AI-Driven Healthcare: Moving Beyond Analytics to Actionable Insights

[00:00:00] Gordon Wong: Welcome to Hard Problems, Smart Solutions – the Newfire Podcast, where we dive into complex challenges and ways to solve them with top industry leaders. I’m Gordon Wong, VP of Data and AI at Newfire Global Partners, and your host for this episode. In each episode, we bring you conversations with top innovators and decision-makers, tackling the biggest issues across industries.

[00:00:27] Gordon Wong: Whether you’re looking for insights to drive your own strategies or to learn from the best, you’re in the right place. Let’s get started. Hi, everybody. Welcome to the first episode of Hard Problems, Smart Solutions, the Newfire podcast. Today, I’m thrilled to introduce our guest, Emily Lindemer, Executive Director of Data and Healthcare Innovation at JPMorgan Chase Co.

[00:00:47] Gordon Wong: Emily has led data science initiatives at Cityblock Health and Wellframe, focusing on innovative AI solutions to improve healthcare. She holds a PhD in medical engineering and medical physics from the Harvard MIT Division of Health Sciences and Technology.Emily has been instrumental in advancing how we use data to improve healthcare outcomes.

[00:01:04] Gordon Wong: This conversation is especially timely, as many organizations Including us and our clients at Newfire. We’ll look at how AI can move the needle from analytics to actionable insights in healthcare. Welcome, Emily.

[00:01:15] Emily Lindemer: Thanks, Gordon. Great to be here. Excited for the conversation.

[00:01:19] Gordon Wong: Yeah, me too. I’ve been looking forward to this for weeks. Um, so a little bit of background for those of you who are listening. Emily and I actually have crossed paths multiple times within the Boston healthcare ecosystem. Uh, we’ve actually worked at some of the same firms, we’ve consulted with each other as colleagues. And so I have a lot of respect for Emily.

[00:01:38] Gordon Wong: So I’m, again, I feel really privileged to have this opportunity to have this conversation with her. And so today’s topic is, you know, when we were looking for guests to explore this topic, Emily is really one of the first people that came to mind, because of her focus on data science and AI and really, Impactful, actionable insights.

[00:01:56] Gordon Wong: Today, we’re going to dig into AI’s role in healthcare transformation. Right, so Emily, you’ve been at the forefront in this industry for a while, and arguably, your entire career in education has led to this point. So, how do you see AI evolving from really just analyzing data and producing descriptive analytics to driving actionable business-focused solutions?

[00:02:16] Emily Lindemer: It’s a great question, Gordon. This is one of my favorite topics and probably one of the things I get asked about the most. Before I answer, let me give a little bit of an overview of kind of like where my journey in AI healthcare has been just for listeners to see kind of like what I’ve seen. Um, I, I started my career in the imaging world, which is where AI, I think, really started showing its first promise in healthcare. Um, it is the perfect application of AI images are, um, you know, we think back to, like, Identifying cats versus dogs, right? Like the early, early applications of AI that were across the world. Like you can apply them to medical images as well. Um, and so I started in neuroimaging and one of my first jobs out of grad school was working at IBM Watson Health,

[00:03:06] Emily Lindemer: where I was part of their AI imaging team trying to make new algorithms that we were bringing to market, um, in mammography, chest CT. My career slowly transitioned out of imaging more to broader health systems, more broad healthcare applications, kind of working with digital health companies, working with, uh, providers like Cityblock, where Gordon and I crossed paths, um, and really kind of seeing more at a global scale rather than singular applications, how AI, and just data science in general was evolving across the industry.

[00:03:36] Emily Lindemer: And so just to say, I did all of my training like way before LLMs came out. Um, there is, we are in a new generation of AI when we’re thinking about LLMs and generative AI and, um, and so I have a lot of thoughts there. I think that where we are today is that there’s a lot of excitement about AI. People are making discoveries, training algorithms to be incrementally better than the last and sometimes more than incrementally at like light speed. But if you know healthcare, you also know that healthcare kind of like exists on somewhat of a delayed timeline when it comes to data and technology. A lot of our data and infrastructure in the industry isn’t there yet to support these super advanced technological applications.

[00:04:23] Emily Lindemer: So, I am kind of coming at this conversation from a place of, um, I’ve kind of, I’ve seen under the hood of what our data infrastructure often looks like, and, and I’m coming at it with this view of a little bit of skepticism, honestly. So I’ll, I’ll give you that as sort of a backdrop for my perspective.

[00:04:43] Emily Lindemer: And it really is just my perspective that I’m sharing here. So let me, after I say that, let me give the question back to Gordon and kind of where do you want to go with that?

[00:04:54] Gordon Wong: Yeah, absolutely. So that actually resonates with me quite a bit. Um, because as a, uh, someone who’s been building data platforms for more years than I care to count, um, we’ve been asked do many cutting-edge things, but frequently the organizations are not ready for it. So I use the term pragmatic cutting-edge a lot, and I suspect that might resonate with you a bit.

[00:05:14] Emily Lindemer: I love that.

[00:05:16] Gordon Wong: So let’s, let’s really drill into that then, right? So we, we are really looking for, you know, solving for actionable business problems. Right? So, you know, what, what, where are you seeing progress in terms of you using AI to actually move the needle?

[00:05:32] Emily Lindemer: So I think about AI and healthcare in like these two broad camps. There’s administrative and there’s clinical. Um, and people are interested in both and there’s really, really important applications in both. If we start with administrative, administrative are the things like, rev cycle management, billing, coding, um, even just scheduling patients, like, all of, all of these things in healthcare that aren’t directly caring for the patient, but cause a lot of burden on the industry. Clinical applications are the things that I think we think about as like these really sexy, like, going to move the needle and change patient care. Those are things like automatically diagnosing a patient or, you know, prescribing like the absolute best treatment regimen based on a patient’s history.

[00:06:19] Emily Lindemer: Um, I think that where we are today is really just the first camp. The real promise and the early promise of AI and healthcare is going to be on that administrative side. I say that, and it’s maybe not as exciting, like, Oh, why can’t we do like these crazy futuristic things? I’ll get to that. I think the industry is just not there yet, but I also wanted to say with positivity about, or speak with optimism, I should say, about those administrative tasks and the promise there. And the reason is that part of the reason that the US healthcare system is just so complicated, so expensive, so burdensome, is the tremendous amount of administrative burden that we have placed on it.

[00:07:01] Emily Lindemer: And so actually using AI to alleviate some of that burden is a great thing. And I think will actually pay really meaningful dividends in the end for patients, providers, the system at large. Um, so I can go into details there, but just to say, like, I think about those two camps, and I really think about the administrative side is where we’re going to see change in these next few years.

[00:07:25] Gordon Wong: I, you know, I actually really agree with you. I see that, I, I see that too. And I’ve, I’ve, I’ve seen the statistic that perhaps something like 40 percent of our healthcare spend goes into administration. So it feels like of opportunity there.

[00:07:37] Emily Lindemer: That’s right. And I mean, you have to, I think, believe that a lot of things will chain together and, and lead to that trickling down to patients having cheaper and more affordable care.

[00:07:51] Emily Lindemer: But that’s what I think the goal should be. As an, or as an industry, when we think about, okay, we’re going to really tackle this administrative problem with AI, I think that we should say more than just: let’s make the burden lower. I think that the goal should really be, and make it cheaper for patients. Make the whole system, patients included, less burdened by this extreme cost.

[00:08:13] Emily Lindemer: Because you’re right, it is up to 40 percent administrative costs in some cases.

[00:08:18] Gordon Wong: So at Newfire, I’m heading up data analytics and AI. So I have the opportunity to speak to a lot of our prospects in terms of how to use AI within their organizations. And so if, you know, what’s your guidance to them, like what problems should they look at first in terms of using AI to, to improve things?

[00:08:38] Emily Lindemer: Um, so my advice there is, I think this is an obvious question for a lot of business leaders, but it’s worth saying is, you know, what is the ROI if you solve this problem? Um, a lot of people, like I said, they really want to tackle these really exciting clinical things, and I don’t want to discourage from that, but we actually don’t know what the ROI is often of solving some clinical problems with AI. I think that the ROI is much more clear for some of these administrative. So if you’re a business leader, I mean, that really should be the first question you’re asking yourself. Um, you know, the second question is kind of this technical feasibility question, right?

[00:09:15] Emily Lindemer: If you are thinking of building and selling an AI product as a company, you have to think about do all of your customers have the technical infrastructure to support this? Do they have, like, the data to support this? And that is often not the case right now in healthcare. There are a lot of health systems out there who are, like, just migrating to the cloud, you know. They, they don’t have the tools to load all their data into some kind of AI algorithm and get an answer back. Um. So that’s the second thing. I think the other thing is generalizability, you know, is what you’re building something that can generalize, can safely exist outside of some very, very small testing grounds that you’ve built it in.

[00:09:59] Emily Lindemer: That’s a trap that a lot of people fall into, I think, with AI, is they can build something that performs amazingly, and then they take it and they try to have it perform somewhere else and it doesn’t do as well, and I think the trust starts to really erode in the industry, um, with AI for those reasons.

[00:10:16] Emily Lindemer: And I think the last thing is, like, keep an eye towards the regulatory concerns around what you’re trying to build. Those administrative tasks that I mentioned are often not so, they’re not so prone to regulatory challenges, whereas the clinical side of things, that’s a big hurdle to get some kind of AI cleared by the FDA for actual patient use in the clinical setting. So, you know, keeping those things in mind, I think, is really critical.

[00:10:42] Gordon Wong: Now, acknowledging that healthcare companies, uh, come in lots of different sizes and different flavors, but I think about Fisher Price, my first AI project. Um, which C-level officer would you typically think would be like a good first customer for a user AI?

[00:10:58] Emily Lindemer: That’s a great question. I have, um, I’ve been talking to some folks about this recently, and I was just invited to, like, a roundtable to listen to chief information officers and how they’re being approached about AI. And interestingly, I think what’s happening right now is I think it’s the CFOs of most organizations who are being approached about AI and the decision-makers and that comes back to that ROI question and the fact that a lot of these are really being targeted for a lot of these applications are being targeted for administrative tasks, internal efficiency gains, things like that. So what you might think of as like these CIO, CTO folks who are first approached decision-makers, I think we’re actually seeing that it’s more people on the financial decision-making side.

[00:11:46] Gordon Wong: Yeah, that, that resonates for sure. You know, part of the purpose of this, uh, this podcast is really to give our listeners some ideas of where they can get started, right? Pragmatic advice. In that vein, do you have any specific examples from your recent history where AI has made an impact in terms of patient outcomes or operational efficiency or just reduction of toil?

[00:12:06] Emily Lindemer: Definitely. So some of the applications that I’ve seen that I think have been really exciting in AI, that are, they, they just are adjacent to clinical, but they really are administrative applications, are things that help reduce the burden of clinical documentation. So, really solving this problem of providers are getting burned out, they’re spending way too much time having to write up patient notes, sift through patient notes and synthesize past medical information about a patient. When you kind of boil that down, those are information synthesis problems, which is what AI is really good at. The applications that I’ve seen that do things like ambient documentation.

[00:12:48] Emily Lindemer: So for example, a voice recording, just like you and I are doing right now, of a patient-doctor conversation, which gets translated to text. That’s easy. That’s been around for a while, but is then with an LLM translated into medical jargon that can go into a patient’s chart. That’s incredibly powerful.

[00:13:08] Emily Lindemer: That, you know, that saves the doctor so, so much time. And I think I’ve seen amazing reviews and feedback from clinicians themselves on how powerful that type of technology is going to be in the clinic. I’ve also seen a lot of applications of AI that I think are really promising from like an investment and investability perspective of revenue cycle management, billing and coding.

[00:13:31] Emily Lindemer: Those are heavily manual processes right now that are prone to error that I think AI is excelling at. And then this is my favorite example, just kind of throwing back to my imaging days. This is not AI the most recent example that, I encountered this years ago, but I remember at an organization I was at, we were working on really advanced, really cutting-edge neuroimaging where we were developing technologies that could, like, automatically segment images of the human brain into all of these amazing, very descriptive statistics.

[00:14:05] Emily Lindemer: And it was hard to sell that because it was hard to tell a clinician why this would help improve patient care or like why this would save them money for many nuances related to like human brain stuff. But um, it was amazing technology that couldn’t find a home. Conversely, there was this technology coming out at the same time that could take a cue of like a hundred images in an emergency room and automatically identify if any one of them was a brain bleed. And just for those listening who don’t know, almost any of us could be trained to a spot a major brain bleed pretty easily.

[00:14:45] Emily Lindemer: It’s a very visually obvious thing. But so what this algorithm did, it wasn’t like finding some really hidden issue, but it would find something that was incredibly emergent that might be really low in the radiologist’s read queue and pop it to the top and say, basically, if you don’t read this image, this patient might die in the next hour.

[00:15:07] Emily Lindemer: And that’s like an orchestration task, right? That’s like a clinical workflow task, but it was such a better at the time application of AI, if you really wanted to, like, help patients. So I just put those two side by side. It’s one of my favorite examples here of, you know, sometimes the simpler solution, the better one.

[00:15:26] Gordon Wong: I love that. I love that. I mean, it reminds me that we should be always thinking about kind of the basic variables that drive outcomes, right? Time, timeliness, reduction in effort, all these things, right? So, you know, uh, stealing from the product manager book, so looking at your typical, let’s say healthcare provider, what person or role would you want to target to, uh, delight with AI within that organization? Whose job do you make it easier? How do you help some be more successful?

[00:15:54] Emily Lindemer: That’s such a good question. So, personally, I think we should be really making things for patients and physicians, like, the people delivering and receiving care and physicians need so much tooling like this to help with burnout.

[00:16:12] Emily Lindemer: So I think delighting the physician, the ambient documentation, question or example I gave is perfect there. However, usually physicians are not the financial decision-makers, they’re not purchasing your product. And so, I do think that you really have to be able to always show delight to the customer that’s paying in the end.

[00:16:31] Emily Lindemer: And so, being able to say to them, this is going to allow your physicians to spend X amount more time with patients, or, show some real financial ROI there is really, really important to keep in mind.

[00:16:46] Gordon Wong: Yeah. Thank you for that. I, really, that really does make a lot of sense to me. You know, I personally have always loved the lens of like trying to delight somebody because we could understand that. But let’s take a, let’s get a little speculative for a second, right?

[00:16:58] Gordon Wong: Think about the future AI in healthcare. What do you think, what are you excited about? What do you think are the big opportunities and challenges we might be able to tackle?

[00:17:10] Emily Lindemer: So what I’m excited about, this is, this is a hard question. I’ve actually been asked this a few different times and I’m always kind of evolving my perspective, I think, on a regular basis.

[00:17:22] Emily Lindemer: Let me zoom out before I answer what I’m most excited about and tell you what I think some of the biggest problems are and we’ll, we’ll work backwards.

[00:17:29] Gordon Wong: Always lead with problems.

[00:17:32] Emily Lindemer: Some of the things in healthcare that are just, in my mind, so broken and in need of change really do come down to what we started talking about was data infrastructure, right?

[00:17:42] Emily Lindemer: Data fragmentation and data infrastructure. This, like, shows up in a lot of different ways. This shows up as technology not being able to scale across organizations, technology not being able to generalize. And one of the things, I think, that’s becoming more and more apparent in the US is kind of health disparities across the country.

[00:18:04] Emily Lindemer: There are growing parts of America that are losing doctors. What are those people doing? Like, that, that, that’s going to cause more chronic conditions and everything. And so I think when we think about like, where do we really want to be, like, where’s technology really, really going to help us in the next 20, 30 years?

[00:18:25] Emily Lindemer: Personally, I’m not sure that it’s going to be in these super futuristic things of like, doing a full body scan and finding like the one cell in your body that is potentially cancerous that I think is like a lot of the sci-fi things we think about. I think the promise is really, how do we get equal care to people that live in every single setting in America across the whole country when there’s obvious resource deficiencies and the supply and demand is just like not the same everywhere? So if someone can figure out how to fit AI into that problem, like, that is what I think will really, really drive change.

[00:19:02] Emily Lindemer: Really lofty goal, but it’s probably the thing I think about the most when I think about AI and tech making a real impact.

[00:19:09] Gordon Wong: So, to put words in your mouth, I think you’re reminding us that AI is a tool, not the end, the outcome.

[00:19:16] Emily Lindemer: Yes, I think that’s a great way to put it. I mean, there are so many healthtech and healthcare companies out there right now that are using AI internally, but they’re not like AI companies.

[00:19:27] Emily Lindemer: And I think that is the right way to be thinking about it. You know, use AI to get done the things that you need to get done and do them better. But sometimes when we have a hammer, everything looks like a nail, and we, you don’t need to become an AI company to really change healthcare.

[00:19:43] Gordon Wong: Let me share with you some, I’m seeing a little bit, I’d like to know if you’re having similar experience.

[00:19:48] Gordon Wong: So, you know, in Newfire, we, we do talk again, we talked to a lot of clients about implementing AI. I’ve, I’ve noticed a funny thing, it’s because you mentioned data foundations and fundamentals that made me think about this, was that as we talk about AI, frequently the conversation starts becoming like, oh what’s the state of your data platform, your data warehouse, your data quality, and it’s driving more awareness of these systems that have been languishing for a long time. Are you seeing the same thing?

[00:20:14] Emily Lindemer: Yes. I think that that’s a great observation. People are, to your point, like, have kind of allowed their systems to deprecate It just not be as advanced anymore. And now they’re like, Oh no, I can’t, I can’t apply the cutting-edge technologies. And that’s okay. I think that this is a great impetus for us to all look and say, how do we build a better foundation?

[00:20:36] Emily Lindemer: So we definitely are seeing the same thing. And that’s why the problems that excite me are actually really these infrastructural problems and solutions so that we can enable these things that are much farther down the road.

[00:20:52] Gordon Wong: So let’s take some of the implementation challenges off the table for a second.

[00:20:56] Gordon Wong: Where do you wish you could build an AI solution? Well, what problem do you wish you could address with AI?

[00:21:03] Emily Lindemer: That’s a really good question. I will, I’ll go back to what I said about like this really future vision. I wish that we could figure out, and I mean plenty of people are working on this just to be clear, and I think plenty of people like share this view, this is not something I just thought of, but this idea of like a person who lives in rural Arkansas who has a stroke and cannot get seen by a neuroradiologist in time. Yet neuroradiologists exist in this country who could read their scan and advise, bringing expertise to places where it currently does not exist is where I would really want to focus my time in like an ideal world. And it’s not just telehealth. It’s not just like connecting, uh, a radiologist who’s remote. It’s maybe it’s not even connecting a radiologist, maybe it’s actually making an AI that can automatically read that image, which people are working on.

[00:22:04] Emily Lindemer: Maybe it’s creating better surgical techniques and tooling and suites that do more robotic tech surgeries, right, that don’t need as much human expertise. I don’t know. But I do think that the thing that’s most exciting to me is like fixing this supply chain issue.

[00:22:24] Gordon Wong: Thanks for sharing that because it makes me, it allows me to sort of categorize, um, you know, something we, we see a lot at Newfire.

[00:22:30] Gordon Wong: So we have the privilege to work with a lot of healthcare startups. And I realized what a lot of them are trying to do is bring expertise to the problem, right? They’re trying to solve for this expert gap and they’re trying to bring it to the patient, which to me, intuitively feels like the right thing to do.

[00:22:44] Emily Lindemer: I totally agree. Yes, I’ve seen some amazing healthtech startups out there that are, this is exactly their model, is you know, there’s patients out there that need a certain type of care and either the place that they live or the insurance coverage they have, or maybe both prohibits them from getting those things.

[00:23:02] Emily Lindemer: And I mean, and even at Morgan Health, we have done research and we’ve learned that physical lack of access to care is a key driver of poor outcomes and, you know, misdiagnoses more so than not being able to afford care, which is interesting because sometimes we think of them as being the same thing, but physical access is different than financial access.

[00:23:26] Gordon Wong: I have colleagues in Canada who are in healthcare and they are trying to tackle this problem is very, yeah, a front-of-mind for them because of course how disparate Canada is in terms of geography.

[00:23:37] Emily Lindemer: Oh, that’s really interesting. Yeah. I’m sure it’s an even bigger challenge for them there.

[00:23:41] Gordon Wong: Particularly around things like LGBTQ care, just having a primary care physician, if you’re in northern British Columbia.

[00:23:48] Emily Lindemer: Yeah. I mean, and you know where we see this a lot actually is, is mental health in the US too. There are, there’s kind of like a dearth of local mental health providers who will take insurance, and then when you talk about specific populations, like LGBTQ populations, it’s like you have an even harder time of finding somebody near you in your network. And so connecting expertise across like geographies is huge.

[00:24:15] Gordon Wong: Emily, similar to me, you’ve been both a provider of technology, also a consumer of technology.

[00:24:22] Gordon Wong: So let me give you an opportunity as a customer and a consumer of technology, of solutions, what do you want companies like Newfire to build? How can we make a bigger difference in this industry?

[00:24:33] Emily Lindemer: Scalable data platforms. I think something I have experienced, and you alluded to this earlier, is so many health tech companies are out there, and this is, completely not a knock on them.

[00:24:45] Emily Lindemer: I I’ve lived it. I understand why these decisions are made, but so many health check companies are out there building internally from scratch. They, they’re coming in and they’re saying, how do I build this, like, bespoke something, data platform, data warehouse for my needs. And, as I said, there’s many reasons why people choose to build versus buy, but it really, I think, is not always a good long-term investment. And I, I wish that there were an entity out there, and maybe it’s Newfire, who could build more kind of scalable data platforms so that these early health tech start, health tech startups that have a lot of promise don’t actually need to burn all that capital building what they think they need to do so bespoke.

[00:25:34] Emily Lindemer: Um, I could, you know, we could do a whole podcast on build versus buy data warehouses, so I won’t go further.

[00:25:41] Gordon Wong: I, I, I think that’s that, you know, and I do think the, the world is moving in that direction. That’s one of the benefits of having gone remote. Of course, there’s a lot of costs having gone remote, but there’s an opportunity to pull in expertise of all types from around the world and, you know, in different time zones, um, you’re starting to bear some fruit.

[00:25:58] Emily Lindemer: Yeah, yeah, definitely. I mean, the world has changed quite a lot. I will say I do so many people are still, you know, building the same thing in like silos.

[00:26:11] Gordon Wong: Well, so Emily, do you have any other, uh, any, uh, final words you want to share as we wrap this conversation up? 30 minutes goes by really fast.

[00:26:20] Emily Lindemer: It goes by so fast. I would say I, I hope everybody like stays excited, stays hungry about all of the amazing opportunities there are right now for AI and just data in general and healthcare.

[00:26:33] Emily Lindemer: I’m a, I’m a first principles type of woman. I think a lot about like, “what is my base case?” anytime I’m trying to solve a complex problem. And I think approaching things that way when we’re thinking about these like lofty, complex technology problems is really helpful and grounding and sort of actually driving more effective solutions early on.

[00:26:55] Emily Lindemer: And, often your base case is going to come down to infrastructure, which we talked about a lot. So that’s my, my parting wisdom is to try to approach problems in that way.

[00:27:04] Gordon Wong: Yeah, this is not a, uh, this is not a 30-day mission, right? This is a lifetime mission and none of us can do it by ourselves.

[00:27:10] Emily Lindemer: Yes, definitely. I, I think that state working in healthcare data is, um, there will always be work to be done for sure.

[00:27:19] Gordon Wong: Well, Emily, thank you so much. This has been an awesome conversation. I really hope that our listeners have gotten something out of this. I think some of the topics I’ve heard were AI is here. It’s making a big difference, but you need to be careful about what problems you tackle.

[00:27:32] Gordon Wong: Don’t be afraid of focusing on efficiency as that may have the biggest impact on your patient outcomes. And finally, look for partners who have the skill sets to help you be successful like our clients at Newfire have.

[00:27:43] Emily Lindemer: Yes, that’s great. Thanks Gordon. This was a great conversation.

[00:27:47] Gordon Wong: Thanks Emily. I hope you have a great day. Cheers.

[00:27:49] Emily Lindemer: Thanks, Gordon. You too.

[00:27:52] Gordon Wong: Thanks for tuning in. We hope today’s conversation with Emily Lindemayer, Executive Director of Data Healthcare Innovation at JPMorgan Chase &nCo. has given you a fresh perspective on how AI is transforming health, starting with the foundational need to streamline data and reduce administrative burdens.

[00:28:06] Gordon Wong: Stay tuned for more episodes where we continue to explore the toughest challenges and smartest solutions in business and technology. Like and subscribe. Until next time, keep innovating and solving the hard problems. This is Hard Problems, Smart Solutions, The Newfire Podcast.