worse to really identify those patients who are early on in their disease journey will say.

Saul Marquez: Yes.

Adam Jenkins: And to make sure that they get the right treatment so that it’s not too far down the line and you know it’s really a futile exercise to really try and make them better. So we’re really putting a lot of stress on patient management through this type of data analysis.

Saul Marquez: Fascinating. And so you’re working on these algorithms you’ve got a group of experts plugging in the data points that’ll flag things now does this are these prompts that that come up in the EMR as the physician is logging things in. Or or is it after the visit. How does that work?

Adam Jenkins: Yeah. So it’s something data is usually a little bit lag depending on where you get it. So sometimes it’s three months lag sometimes it is instantaneous and going through the system but we usually use how the physicians are actually code putting their morbidity or a prescription. Things like that because it is such retrospective Yeah it’s retrospective.

Saul Marquez: Got it.

Adam Jenkins: But it’s very telling when you look at a physician and actually things that we often miss are due to that physician – patient interaction. So for instance if someone’s had a disease for a long time they’ll stop coding it in a patient’s history because nothing’s changing so they don’t keep coding it. And for us you know when we start looking at the type of information for us it looks like the patient stopped having the disease when in reality they didn’t.

Saul Marquez: Yeah yeah.

Adam Jenkins: So things like that you can have to put yourself in a physician choose to see what’s going on. So it’s not always you know accurate data.

Saul Marquez: For sure. I think that’s really great. And yeah I mean yeah as a leader you really have to understand where you’ve been. See what mistakes were made and then of course correct them. You could definitely use this data to help explain to to the physician and to the other leaders in the organization what the misses were and how we could change that moving forward.

Adam Jenkins: Yeah. Yeah I completely agree. It’s often a topic that AI for a lot of whether bc suite or physicians isn’t an area that they’re comfortable with. So being able to translate that to it kind of an action tunable insight and something they can understand is big for us because if they don’t understand or if whoever is going to be responsible for enacting this doesn’t understand kind of what the levers are that they can pull and what the impact is going to be. We felt that our job so was so we try and make sure that whatever we do and produce is is palatable and easy to utilize for everyone.

Saul Marquez: Yeah. Now that’s really interesting. How about with the building of technology the running of algorithms. You’ve got to do iterations. Can you share with us a time when you had a setback during one of those iterations and what you learned from it?

Adam Jenkins: Yeah I’ll say that so I can say this one happens all the time. It’s not. It’s probably the biggest one that happens in terms of impact and the one that happens most awful often is that when we’re running these algorithms we have to keep in mind that we don’t see the entire patients kind of history or what’s going on with them. So a lot of times patients are being say coded as one disease it’s just the physician being smart and doing you know what I need to code up this. This patient is having a disease in order for them to get treated by their insurance. So oftentimes we see these type of artifacts coming through the data and the algorithms will learn that and that all of a sudden a physician or a payer for instance will say will reimburse different codes. You don’t have to keep misquoting this patient. And then the algorithm fails ultimately and we don’t we don’t understand why and it’s only through kind of intense looking at how formularies change on the first of each year which is also very topical seeing as though it’s beginning in January you know those type of things that algorithms are learning from and that we’re learning through the data that aren’t captured those those cause a lot of failures and it’s really frustrating for us but it’s just part of kind of this space like I started off the podcast saying you know it’s all these different players together in the same playground which makes it very difficult to tease out true signals. There are a lot of algorithms so it’s us trying to triangulate and calculate and figure out what’s the highest probability of being correct. And oftentimes you know we kind of missed the boat and it’s not it’s not what we think it is. So I say that that’s kind of the biggest issue that we have time that we fail most often is when that happens.

Saul Marquez: Now that’s interesting and because what happens in the room and the judgments that are made and the outputs could be confusing for the algorithms.

Adam Jenkins: Yeah it’s getting as much as it’s confusing for the patient in terms of medically why they’re doing things. It’s just as confusing for us you know kind of looking at what the output from that room is trying to really understand what occurred.

Saul Marquez: Wow yeah that’s super interesting. And so you obviously tweak it, you learn it, you collaborate amongst different different silos, and eventually you get to an end point. Talk to us about a time when you had an amazing experience with what you’re doing there. Talk to us about something exciting that happened to you recently.

Adam Jenkins: Yeah I’ll say that for. For us working in the neurological on the rare disease space a lot of the diseases that we work on really are ones that don’t have any treatment options. So when we get into a rare disease phase one of one of the biggest moments that we see that are exciting is when we have a launch and when we apply some type of algorithm or statistic that we think will make a patient’s lives better. So whether it be treating spinal muscular atrophy which is our big rare disease at Biogen or something like multiple sclerosis. You know implementing an algorithm in the real world saying that we think that this will decrease adverse events or something like that seeing those in the real world and seeing the data start to stream in months later oftentimes we’re waiting and seeing that is changing. That’s that’s usually the biggest win for us. That’s you know that’s when we go out for drinks after work and really say, you know it worked. So that’s really where we kind of hang our hat and say you know we really did good today even though it will take you know a year or two years to get to that point. That’s really where really where we aim for and why we work where we do.

Saul Marquez: That’s pretty great Adam. And so you know as as folks are listening to this interview today you know. What advice would you give to an organization leader or the people that typically get involved in implementing these types of programs like what does they have to do to increase the chances of success with A.I. in their organization?

Adam Jenkins: I normally say take it slow. It’s not a easy thing to do. You have to get the data correct. You have to get the right people. You have to make sure that the environment that you’re trying to affects will actually be amicable to you know taking those new insights. It’s not a quick way. You’re not going to do it in a year or even two years. It’s going to be a multi-year process and it’s going to be difficult. There’s nothing easy about it. I’ll say that the creation of the algorithms is the easy part. It’s everything around it that’s really really difficult. So oftentimes you’re not you’re not fighting with the data, you’re fighting with actual people and implementations and just the operationalization of it. So really take it step by step kind of stay the course and before long you know you will start seeing a little wince and successes. But I will say that just knowing that it’s not a quick journey is usually the biggest thing that I can say to anyone who is trying or any organization that’s trying to get into this space because it’s often the one that companies are looking for quick win so little they’re looking because they think that they see every other company doing it they’re looking for that quick feedback loop to say hey this is working with a reality you’re going to have more failures and a long longer timeline than you think.

Saul Marquez: So what else is there a typical timeline you’d say to get it to start working?

Adam Jenkins: So I only say so the first thing is I’ll say that you have to I’ll say it’s usually a three year process that first year is really assessing not even the data or the or the algorithms but really what space do we want to try to effect and where do we want to kind of concentrate all of our power. The second year is usually all about data and making sure that you have the right data set up in the right place that is accurate data. So doing all kind of those descriptive statistics just to make sure that what you have is actually what you need and then that third year you can usually start putting everything together and and seeing some type of return on your investment.

Saul Marquez: Love that. That’s a great outline. Adam and folks just a little insight as you look to implement things here in the New Year. Do it with perspective and do it with the right approach and manage your expectations. Because if you do and you do things right the results are going to be very promising. Adam what would you say is is a project that you’re hyper focused on today?

Adam Jenkins: So hyper focused. I am probably the one that most excited about the one that my most hyper focus about is the misdiagnosis of rare diseases we see so many patients being misdiagnosed just because physicians don’t know or they learned it you know one line in a textbook back in their first year in medical school and they don’t remember a disease exists. So we see all the time patients misdiagnosed and going through you know you hear stories 10 – 15 years of patients kind of suffering through a disease that they think they can’t solve. And it’s only just because a physician doesn’t know it exists or doesn’t remember it. So if all the physicians no fault of the patients. But it’s really seeing a patient finally be able to put a name to the disease and a reason for what they have. That’s something that’s really really exciting especially when it starts to open up different kind of interventions and disease modifying therapies that they can now be treated with. Seeing that kind of world open up to them is something that’s really exciting is something that I’m really excited about.

Saul Marquez: Adam I think it’s great and folks I’m sure you’ve at one point or another found yourself maybe with not something super urgent or terminal but maybe something there you’re just like, man, I just got out of the urgent care and I still don’t know what this thing is. I just think that’s 20 times worse and now there’s a platform to be able to say this is exactly what it is. And that’s exciting. And that’s the work Adam and his team are working on. So Adam appreciate all the things that you guys are doing to really help the diagnosis of these things.

Adam Jenkins: Yeah no problem. And it’s one of the things I always joke about that WebMD was the best and the worst thing to ever be created because it did kind of open up a world where patients and even physicians were able to start to diagnose and really look at kind of a technical technological interface for diagnosing themselves or others as bad as some that I know. The diagnosis is usually are and how off they usually are. There’s a great first stepping stone for people getting used to that type of interaction. So I’ll always say is bad it is. It really was a great first step kind of in our industry.

Saul Marquez: I think it’s a good call out. So Adam getting close to the end here. Let’s pretend you and I are building a leadership course in A.I. and healthcare to be successful and prove outcomes. I’ve got a lightning round questions followed by a favorite book for the listeners. Are you ready?

Adam Jenkins: You got it. Let’s go.

Saul Marquez: All right what’s the best way to improve health care outcomes?

Adam Jenkins: I say understanding the needs of everyone from physicians,patients, providers. Nothing works in a silo. So even if you have a terrible disease but a payer will pay for it you might not get treated. So really understanding the needs of everyone that’s in a given environment.

Saul Marquez: What’s the biggest mistake or pitfall to avoid?

Adam Jenkins: Assumptions. Assuming that the algorithm is always right or their statistics are always on point. You can assume that your assumptions are wrong but never assume that they’re correct.

Saul Marquez: Love that. How do you stay relevant as an organization despite constant change?

Adam Jenkins: I’ll probably say learn slow and act fast so don’t be worried if you’re not the first one to implement a technology or do something. Learn slowly along the way – see what others are doing but when it’s time to finally act, act fast on it really get it going.

Saul Marquez: What’s one area of focus that drives everything in your organization?

Adam Jenkins: I’ll say the patient everything is patient focus from the research we do to the commercial side to our patients services. Everything we do is really to help the patient help their outcomes and kind of provide therapies to those who currently don’t have any.

Saul Marquez: What’s your number one success habit.

Adam Jenkins: My number one success habit: keeping a schedule. It’s got to be a schedule knowing when they’re going to do things why you’re doing things. Keeping that habit going and that kind of regimen helps me really stay on task.

Saul Marquez: Love it. What book would you recommend, Adam?

Adam Jenkins: I always I probably say two of them. Any book by Jim Collins. So whether it be Good to great,Great by Choice. Just in terms of seeing what it takes you know a business to succeed. I usually think that those things are really cross-functional. Most companies have core aspects that are very similar to each other. So either of those two books are great. And then Brian Grazer spoke A curious mind in terms of asking questions and where it gets you. Those are all great books that can really help anyone.

Saul Marquez: Outstanding recommendations Adam, thank you for that. And folks you could find this mini syllabus that we’ve created for you along with a full transcript links to other books and resources that Adam suggested just go to outcomesrocket.health and that type in Biogen or type in Adam Jenkins in the search bar. You’ll see this interview pop up with all that info before we conclude. Adam I love if you could just share a closing thought and then the best place for the listeners could get in touch with you or follow you.

Adam Jenkins: Yeah. So yeah for a closing thought I really you know everyone who is in the medical sector. I’ll say oftentimes as I’ll say exciting as it is and the prospect of helping everyone. It’s often a very frustrating sector as well when we see all the potential for whether it be a treatment or a technology or a platform to never get discouraged. They usually take a while to get off the ground and they may seem futile at some points. But I’ll say you know always keep going because we know why we’re doing it for the medical community and for the patients. So just to keep on working as hard as we can to make that happen. In terms of contacting me feel free to find me on or connect with me at LinkedIn with my name Twitter my handle is @rootofallevol, so for evolution for evol, look at that person. Or email me at adam.jenkins@biogen.com, I’m always willing to help out.

Saul Marquez: Adam folks take Adam up on that invitation to connect. And Adam thanks for spending time with us. It’s been really really fun.

Adam Jenkins: No problem.

Thanks for listening to the outcomes rocket podcast. Be sure to visit us on the web at www.outcomesrocket.health for the show notes resources inspiration and so much more.

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