A (3:14)

Thank you so much. I appreciate the story. And again, the start of a great story on this podcast, every single time is somebody's experienced something in their life that has influenced them in one way or another. And growing up in India and seeing a lot of that poverty and disparity, and then your mother as a mentor, gosh, that was incredible that she did that and reached out and showed you the way. And look at you now. You've. You've all grown up and you're, you're doing something even bigger than that. So that's really awesome. Thank you so much, Neil. And Neil, you've had a diverse career from designing medical devices to researching machine learning applications in healthcare. How have these experiences informed your leadership at PEAR team?

B (3:52)

I think one of the most important things it's kind of instilled in me is a sense of determination. I think every single problem I've gone into, I've been pretty naive as to how to solve it. And even the complexity and the depth of, of the problem that I was entering into and through these experiences, you know, through building out machine learning applications, this was looking at, you know, using a medical device to detect pneumonia in the lungs and, you know, using sound waves and seeing if you could use machine learning to see if the sound waves, you know, basically fluctuated differently when going through a liquid in the lungs, going through water in the lungs. And I'd never done this before. I'd never, you know, had never worked on a medical device I'd never worked on. Worked at looking at like diagnosis at that point and just jumping in head first showed me that jumping in and being determined, staying up late nights, researching, really getting into the weeds and getting into ground Truth like you can build up your own knowledge tree on this and you don't need anyone to tell you how to, you can just go and do it. And I've kind of taken that same mentality and brought it into Pear. What I like to tell folks at Pear and the team here is we're a bottoms up company. Meaning some companies are designed from the top down. They are. You see a problem, you design a solution from the very beginning and you kind of have the big macro, the intelligent design, so to speak, from the get go and you build towards it. Parateam has always been a bottoms up in that you find the bigger and more impactful problem to solve. So we went through, you know, supporting providers to then actually becoming a medical group ourselves and then to a medical group with a network of technology enabled social support services. And this was all through very iterative kind of determination. You know, Jeff Bezos has this very famous, you know, just do it step by step there. And that's what it's really brought into pear. When I think, when I think about these past experiences, I would say if you had, if you had to give me a shirt, it would just be you know, determined problem solver. That's what I think my career has led me up to before leading pair.

A (5:55)

That's awesome. Thank you. I like how you picked something. Obviously you talked about the sound waves and in the medical field but being that self starter and having studied and learned a lot on your own and then taking that brought that initiative and determination to the peer team has really helped you and the team excel. And of course you are certainly a problem solver and we heard that early on in your first answer here. So Neil, AI is the cornerstone of PEAR teams approach. How do you ensure that AI applications used are both effective and equitable, especially when dealing with vulnerable populations?

B (6:29)

Yeah. So I would start with what I feel so passionate about, AI right now in healthcare. And I'll just start with none of these are solved problems. I think parateam has a unique opportunity to actually build more effective and more equitable AI use cases in the healthcare system. And the reason I feel so passionate about this is because when I took a step back and I asked myself like what's changing in the world with AI? This is the first time that AI was actually able to not just do back office automation but, but actually be front of house, actually have conversations with patients, have conversations with our care delivery system, interact with doctor's offices. For example, you've seen these voice and text agents that are out there and that creates access. And so when you look at more vulnerable populations or underserved communities, access is one of the biggest issues. You have a need in the moment. If you're experiencing homelessness and you have a health concern, that need is now, if I'm not getting that addressed right now, I'm going to the emergency room or I'm going to the hospital. And in the typical system what happens is they have that need. The primary care office has a two week wait time and because of that you're stuck. You have no choice but to go to the hospital, which is bad for the system. It's very expensive visit. And the emergency room is just, no one likes to go there. And so I think AI is actually one of the ways we can bring access to the care delivery system, particularly with virtual care, you know, text, phone call, soon to have video agents that can help be multilingual for these patients. Right. I think there's over 200 languages that are spoken in the US so you get the long tail end of languages anytime, any place for patient care, it's available 24, 7, any time of day, any, any time of night for that need. It is infinitely patient. Right. Some individuals, especially those that are in need, that are scared, that don't have anyone else to really turn to, which is a lot of these, you know, more underserved communities. Unfortunately, someone might not have family to talk to. And I remember a distinct story of one of our patients who was going through a pre op plan because they were going to get surgery. And our care manager called them and had to talk to them about things they couldn't, could not eat. And so this woman was going through every single item in her cupboard asking the care manager if they could eat this or not. And you know, then you get to things like cinnamon, like sugar, like pepper, and you realize after a little bit that this is less about the conversation of what can I eat or not, it's more about having someone there that you can talk to because you're kind of scared. The scary thing, right? And that's actually what I see. And unfortunately we can't provide our service to every single person on Medicaid and Medicare. In fact, the way the healthcare system works is we can only provide our service to those that are the highest need. And by highest need, it's folks that go to the hospital enough such that keeping them out of the hospital would cover our labor costs. And that just like how the financial sustainability model works. But if we can provide our service at a fraction of the cost of a traditional care delivery organization, then you can provide this service to everybody. Right. Like, think of it, instead of it being hundreds of dollars a month, you're talking about $10 a month in this always available AI care manager. And that's what we're kind of building towards. And I think Medicaid is actually going to see the most impact because of these fundamental access challenges. There's just not enough doctors in the country to support everyone's need in the moment when they need that attention there. And PEAR team is positioned to safely deploy this technology. So there's a whole component here of how do you actually get it into the hands of patients? We operate a medical group so we can introduce AI services kind of job by job in a very controlled fashion, do gated releases and look at like confidence intervals of are we confident that when our operations team reviews what AI agents have done, did they do it correctly? Did they do it safely? Did they do it within an equitable fashion? Was the agent culturally competent, for example, and kind of navigated a conversation depending on the background of that individual. We have an expert team that can review all of those interactions. And then we're building up labeling the data set here. So this creates the data loop on how we continue to build out stronger and stronger data sets that are now tuned to dealing with a population that relies on our country's safety net.

A (11:12)

Thank you so much. I appreciate you breaking that down for us. I know you're passionate about AI in healthcare. It brings a big promise to healthcare and you've really focused on that conversational AI for that care delivery for clinicians. That communication is vital. And of course, access is one of the biggest issues for the Indo served.

B (11:28)

Right.

A (11:28)

And you're using AI to help with the care delivery of, of healthcare for everyone. And I like to call it better, faster, cheaper, smarter. Right. So you're doing a great job there. Appreciate that. And Neil, last question of the day. Looking ahead, what are your aspirations for PEAR team in the next five years and how do you envision the role of technology evolving in addressing healthcare disparities?

B (11:46)

There's two parts of it, two things that one, PEAR wants to do and I think just generally happening in the world. We'd like to open source our playbooks. The goal of PEAR team is not to hold on to the the workflows and technologies that we build out, but instead show people how you can leverage both technology and a strong, operationally focused medical group to better serve our safety net and work with. Over the course of five years actually work directly with state departments, work directly with the federal government to deploy our playbook across our safety net care delivery system. And so that's like one component where I see us in five years is really, really working to deploy this at scale and more thinking of it as we've shown you. So we've created the factory. Now let's show you how to create your own factory. Right? The blueprint for the factory, so to speak. And I think that's just the most effective way to impact and bring the right quality of care to the most individuals possible there. As part of that, one of the things that I see is just to proliferate, you know, especially with these. You look at like AI coding agents and the. And the ability to actually generate software. The safety net has been quite overlooked with respect to software. Most nonprofits do not have access to high quality tooling. One, they don't have the funding for it. And the tooling that is out there, it has very limited budgets, right. To build it out and are quite siloed in what it can do. And so I see a more unified system being built to connect nonprofits and not just help them be a directory service, but actually help nonprofits deliver care services and become a part of the care delivery system. So I'll give you an example, and this is what PEAR Team does day in, day out. What if a shelter organization had software that was connected to the local hospital so that it knew when one of its clients, one of the individuals who was staying at the shelter, went to the hospital or went to a primary care office? And maybe that shelter could be the place where someone could get their drugs shipped so they can do up an easy pickup, especially for those experiencing disabilities. The same goes for food pantries, helping to if someone has high blood pressure or is diabetic, how that food pantry can understand that context and help give them an appropriate nutritious meal depending on their health needs. And I think this is a way and something that paired team wants to build towards is a future where you have this shared and unified technology infrastructure across our safety net care delivery system.

A (14:23)

Thank you. I really appreciate that.

B (14:25)

Neil.

A (14:26)

You know, you did highlight a couple of things that I think is important. Obviously you want to be an open book there, your playbook. You want to be transparent, in fact, helping local, state, maybe federal governance in your playbook. But you really highlighted that gap with having a better tool set for the safety nets, because that's overlooked a lot of the times. And I've worked in healthcare a long time I worked at a safety net as well in tech, and so I definitely see it. So leveraging AI tech and software will definitely help in these areas, and I'm glad you're putting a focus there. I really appreciate that. And Neil, it's been such a pleasure having you on today, and I look forward to speaking with you real soon.

B (15:00)

Brian, likewise. Thank you so much for having me.

A (15:02)

Bye for now.