Tara Austraat-Churik, Partner at Blue Matter Consulting, Lead of Pharmaceutical R&D Practice, on Good Science, Good Execution and how time spent at The Department of Justice and The FBI helped shape a career in Pharma.

Welcome And Tara’s Role

Hi Tara, how are you doing?

Hey, good, how are you?

Good, good, thank you. And um, starting at the top, um could you tell us a little bit about yourself? Uh introduce yourself, please, current role, and where you're focused today in your professional life.

Sure. So uh my name is Tara Alstra-Curek, and I'm currently serving um as a partner at Blue Matter Consulting. Um, and there I lead the biopharmaceutical RD practice. So essentially my day-to-day focus is really working on um good science and good execution, um, meaning really thinking about the end-to-end molecule to market value proposition of what our clients are trying to do, um, whether they are biotechs or working on their first asset, you know, all the way through, you know, large companies who are trying to maximize and optimize um their portfolios. And then within that, I particularly focus on uh the upfront side of the value chain. So things like um helping with AI in drug discovery and trying to optimize processes to do that effectively, um, clinical trial acceleration, um, including site selection and accelerated enrollment and patient finding for clinical trials. And then I also do a fair amount of work um in adjacencies like medical affairs, uh real-world evidence, and operating model work really across the pharmaceutical spectrum. And then I began my career in pharmacovigilance, um, where I do a fair amount of work um, you know, up and through today.

It sounds like you don't have a busy week when it comes to work then. Before I'm so excited to speak about or ask you about your career, your journey, and and and the work you're doing now, especially, and you've you've touched on it straight out of the gates, you know, in the advent of AI. But before we get into that, could we kind of dive or jump right back to the beginning where you began your career to learn about that? But before that, something I think is really lovely is a personal fact or something interesting or fun, kind of an icebreaker about yourself that our listeners wouldn't know about you, and I guess I don't know about you, that you'd be happy to share.

Humanities Training Meets Healthcare

Um, so I think people find surprising um that I initially studied um English and critical theory and philosophy um throughout my time in undergraduate studies. And I I don't sort of often see people with the profile of doing that and then uh moving into more say science related or or science-focused um careers. So I do think people tend to find that a little bit surprising. Um, but it's you know what I what I always loved because when I was in high school, I I mean, I really wasn't exposed to any of that type of thinking in terms of philosophy. And then in in undergraduate, I had the opportunity to take that. And I was just so intrigued by it because you take an issue and you examine it from so many perspectives. And uh by doing that, you learn what you think and you learn why you think it, but you also interact with people who have very different perspectives from you, but you learn how to debate um respectfully and intellectually, and you um, you know, it doesn't change necessarily any anyone's opinion, but you've been able to really be influenced by and contemplate the perspectives of others. And I think that that type of training is just so important um, you know, in life, in the world, in business today. Um, and I loved it. I I really, really um, you know, loved uh loved doing it.

Amazing. And I completely agree that the people you meet at a university or any educational from different backgrounds, different views that you can have a good debate with to challenge your own thinking and and vice versa is invaluable and to carry through. Fascinating. Um so you you started English philosophy, you you had the an interest there, but you've you then transitioned at some point, way back at the beginning of your career, into healthcare. When did that switch happen? Was it intentional? Was it part of, I guess, the academic journey? You were you exposed to something that that put you on the path to where you are now?

Switching Paths Into Pharmacovigilance

Um, yeah, so I grew up always wanting to pursue medicine um and to become a doctor. And so that was always in the back of my mind. And I earnestly tried that through about my junior year of undergrad when I just, you know, kind of hit this wall because I happened to be in London at the time, actually studying. Um, my university has a program there. And uh it just it just became a natural transition. It became clear to me that I was sort of like going up against this wall where it wasn't medicine wasn't the right thing for me. And I think being in, you know, in in London in a place that really embraces the humanities, the arts, the theater, um, all of that, it just made it a very natural decision. And I don't think it's one that I would have been able to come to had I not had that experience. And so then I started to think about, you know, really what should I be doing? So for a while I thought I'd pursue a PhD um in literature and actually uh, you know, try to become a professor. And then I realized that I wanted a different type of career to make an impact in a different way. And so initially I started out in the government, um, but then the healthcare focus really came back into effect in say like 2007, um, I believe is when I had the opportunity to join a boutique consultancy that was focused on uh pharmacovigilance consulting. And that's really where I got back into it, like sort of professionally and and and what I knew that I wanted to do, you know, for for the rest of my career.

Brilliant.

Lessons From The FBI Team Mindset

You then had kind of fast forward a few months or years and keep me honest on the timings. But you had a period of your career with the FBI, which I think is a first on the pharma brands podcast of leaders who've had, you know, people who've had entrepreneurial journeys and have come from outside industry and in, but you are the first with a background in the FBI. And we're gonna speak about some of the tech technology companies you've worked with, but yes, tell us about your your time at the FBI and I guess key lessons or the things that really stood out in that experience that that have stayed with you to today.

Yeah, absolutely. So, you know, I briefly mentioned before the government um component of my career. And so um, yeah, after I um uh completed uh graduate school, so I do have you know a master's as well in critical theory, philosophy of literature, um, then I decided to, you know, again, like make an impact other than being a professor. And so at that time uh when I was graduating, I did um, I have had an opportunity um to join um initially the Department of Justice, where I worked for a few years. And then it was actually right around 9-11 um that I joined um the the the bureau and joined as as an intelligence analyst. And uh it was a tremendous experience. Um I I know that I was quite lucky to be selected at that time to join because of the the number and the volume of interest of folks who who wanted to join in that way. Um I remember talking to my section chief about that process and it was it was it was it was great. Um I I had so many great experiences there. I think it's it was really it was really my first professional role where I learned how to be on a team, meaning I learned I I knew how to do good work individually, but I didn't know how to sort of collaborate or strategize with others in the way that I learned to do um on that team. And I, you know, I think the thing about being in a place like that is pretty much you're working with people who are motivated by their individual success, but they're also motivated by what I like to consider like a higher purpose. So everybody is there, you know, truly like right, you know, you're working for the safety, you know, of the country, of the people, you know, of the United States. And when you go to work every day and, you know, you deal with, you know, politics and you deal with things that happen at every organization. But I think people at the end of the day, because the they're united kind of by the difference that they're making by this high higher purpose, it was just a tremendous way to learn um, you know, those those skills. So I I just, you know, I credit that with with with with teaching me, you know, how to be um, you know, collaborative, how to um sort of stand behind your thinking, like how to hone your thinking, why you think what you think, how to present that in a way that you can get others sort of quickly to succinctly understand what they need to understand and make decisions. Um I think those are some of the key key skills that I that I learned there.

I feel like there needs to be, and maybe we could dive, I'll put you on the spot, but it'd be unfair. But a follow-up of, you know, the FBI's probably can't run with this, but the FBI's approach to like healthcare communications or the FBI's approach to winning hearts and minds in pharma, it feels like you have a phenomenal skill set um to win hearts and minds. And and it's so important to articulate the complex and to make it simple in healthcare and pharma is one of the biggest challenges. And it, at least what I'm hearing, is those lessons that were provided at your time at the FBI.

Yeah, yeah, absolutely. I I think that's a really interesting way, way to think about it and way to put it because I mean I I know sort of the survey data, you know, consistently over time in terms of, you know, quite frankly, the the the negative um marks that pharma can tend to get in the minds of of of you know of public perception and things things ebb and flow. Um, you know, I remember during COVID actually, um it pharma received some of the most positive scores ever. Um they're really, really on, you know, on an upswing, but um time passes and people forget, you know.

People forget quickly, sadly. And time moves up. And in the age of AI, and then we're gonna get into some good AI topics in a second, that challenge for farmer and healthcare is even more now. I'm we're seeing from trust and farmers' role to help tackle misinformation, there's some big question marks surfacing around that.

Inside IBM Watson Health’s Rise

So taking a step forward, you also had some time with IBM, IBM Watson Health. At the time before AI was cool. And what I mean by that, like everyone's an AI expert now. AI's the cool kid on the block, or depends how you look at it. But then there were what I call genuine experts who were building, deploying, and selling it. And I as I say that out loud, I feel bad now. It's almost by saying you weren't cool by being IBM first. But do you see what I'm saying? Like, you were doing this stuff that that is becoming the norm to some degree now in other areas, way before Chat GPT was a thing publicly and people were talking about it. I would love like your experience with the FBI, I'd love to learn more about that and hear what you were exposed to and the things that really surfaced at that time.

Yeah, so I think we were, this was in 2016. So, you know, Watson Health um, you know, had had been formed um based on a number of key strategic acquisitions. Um at the time, IBM CEO, you know, really had this philosophy of, you know, data being like oil, right? That was one of the key, you know, tenets underpinning that. And the, you know, the desire to really change healthcare, right? To, you know, to for it to be a moonshot initiative, if you will. And so um, I think there there was so much interest and um excitement about what we were doing. Um, you know, we were for the most part working and a lot of people were headquartered at um at Astor Place, right? In in in New York City was where kind of the hive was for you know, for Watson and for some key members of Watson Health, in addition to Cambridge, right? Cambridge was our headquarters. And you just kind of had this, um it was tremendous. I mean, it was tremendous to be a part of of that energy, of that co-creation. Again, like teams collaborating, um, you know, IBM research, um, you know, IBM offering leaders, IBM technologists, distinguished engineers, you know, ourselves on the, you know, partnership sales, uh, consulting side, like all coming together to just like make this happen and you know, make make things for our our clients. And when I I say things, I mean really, really like transformational enterprise um capabilities. And so uh a lot of the time actually I spent uh in Europe. Um there was so much excitement amongst uh pharma CEOs. And so, you know, I spent a lot of time with them. Uh, the team that I worked with did as well, um, you know, really, really working on um, you know, transformational initiatives. And I know I've said this like probably excited or you know how many times that we've been talking, but it's it's true. It was just it was just so fun. And it was such an exciting time. And I remember I was talking to somebody, I won't say the company, but on the West Coast, I happened to be in Europe and I think it was like two or three in the morning, you know, my time. And the person I was talking to knew that. And they're like, you are so excited for it being like two or three in the morning to be having this conversation with me. And I'm like, yeah, because this is this is what we're doing, right? We're like, you know, we're we're we're co-creating, like we're doing these, you know, these these fundamentally really, really game-changing things with with clients. And so I think the problem, right? And so, you know, some of the lessons learned, right, are that, you know, unlike today, right, the the technology that we were working with at that time, particularly in Watson Health, right? Because there were differences between Watson Health and Watson. And I think that's something that a lot of people don't appreciate, but it's true. And so, you know, particularly, you know, some of the technologies that we were working with in Watson Health tended to be much more um say programmatic, much more, you know, machine learning based, um, in some cases automation driven, right? Rather than, you know, what we would consider to be, you know, generative AI today. And so when you take technologies like that and apply them, you know, in regulated environments like healthcare, with very, very disparate data sources that are not um synchronous and have a lot of missingness and longitudinal issues, et cetera, right? You you have challenges. And ultimately, you know, those challenges resulted in, say, you know, the business not realizing the strategic promise, I would say.

100%. So sorry to interrupt, that was gonna be one of my kind of questions. Because arguably chat G Open AI and and the anthropics shouldn't I mean they could be, but shouldn't have the velocity velocity they have within the life science space when you know the work you were doing so early on was groundbreaking in many areas. And again, like to myself and and the listeners, people probably aren't familiar with the nuances between Watson and Watson Health, as you as you mentioned, and also where they are now, it it seems almost like a Kodak moment of you had all the momentum, you had the market, yet it's it doesn't seem like they they were able to capitalize on the opportunity they they achieved. And again, as a layman, I guess, how are they doing today today by comparison? It I don't hear them mentioned at all um in any of the conversations around AI that we have.

Yeah, I mean, the well, the business has fundamentally changed. So IBM actually, you know, spun it out. Um, and so the data assets, like the core data assets that comprised the key data elements of Watson Health, like the you know, the Truvans of the World, Explorers, et cetera, um, you know, were spun out um as marative. Um, and so that's, you know, the the company as it exists um today. But yeah, exactly that in terms of um it, you know, it ultimately wasn't um, you know, the success, I think that was envisioned, right, by the leadership that was in place in the time at the time, right, when when this business was um you know was launched. And I I I mean I attribute that to several factors, but I think the key thing is that, you know, the technology wasn't there to align with the promise that was created. So um, and there's a couple pivotal moments like this, like so you know, Watson being on Jeopardy. Um but again, right, Watson and Watson Health were different, you know, they were the same branding, but they were different. And also, um you'll you may remember these commercials, right, with some famous celebrities. I often think of the Bob Dylan one and quote the Bob Dylan one as being, you know, kind of a signature moment. Um, but unfortunately, you know, the the the Bob Dylan conversation is much more likely to happen and can easily happen today with with a chat GPT, right? With an anthropic god, right? Not with what we were working with then. And so I think it's a bit of a you know false um promise in some case, right? To think to to think that you know it would be that easy. Yeah. Um, right, because there's a lot of complexities that undermine, you know, transformation, particularly in regulated environments.

You mentioned just then data and dispersed data, unstructured data, lack of longitudinal data, and of course, that's that's the key in all of this for healthcare and for pharma. In

The Persistent Data Challenge In Pharma

your experience and and moving even to today to a point, do you think there's still a fundamental data challenge within the industry? Or is the adoption and excitement around all these new tools and technologies helping to address that? So people are cleaning the data, qualifying it, categorizing it, and making it much more usable to drive that transformation?

Um, it's it's certainly improving, but it's still a challenge. So, you know, even and this is sort of you know the the large, the large company segment of the market, you know, you're for four. In, you know, X hundreds, right? Very, very large-scale companies. Um, you know, I mean, often people don't even know what they have or don't have, right, in terms of data. Um, and it it is, you know, typically in, you know, many different formats, um, you know, many different provenances, many different, you know, systems and on all of that. Um, it is easier to try to um, you know, make sense of that in the world of Gen AI. You don't need to spend hours and hours and uh I'll use the scientific term, lots of money to annotate uh the data, right? To make it, you know, ingestible by um you know, by models. Uh in other words, the process is much simpler now, but it still is it still is a challenge. And so that absolutely remains as you know a concept to be um, you know, worked on, right? Particularly um, you know, when when companies, you know, need to sort of, you know, build, you know, LLMs that can be leveraged to gain competitive advantage, right? In in, you know, in the Gen AI world. So that's when the data constraints, you know, really tend to um tend to show up. So I mean, I am optimistic about it, right, for the future. Um, I know that you know things are improving and will continue to improve. It's not the hurdle that it once was in the sense of like the say extreme nature of it, right? Um that's what I see now.

Really interesting. I I hear mixed mixed depending on the company uh of where people sit and their views to data and quality and its use, and that ultimately ladders up to not on the RD, I guess, to distinction that a lot of the conversations that that I have is on the commercial side, um, not on the RD side. But it it it it ends with them not trusting sometimes the outputs because they they don't have full confidence in where the data's sitting yet. But I I do hear it's a it's moving fast, as is the space. Leads me on to the follow-on.

Escaping The AI Sandbox Problem

Everyone and their uncle, which is a what is a relatively common UK term, actually, everyone and their uncle. Is that something you you use over in the States? Oh yeah. Okay, cool.

Well, it's like everyone and their brother, I hear that's everyone and their brother. Okay, yes.

Everyone's running an pilot in some form, and and all the big models are being embedded, and and as you know, companies have access to now multiple models in some degree in various tools. Um a lot of companies are struggling to make it to scale and to make it everyday use. Some are doing a phenomenal work and are there, but I would say the majority are still in the transformation scaling phase is the read I get, but I'm probably not as close as it. Well, I'm not as close as it as you are. I'd love your kind of your view to that of where companies live on the AI pilot to scale journey. And any recommendations you'd have for companies so they don't get trapped in forever just bringing a shiny new startup in to run a pilot with that never becomes business as usual, or they don't realize the full the full value of what could be.

Yes. So definitely there is a spectrum of maturity here. So, you know, some people, some companies, right, have built um sort of, let's say, enterprise or say function, maybe function is actually the better word, like function-specific platforms that can, you know, really enhance and transform the way that that function is working. Um and I would say that they're in like phase one to two of that, right? Meaning that there is, you know, an initial, initial improvements are there, you know, people are beginning to adopt it as a day-to-day kind of way of working. You know, things are centralized there, they can get insights there, um, and they can use those insights to do work. Like that's kind of what I would consider to be the first successful phase of something like that. But then you have the other end of the spectrum where people are so people in companies, right, can tend to be, you know, quite um say disorganized or have challenges even getting consensus on doing a pilot and then even getting it off the ground from there, like to do the pilot, and then inevitably nothing happens with the pilot, right? And that's kind of wasted money, quote unquote. Um, I mean, there's some learnings, but unless you take the learnings forward, you know, that's not not a good outcome for the for the organization. Now, I think that the people um who tend toward the more successful side of the spectrum um basically avoid the sandbox problem. So, right, the sandbox problem, right, is you have a leader who has an idea, you know, and and and has the ability to, you know, work with a third party or um, you know, do something themselves or or or a combination thereof, um, and and sort of do that. But that can lead to challenges if you don't have um the cross-functional process that goes along with it. So basically, you know, you kind of need to approach this like like you're planning for it to be successful. So, in other words, what are we trying to achieve here, right? How are we gonna know we're successful? Um, you know, let's let's kind of do, you know, two streams of this, right? Where we do it this way, we do it the the what we're trying to test way, right? The pilot way. We look at, you know, what what the model is doing, right? How is you know the model governed? How is the model being operationalized? So you're kind of applying this like, you know, tech or validation and verification process, right, to the actual pilot. And then in doing that, you know, you're bringing the stakeholders along, you're getting them involved, they have a vital role to play in the output so that you know it's being set up to scale rather than just being like done in kind of this corner that nobody's aware of. Um, and then once you can sort of uh take it and approach it that way, where you're also bringing some accountability to what happens and doing readouts, etc., you know, you're kind of setting yourself up for success and scale if there are improvements that can be measured. That is the kind of the key step I see that a lot of people tend to miss.

It's such good advice, and and I hope it resonates. Maybe when we do the edit, we'll just redo that section twice because it's so on point, and it's almost building that end state of how the company will look and operate with this new way of working and new technology without focusing it on a three-month or six-month pilot to see if it works. And and one of the things to build on, we were having a conversation with a startup that's that's doing something truly transformational, and they struggle as they they struggle to take on pilots because of the cost to work with a company, to deploy it, to get it set up. And they're actually at a point where, unless there's that long-term plan and that cross-functional integration, it doesn't really make sense for them to invest three to six months knowing that it may not go anywhere. They need to know what the long-term plan could be, which of course any commercial company would want. But at any innovative things, it just seems crucial to map that out. So I think it is great advice and yeah, well shared.

AI That Improves Clinical Trial Access

Moving the conversation from pilots to scale and to data and moving all the way down the line to patient outcomes. AI arguably, and the use of AI today, gen AI, and the the accessibility of it in that we the the adoption, of course, is through the roof in most markets. Where do you believe the greatest potential exists to genuinely transform patient outcomes? And I'm kind of giving you complete free reign here from talking about therapeutic areas and look at rare or certain technologies or certain parts of the patient journey that are just ripe for complete disruption or evolution.

I really think that so much work that humans do today can be augmented in really, really transformational ways by AI. And I think we're really only at the beginning of this. And so specifically, you know, for patient outcomes, um, I think about clinical trials and because I spend so much of my day-to-day there, but you know, fundamentally, and maybe this even gets back to what we were talking about before with like the trust and the public perception challenges that pharma ebbs and flows through, is for whatever reason, you know, we don't treat clinical trials as um something, and I'm gonna use a word purposefully and I'm gonna qualify it, but something to be marketed. And what and what I mean by that is that there are very, very clear inclusion and exclusion criteria for clinical trials, right? They are driven by scientific rigor and they're driven by a very, you know, um, say, let's call it right, ethical process around enrolling patients. But the the side of the coin that we're not really talking about, I think, enough is clinical trials now, particularly for, you know, oncology patients, you know, provide access to life extending therapy. And this this makes a difference for people. And the way that the system is set up now is when people are getting care or check-ins, you know, be it with um, you know, sort of their care teams, um, or other physicians who are not the primary, say, oncologist who may be treating them. And I'm just picking oncology here. Um, but, you know, those oncologists do not know about trials that their patients may be eligible for because it's just not something that, you know, is part of their day-to-day workflow, right? They have their job to do with their patient in front of them, and they're not necessarily aware of the broader landscape of trials that are available. And I think AI could really, really help to fundamentally change this. Um, this was a use case that we were working on way back in the day, um, you know, in Watson Health, but I believe in it so much because you can use AI to fundamentally make care more accessible to people because of awareness. And in turn, right, sites who are running clinical trials can make those trials more publicized. So that's why I'm saying, you know, marketing, not in the sense of, you know, enticing people who are not eligible, because that's that's not the point, but the point is to make the the trials that are available more accessible and more top of mind so that patients who are eligible can be connected to them. And making that whole ecosystem work better, I think could fundamentally transform patient outcomes. And then what I would say is you work backwards from there. So, for example, if in the day-to-day work of the clinical research associate, if they could leverage AI to help free up some of the tasks that they that they have to do now, where they have to spend a lot of time manually reconciling things and completing forms and that that type of work, if that can be drafted and handled by AI, which frees up the CRA to be visiting with PIs and with potential patients more and consenting those patients who are eligible for trials. Like that is, you know, the game-changing thing. So now you think about the future where you know you have clinical trials top of mind for care team members who can make recommendations. The patients can then more easily consult with the potential PIs and find out if they're a fit. And then if they are a fit, get that match with the clinical research associate to be consented. Um, I mean, that could dramatically increase the percentage of um eligible patients who participate in clinical trials. And that to me is what it's about. Because at the end of the day, you know, we, you know, that that's how that's how we get patients more time, right, with their families. Like that's at the end of the day, what matters.

1000%. I'll I'll put you on the spot, and I know when we didn't speak about this before, but there are any companies or or and it's fine if you you don't want to mention anyone, but any in really interesting companies that are making Segway big dents in this space, and I know you know ambient scribe technology note-taking, then EHR integration. There are things like that surfacing, but are there any that you're really impressed by at the moment that are helping in in the areas that you've mentioned?

Um there there certainly are. There are about, you know, say three three companies that that come to mind. I I would I would shy away from mentioning them just because I think while their technology has has promise, at the end of the day, it has to be able to be implemented better at sites. And sites struggle to do that because of budgetary concerns. It comes back to the kind of the funding cycle of clinical trials.

Yeah.

Where where sites themselves, you know, don't often have, say, I'm gonna call it the the master budget, right? At the IT level to choose a technology partner to come in and do that work. Like that budget is just simply often non-existent. And so they have to rely on, you know, solutions that you know sponsors can help provide, um, which becomes a piecemeal patchwork of I'm gonna call it non-success. Yeah. And that to me is what needs to change. And so if you get sites um, you know, in a in a network where they can ascribe to common platforms that help to speed these things up, that is what I think is the, you know, the the difference maker. And like I said, there's like three top companies that come to mind who are making strides in doing things like this, but the real key to unlocking it is getting those sites like as as clients.

It feels like a great follow-on webinar. We do some vetting and invite those companies and get into the nuts and bolts of that. Because as you say, the initiative is so on point and it's needed, and it it's having these conversations and surfacing these great innovations and entrepreneurs to create the change. And sometimes it is as simple as you know, back to the FBI points of great communication, great teamwork to drive these things forward more than anything else to win hearts and minds. One

Career Advice For The AI Era

of the big things in the age of AI in which we we live is jobs and careers and the impact on jobs. And and it's it's playing out, I think, on in the States, in on your side of the pond and on ours and in Europe, especially for new grads and people coming into the workforce. A lot of us have been impacted post-COVID, remote working is now the norm, which is benefits and limitations, um, not being as close to people and from a proximity standpoint. You've had a great career, uh, having a great career, rather, uh, in different areas, you would have collaborated and worked with lots of amazing people along the way. Everything you've seen and experienced and we've discussed in the age of AI, what what advice would you give to people starting out on their careers at the moment with that backdrop or that kind of scene setting?

I think the key is is to be, if you can, um quite purposeful about skills that you want to build and think of your career in those terms rather than you know working for Company X because of Y reasons. You know, I think it has to be much more an intentional journey around who you want to become and why you want to become that, and understanding that um that may change over time, and that's okay because the skills that you build along the way, you'll be able to apply them in you know in in in new ways. Um I think you know, we're really at the entry, the early stages of how the entire world is gonna change over the next, you know, 10 to say 20 years, maybe 15, 10 to 15 years. And I, you know, I I think there's so many things that are gonna happen. I mean, you know, we're already seeing uh universities uh, you know, fundamentally change um what they're doing and why they're doing it, and you know, downward pressure on tuition prices and graduate school costing, particularly in the US, right? That's indefinitely, you know, more of a uh a US phenomenon in terms of the the costs of higher education. But um, you know, all of that, all there's you know, all of that will be will be um, you know, say what's the word I'm looking for, um, like depositioned, maybe, you know, or or you know, sorted, right, over the next, you know, over the next um, you know, say decade or or or less. But no matter no matter how those things evolve in real time, right, people are going to be, you know, going through institutions. They're going to need to be learning. And the thing that I've always been very um hesitant about is conflating higher education with vocational training. Like I know a lot of people think that you go to college to get a job, but you don't. You go to college to learn who you are and who you want to become, and why you think the way that you think, and what skills you want to acquire when you leave the walls of that university. And there are skills that you can acquire along the way, but specifically technology skills, right, will be obsolete. I mean, you know, did we all or right? People, you know, they learn C plus basic, right? I mean, like, you know, uh, so people, you know, want to learn, you know, um, about AI now, right? They want to learn, you know, a particular coding language or, you know, a particular platform of skills. Those things will change, right? But learning, so you know, but learning how to think and learning those fundamentals of the how, that's what, you know, to me, the university is for. And so I think that's very, very important to keep in mind.

Um I sorry to to jump in. I love that you know, college is about who you want to become. I think that's such a powerful message, you know, not necessarily who you will be through what you learn, but who you want to become is is great. And you're speaking about skills, and it was only 10 minutes ago, prompt engineering was a thing that people thought they had to go out and learn, and then that's not a thing anymore. Like it passed pretty quick.

Exactly.

Amazing.

Rapid Fire Wrap Up

Um Tara, I'm conscious of time and we're coming up. I I threw something in at the end, which I hope you can entertain for us. It's something you were trying as a bit of fun. So I'm gonna throw out some words, and I'd love for you to just say the first thing that comes to mind um a single word or a couple of words as we wrap up the episode. But would that be okay?

Sure.

AI.

Transformational.

Patient data.

Disparate.

Behavioral science.

A fundamental requirement, a fundamental requirement for every human to understand. Pharmaceuticals, life-saving, patient centricity, a value proposition that is unfortunately unrealized today.

Brilliant. Thank you so much. And the behavioral science thing, I feel like we I just scratched the surface with you. Um much more to talk about there. That was a brilliant response. Um that's it. Tara, thank you so much for your time today. Um, I think that will still be on the line. Great. Tara, thank you so much for joining us today. It's been an absolute pleasure speaking with you.

Thank you so much. I I agree. It's been really fun and I really appreciate the time. Thank you.