In this EdTalks episode, host Edward Marx interviews Susan (Susa) Visuri Monacelli, strategy and business development leader for Philips Connected Care, and David Lazerson, CEO of Breia. Susan outlines Philips’ vision for AI in connected cardiac, sleep, and respiratory care, emphasizing that future care delivery will be AI‑driven. She describes a three‑layer collaboration with Breia: (1) secure, on‑premise data access without extraction, (2) creation of common data indices and a shared language to enable queries across clinical, business, and product development teams, and (3) generation of actionable insights that accelerate evidence creation, decision‑making, and patient care. David shares Breia’s background—originating from cybersecurity, moving into healthcare, and building an AI platform that ingests heterogeneous clinical, real‑world, registry, claims, and unstructured note data. He explains how Breia has helped pharma clients with real‑world patient journey mapping, trial design alignment, epidemiology, safety analysis, post‑marketing (Phase 4) studies, and label expansion, turning raw data into regulatory‑acceptable evidence. Both guests argue that AI is the catalyst that moves healthcare from data scarcity to data‑rich evidence generation, and that the same capabilities can be transferred to med‑tech, where device‑generated data offers a fertile ground for AI. Susan stresses speed and liquidity—rapid evidence generation and decision‑making—as the key value of the partnership, while David notes that AI is empowering smaller biotech and med‑tech firms to achieve what once required large pharma resources, predicting eventual widespread AI adoption by big device manufacturers.
Chapters
Hey, welcome to another edition of Ed Talks. I love talking about all things AI, but with actual evidence of how it's really working and moving organizations forward. And so I have 2 great guests with me today, my friend David and Suza. Welcome to Ed Talks.
Thanks so much for having us. Thank you.
Glad to be here.
Yeah, Suza, let's start with you. Assuming our audience doesn't know who you are, tell us a little bit about yourself. Then David, we'll go to you. And then have you explain kind of this collaboration, and then we're gonna dive deep.
So I'm Susan Monacelli, and I lead our strategy and business development here at Philips for Connected Care, which covers both hospital and ambulatory cardiac care monitoring and diagnostics, as well as sleep and respiratory care. Obviously, the topic of today is related to AI and how we're embracing that. And honestly, as we look forward into the future, There really is no path that doesn't embrace AI in some form or fashion. A lot of it now is operational, but in the future, we hope that it will also change the way that we are able to deliver care to patients. And so that's my passion and a little bit about me. So I'll pass it over to David.
Thank you, Sousa. Hi, my name is David Lazerson. I'm the CEO of Breia. I started my professional career at the elementary school doing cybersecurity and data. I spent in this field couple of decades before the decision to switch to healthcare. It came with the understanding that healthcare is a little bit behind some other industries, and there is a lot of opportunities to apply cutting-edge technologies where they can make the most impact. So around 7 years ago, switched to healthcare, uh, at some point started Breia and, uh, running this company for the last few years.
Yeah, no, that's super exciting. And tell us a little bit, Maybe, Suza, tell us a little bit about the collaboration between Philips and Bria.
Yeah, so what we're really looking to do is make it easier to access data. And because AI is so heavily dependent on that data access, but then you start to build layers on top of it. And what we're looking for in our collaboration is, okay, so great, we can get access to data, secure it, work comfortably with our partners in their environments with that data, not necessarily extracting that data to other environments. But also then layer on it indices that allow us to create a common language that allows more and more people within our organizations to access that data, to run queries, to create insights that help not only our clinical publishing arm, which is where the obvious connection usually comes to, but also helps our business owners and our product development teams think about how they can utilize and query the data in a more accessible way. So to me, that's kind of the second layer. And then the third layer becomes that insights layer. How do we turn these elements into real actionable insights that change the trajectory of the business and change the trajectory of our patient care? So that's kind of our motivation in the relationship with Rhea.
Yeah. And David, you've had some success already in healthcare. And was it pharmaceuticals? And now we want to take this to sort of medtech. Tell us about some of the success to date, and then, Susan, we'll come back to you to sort of talk about what we see the future for healthtech.
Sure. So, right, I think it's very much the success stories that I can share very much aligned with the points that Susan mentioned. And one of the challenges that we see, especially in the recent years, because previously even getting any access to data was very, very complicated. Data was not available. In the recent years, as we see more and more data becoming accessible for research, for studies, then the challenge went to another layer. Now is how to consolidate data from a lot of different sources, how to analyze the data, make it actionable, derive insights from this data. That's what became the main challenge in the recent years. And then I think just in time, the AI revolution came in because otherwise I would believe this problem would still be unsolved for another few years. But AI is the right approach to handle huge amounts of data of different formats and structures of different protocols, et cetera. And Breia was one of the first companies that leveraged AI in this specific vertical of life sciences because As probably our audience knows, healthcare and life science, it's a very unique space. You cannot take, for example, any tool from outside and just use it, including AI tools. They'll just not work. The depth of the knowledge, of the understanding of the nuances that's required in the space is significant. So it requires dedicated software, dedicated tools that understand how infrastructures work. So basically back to Breia, what Breia built is a platform that understands epidemiological and scientific workflows, understand healthcare workflows in healthcare organizations that can integrate and communicate with different types of data, clinical data, real-world data, registries data, claims data, and others can process all of them, can process unstructured notes. And by the way, around 2/3 of the data in healthcare is unstructured. It's just notes. So Bria is able to handle all of this. To support a lot of existing challenges. But mainly I would say the question is not how to get data. As I said, getting data nowadays is easier. It's how to turn it into evidence. That's the big challenge. The FDA requires recently to have more and more data in every submission. But the problem is most of the data that you will bring, they will not consider an evidence. You need to build this data to be an actual evidence. And this is the big challenge. So we worked mainly until now with pharma companies, now moving to devices as well. Among the use cases that we successfully delivered was identifying real-world patient journeys and aligning the clinical trial design with those patient journeys. It's around running epidemiology and safety analysis. It's around the landscape and gap assessment. In many cases, it's the phase 4, the post-marketing stage. So these are Uh, some of the examples, or for example, label expansion, things that, uh, are purely based in data, on data, where you take the existing knowledge and try to generate more insights. So these are the areas where we had successful project engagements with some of the both big pharma and small biotechs.
Now that, that's, uh, terrific. And if you can do that in life sciences, uh, medtech should be pretty easy, right? Souza, what, what do you, what do you see happening here with this, uh, collaboration and, and then How can we find you?
I think that they, the really the, it is everything that David said and then some. For us, it's about creating speed. Ultimately, it's a speed of facilitating not only the evidence generation, but the speed of facilitating decision-making. And so from our perspective, as we're thinking about new designs and new ways of being able to really create that intelligent care system on behalf of the health systems that we serve, That speed and liquidity and being able to connect data from different data sources, real-world evidence with emerging clinical evidence, and then create enough of it so there's confidence in how we deploy and market. That's what we're looking for our collaboration to be able to solve for. And we're at the beginning, right? But this is where it starts. And I think that that's our excitement. So from our perspective at Philips, we're obviously fairly large and easy to reach, but in the connected care area, We are starting to really bridge the ability to work with young companies that are growing, that are seeking to make an impact, because often they tend to move at a greater speed, though we tend to be able to have that ability to deploy that. And that's where those collaborations and that magic really starts to happen. So that's a little bit on me, David.
Yeah, I agree. And I see a significant potential specifically in medtech because we see that the AI revolution Actually even pushes forward the already existing trend of more and more R&D happening in small biotechs rather than big pharma companies, because they're, they're faster and better in adoption of AI. So now a small team in a biotech company can do what previously only the big pharmas were capable of. Now they can do the same with AI. So that's what you see in big pharma. In medtech, I think that Even though may potential resistance, eventually the big companies will adopt AI because they are by definition more technological. They have their own data that the device generate. So it's inevitable they will adopt AI and they will incorporate it. So I see with the big medical device companies even a bigger opportunity potentially than the pharma because they have devices that generate data. They have data. That need to be processed.
Yeah, that's brilliantly said. And we'll put all the information, how to reach you in the show notes and in the comment section so that those that are interested can, can learn a little bit more. Thank you so much, David and Souza, for being my guests on Ed Talks.
