Adam Torres and Steve Chamberlin and Ann Lewandowski discuss health equity.
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Show Notes:
Reviewing bias in healthcare is important to provide better patient outcomes. In this episode, Adam Torres and Steve Chamberlin and Ann Lewandowski, Co Founders at Equitable Evidence, explore how AI can help on the path to achieving health equity in healthcare.
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About Steve Chamberlin
Steve career has provided exceptional opportunities for local and global experience, award recognitions and being published. Along the way, he honed his skills to craft services and products to take a consultative approach of working with clients.
About Ann Lewandowski
Healthcare is the most regulated industry in the U.S. and is thus subject to radical shifts from policymakers. She have a knack for anticipating the impact of policy on patient access to hospitals, non-profits, public health, pharmaceutical manufacturing, and insurers. Healthcare policy drives outcomes and business problems. I’m here to help you understand, mitigate, and build effective policy solutions. She can help advise your company on navigating the complex web of policies impacting your business and create effective advocacy campaigns to address your challenges.
She also serve as a fractional chief patient officer, bringing the patient’s voice into the daily operations of pharmaceutical companies and care delivery providers. My skills at presenting to every level of the organization, bringing teams together to build collective impact while considering the policy environment, have delivered real value for small and mid-sized teams.
Hey, I’d like to welcome you to another episode of Mission Matters. My name is Adam Torres. And if you’d like to apply to be a guest in the show, hit me up. Just head on over to missionmatters. com and click on be our guest to apply. All right. So today’s guests are Steve Chamberlain and Ann Lewandowski, who are co founders over at Equitable Evidence, Steve and welcome to the show.
Thank you, Adam. Thank you. Great to be here. All right, so I’m looking forward to today’s conversation. We’re going to talk about using AI to achieve health equity. So a hot topic AI, how that plays into health health equity. We’re going to get into that. We got a lot to unpack here today, but we’ll start this episode the way that we start them all with what we like to call our mission matters minute.
So at Mission Matters, our aim and our goal is to amplify stories for entrepreneurs, executives, and experts. That’s our mission. Steve, Ann, what mission matters to you? So thank you so much for the opportunity to speak about equitable evidences mission. It’s both very simple and complex. We’re using AI to track bias within the healthcare system.
Looking and seeing how it influences things like clinical protocols, payment methodologies that ultimately influence health care outcomes. And this was really born out of my fear as a patient and seeing how AI was being used to ration care and potentially reinforcing bias. So when I saw a call for how to develop the future of evidence, I flipped the script in my mind and thought, But if we could use AI and its ability to review massive amounts of data to see where the pain points were for underserved people.
Whenever I have co founders on here, I always have to ask this one. How’d you meet? How was, how’d this idea? How’d this come together? I can tell you my co founder story. I met my other, my co founder. I always tell people I swiped right on LinkedIn. I met my other co founder here. We’ve been together for eight years ever since a match made in heaven, I say, and I, and I can’t do it without Chirag.
So I always want to hear the co founder story. How’d you come together for this, for this company? Yeah. Yeah. So kind of a similar kind of story where Anne had this fantastic idea of needing to do something better for patients. Yeah. And so she was telling a mutual friend, okay, here’s what I’m doing.
Here’s what I’m thinking. And so she told her, Oh, I’ve got just the person that you need to meet. That’s already doing working with AI and healthcare. And so she introduced us. And so we just. really hit it off and just started talking and sharing about, okay, who does what, how do we want to get the ball rolling?
It’s just been a great partnership just because we really have not only great personality that. Is, you know, meshes well with each other, but each of our strengths it’s perfect as far as me bringing the, the technology and the AI side to it with her healthcare background. So even though I’ve been doing this a while, she’s just a wealth of knowledge.
So it just worked out well and it’s been going stronger since and what’s your story and what’s your side of the story? And I, before I, before you answer that, I have to tell you this. So sometimes I’ll interview co founders separately and they had, and I’ll hear the first one. And then I hear that.
I said, I’m like, that’s not what they said. You have the privilege of you’re both doing it at the same time. That’s the, and what’s your side of the story? Well. I think the first and most important part of the story to note is that Steve has convinced me that we are not on the Terminator timeline. There we go.
Okay. So that’s very important to note . But really, like he said, I approached a friend, I was reviewing opportunities and thought, thought sort of thought pieces. Mm-Hmm. to, you know, challenge my thinking around ai and I saw a question about how could we make evidence. That influences the next 10 to 15 years of health care more actively, and that really flipped the script for me on AI and machine learning.
And so I did approach a friend. So it’s an arranged marriage. It’s not swiping right on LinkedIn, but arranged marriage instead, and he introduced us. And, you know, I thought he was amazing. What he’s doing is amazing. And it complimented my skills and opportunities very well. And I can’t say how grateful I am for him.
It’s been a true blessing to start a business with him. Amazing. So as we start to kind of unpack the topic here let’s start by just defining this, what bias in healthcare, like, like, what does that mean? Like, let’s start kind of basic here. Like, what does that mean? Why does it matter? Yeah, so bias and health care is a very hot topic.
I’ve worked in health equity for about 14 years. I worked in rural health. Started a coalition in vaccines, focusing on delivering the COVID 19 vaccine equitably around the state of Wisconsin. And, you know, joined a large national pharmaceutical company that talked about health equity as well. And really there’s many pieces to bias in healthcare.
There’s implicit bias where somebody I might present as a woman or a person with darker skin, or Without adequate English skills or the ability to really talk about my disease. And of course, those have influence on how a physician or caretaker. Might view me on how they might treat me. And then there’s more overt bias that we can think about, such as denying pregnant and lactating women access to clinical trials and really, you know, or the exclusion of women in clinical trials until the nineties or, you know, inadequate representation from certain populations, like BIPOC or, you know, Asian Americans and stuff like that.
And so now looking at, as we get first, so now, thank you for defining that by the way. So now we have a, like a basis for the conversation. So now if we connect that with AI and with, so how does this piece of it work? Like, how does the fact that things are moving so fast? To where now maybe some of those decisions in the past, those were like pretty, somebody made that decision, right?
Like it was somebody, a group, a committee, whoever that said that somebody could participate or not. But now with AI and just how things are moving so fast, like how do we connect these two? Like where do we start with that? Yeah. So it’s a, I mean, couple great points and a wonderful question. Technology has been.
In the works for quite a while and helping people make decisions both in health care and outside. I mean, if you think about it as far as just in general, something that people probably can relate to, maybe a little bit easier is H. R. Finding job. There’s so many resumes and so technology has been already sifting through those to bring keywords that people are searching for, for a particular job that they’re trying to hire to, to make that happen.
And so you keep evolving that with technology and finally getting to the point where you’re using AI or machine learning to be able to manage that process. And so with this, we’re trying to do some of those same things. We’ve been already using. AI or machine learning for healthcare. For my company, it’s been really more on the, the administrative side, if you will.
We’re not looking at trying to solve, you know, how to better care for somebody. It’s been kind of like what we’re talking about today. How do we bring better care and bring the tools so that people can really focus then. On getting care to those individuals. So what we’re looking at here is not trying to, like we were saying before, not trying to build a new Terminator to, to try to take over things, but we are trying to be more responsible and be more transparent.
So those are transparency as well as the. Protection of the personal data. So those are some of the top issues and in the industry, that’s finally really starting to happen. There are some state laws like Colorado that are trying to put some things forth that require that to happen with different companies.
And some of the new startups are using that as their primary focus is how do we keep. So that’s one of the things that we’re trying to do with this as well as we’re never going to be selling any data. We’re never going to be using it or selling or giving it to anybody else. We’re actually just trying to make it.
A better process for the patients to be able to find this better care by using technology to go through these millions of different types of transactions and all the different kinds of plans based off of location and some of the different characteristics that Ann was already bringing up to make it a little bit easier for humans to be able to find that right.
Plan so that they can get the care they need. You know, what I’ve learned from him is really that AI and the algorithms and machine learning start with a set of data. And as a patient, my biggest concern was these data sets are based on human interactions, right? And if we’ve already established that bias, both implicit and explicit are huge problems in healthcare.
Then, of course, we need something that starts to quantify and highlight where those challenges might be experienced so that we can really start to use AI’s power for good rather than reinforcing what we already know to be potentially very bad experiences for people leading to very bad outcomes. How does this lead to, how does this lead to better patient outcomes?
How can it? Yeah. So I’m going to take that one. Thanks to you. I think about it this way. I, our first project we anticipate being in multiple myeloma. And what we know there is that people with black skin often are diagnosed much earlier. And have much worse health outcomes than the typical, what healthcare might think of as the typical multiple myeloma patient, somebody who’s older, a white male, et cetera, et cetera.
And so what we’re hoping to do is, you know, sort of look at what is the base of evidence? You know, what does it say about what, what experience might be somebody be having with multiple myeloma? And then working with hospitals so that as they are developing their own AI clinical algorithms, we can say if a person with darker skin presents and they’re in their 40s, you know, and they’re in pain or whatever, you should just to be safe, right?
It’s a blood test. It’s not, it’s not a lot of money. It’s not like we’re saying, you know, go on and do a huge biopsy, but just flagging that. Have you considered. multiple myeloma in this case for this patient because the evidence says there is the potential for that. Now it’s not telling doctors what they have to do.
It’s just helping them understand that if they’ve not checked that it’s a potential issue where maybe that patient does have a disease that they’re not immediately recognizing because when they think of multiple myeloma, they think of a 70 or 80 year old white man. Yeah, whenever we have like something like what you’re trying to accomplish, like health equity, like that’s a, it’s a big topic.
I mean, when you think about the, the, the medical industry in general, the healthcare industry in general, you’re talking to healthcare providers, you’re talking to hospitals, you’re talking to doctors. There’s a lot, there’s a huge education piece around this, like, where, where do you start? Like, where does someone start with us?
So I’ll start, Steve, and then I think your experience with SAMHSA is going to be really great. So, there’s a ton of effort going into health equity, and I think that’s part of the reason why I thought now was the time for equitable equity. Plans are starting to be scored on their attempts to create more more health equity and other opportunities.
Thank you. Hospital, we’re thinking about it. How do we avoid these adverse outcomes or worse outcomes? Because, of course, with value based payment that that immediately contributes to the bottom line. So. I think as we start the work and really build the algorithm and understand, we have the opportunity to go in and talk with people who have stated that this is very important to their organization has very good experience with business development.
So I’ll let him step in and talk about what he does. Yeah. So we’ve had great experiences working with some of the doctors, hospitals different practices, as well as patients as we’re developing, designing, putting this together. And so it’s not only working with them upfront to understand, okay, here’s some of the different challenges, here’s some of the different experiences, but then being able to get right down.
into the trenches, as you will, as you’d say, being able to work with them, get their feedback. Some of the times, for example, with some of the doctors, we’ll actually go to their practice and between times between their patients so that we can talk with them, get their feedback and do some changes and such.
But so part of that process of being able to make sure that it is smooth, works well comes down to some of the technology side. So on the technology, we are using the same technology that. Some of the large insurance companies are using banks, the department of defense, actually the CERN super collider which is Oracle’s database.
It’s their autonomous database. So what that means is the database is even using AI. And so what that means is that as soon as the data gets to the database, it’s immediately encrypted and there’s safeguards for the personal information as well, so that everything is masked. And it’s so secure that Oracle can’t even get through it.
So if we, if for some reason we lose the keys, it’s gone. It’s done. So it’s, it’s starting from scratch kind of thing. So it’s super secure and scalable. And being used on a global scale. So it’s one of those nice things all the way from the database to the machine learning to the new gen AI that they’re they’ve also integrated into the database all makes it a complete stack so that it’s all reliable.
And then. Fits into the model that we were just talking about of being able to get it out there for people to use easily from anywhere. And Steve going, going a little bit further, like kind of down that line. You mentioned the department of funds. I mean, I see this as being potential used by, I don’t know, policymakers, think tanks, research groups.
I mean, the applications are pretty wide. Am I, am I off on that? No, you’re exactly right. I mean, this kind of technology is being used in a lot of different areas. And that’s actually some of the things that and and I have already been talking about and is already generating a lot of great ideas on how we might be able to make this available to others.
Kind of like open source, if you will. Yeah. So as far as some of the algorithms that we find so that they’re not biased. So that people are able to see that this is really a transparent type of process and we are keeping everybody’s interests at heart and how we’re taking some of those extra steps to make sure that we are thinking about everyone, making it sure it’s inclusive and not just focused on, you know, the older white guys that are typically the focus of a lot of these different programs.
So we’re. We’re really trying to make sure that it’s available for everybody. Sorry, Steve. I represent an older white guy. And what do you think this evolves into? Like, as we get further and further down that, I don’t know if we’ll call it the continuum of health equity. Let’s just say it’s not static.
We’re getting closer and closer with every step we can take here. With new data, with new insights, as it’s trained more, as the sets evolve. Like, what do you think this evolves into? Let’s, let’s dream for a moment, we’re entrepreneurs. Yeah, I don’t know. No, that’s, that’s all good. Andy, you want to go first or you want me to?
Why don’t you go first? And I will take a moment to compose my thoughts. All right. So some of our customers are actually already excited about this, and they were coming up with some similar kind of ideas and how we’re looking at this. And so some of where we’ve actually already started applying it is the pre certification process, because that’s what this actually fits into.
Is that how can we, their perspective being a hospital and practices, which are, okay, how do we make sure that as we’re bringing in patients for surgery, that there hasn’t been some procedure or some medicines or you know, whatever’s happened in the past to manage their issue is not going to impact our current search.
Surgery. And so what we’re looking at is being able to use AI for being able to. look for some of those different red flags, pull data off of medical documents and being able to search non structured type data as well as the structured. And then being able to allow the people that have been doing this on a regular basis, help train the system.
Okay, here’s the different things to look for. Did we miss something? Okay, training it to look for some of those things in the past. So then that way, again, the patient is having a better experience being able to make sure that everything goes smoothly with their surgery. There’s not anything that’s been missed, but then on the other side helping out the providers, the doctors, their surgical teams by making sure, okay, that they don’t have any surprises as they’re going through and doing that.
So it’s kind of a win, win. And then you can actually see, obviously with equitable. Evidence is that that’s a natural tie in. Okay. How do we make sure that for some of these different programs that With all of that information that we have that we can bring to the table is make sure that some of the people that really need the care that maybe doesn’t have the right kind of coverage right now, we can actually make some recommendations back to them to say, okay, here’s something that you should maybe look at prior to going further down this path of healthcare that Could provide you better care, better coverage so that you can actually get what you need.
So it’s a a lot of this is a natural fit and it’s a win, win, win for everybody. Yeah. Go ahead. Go ahead. Did you want to add to that end? Yeah. So I think from my point of view, I really come at it from a patient and I think about my own experience as a patient. It’s complex. I have two autoimmune conditions and controlling one has made one not fit the standard of evidence.
And so I do think that I am very empathetic to the fact that for many people, The standard of evidence that’s used, whether it’s the clinical standard or the payment standard, does not fit who they are and what they feel in their own skin. And so what I really hope is that we’re able to work and bring together a coalition that starts to understand what is it like to be a complex patient, because everybody talks about people like me.
Costing the system the largest amount of money, but we’re not very good at managing them. We’re also not good at managing people with culturally sensitive needs or, you know, even diagnosing certain types of population like skin disease on darker colored skin. And so for those reasons, I really hope that as we move more and more towards value based payment and all the initiatives.
And I think it’s important for us that we can take some of that burden off in terms of, because we hear about physician burnout, nurse burnout, how much these quality initiatives are damaging our workforce, while also enhancing the real care that people deserve and should be having, that is personalized to them, right, in the state, but also really recognizes, you What they experience in their own skin.
So that’s my hope is that we can bring providers, insurance companies, makers together to really enhance care for every American. It’s great. Well, and Steve, I just have to say it has been great having you on the show today. I mean, what’s next? What’s next for both of you? What’s next for the company? Like what’s next?
Well, we need to do some fundraising. I’ll be really honest, but I think that, like I said, the first disease state that we want to tackle is multiple myeloma, really starting to see and how to frame that so that we can really start to disrupt some of those health disparities. I’ll share a story of why that’s so important to me personally.
I worked as policy and advocacy director at a major major pharmaceutical manufacturer, and in the course of my duties, I went to a light the night Event, which is the leukemia and lymphoma lighting up the night, creating hope for cancer survivors and those in the middle of their cancer, cancer journey and a woman shared black woman shared her story about how it was to be diagnosed late.
Not be recognized immediately and, you know, she had a family, she was young and it just left me with this urge to really help make a city in my own state, Milwaukee, which is known for its health inequities. Better and more equitable place. Steve, any final words in your end? What’s next? Yeah, just trying to Tell me the tech side.
What you got up your sleeve? Yeah, no, I mean, it’s, it’s really, I mean, I’m sorry. It’s really an exciting time for technology. I mean, there’s so many things that are going on. And there are some, there’s some actual really cool tools that I just found out about yesterday. Okay. That I can’t wait to get my hands on.
That’s going to make our process even easier. So again, it’s really kind of funny with what we’re talking about. It’s actually using AI to help generate the code that we need for actually building out these tools so that we can actually do it faster and better. You’re going to have to convince me on that one, Steve.
I’m not sure about this whole AI to train AI thing. There goes your Terminator timeline. Oh, there it goes. It all comes back. I know. And it’s probably so obvious, I’m sorry, but I am a geek at heart and all this technology stuff is, is just really gets me excited. So we know it’s not exactly so it’s making sure that we do the right thing.
And then yeah, and. Putting a lot of this together so that we can actually start showing some of these different models of, okay, this can really work. This can really happen. And it’s here today. Yeah. That’s great. Well, if somebody is listening or watching this and they want to follow up and they want to learn more and continue to follow the journey, or even you mentioned that you’re going to be doing some fundraising, like, like how do people connect?
How do people connect and keep the conversation going? So we do have a website, it’s equitableevidence. com, and of course my email there is anne at
equievu. com, so feel free to reach out and give us a shout and we can’t wait to get to know your audience even more. Awesome and for everybody’s watching and listening just so you know We’ll put all the links to to the website all that other good stuff in the show notes so that you can just click on them and head right on over and Speaking of the audience if this is your first time with mission matters This is the daily show that means each and every day We’re putting out new content for you’re bringing on new ideas new entrepreneurs and hopefully new Inspiration to help you along in your journey if that sounds interesting.
We welcome you hit that That subscribe or follow button so that you get the notification. Cause guess what? Daily show tomorrow. You’re going to get that notification. We’re going to have another episode for you. And Steve, this has been so much fun again. Thank you so much for coming on the show.
Thanks Adam. And I can’t wait to catch tomorrow’s episode. Yeah, it was our pleasure. very much.
Hey, I’d like to welcome you to another episode of Mission Matters. My name is Adam Torres. And if you’d like to apply to be a guest in the show. Just head on over to missionmatters. com and click on be our guest to apply. All right. So today’s guests are Steve Chamberlain and Ann Lewandowski, who are co founders over at Equitable Evidence, Steve and welcome to the show.
Thank you, Adam. Thank you. Great to be here. All right, so I’m looking forward to today’s conversation. We’re going to talk about using AI to achieve health equity. So a hot topic AI, how that plays into health health equity. We’re going to get into that. We got a lot to unpack here today, but we’ll start this episode the way that we start them all with what we like to call our mission matters minute.
So at Mission Matters, our aim and our goal is to amplify stories for entrepreneurs, executives, and experts. That’s our mission. Steve, Ann, what mission matters to you? So thank you so much for the opportunity to speak about equitable evidences mission. It’s both very simple and complex. We’re using AI to track bias within the healthcare system.
Looking and seeing how it influences things like clinical protocols, payment methodologies that ultimately influence health care outcomes. And this was really born out of my fear as a patient and seeing how AI was being used to ration care and potentially reinforcing bias. So when I saw a call for how to develop the future of evidence, I flipped the script in my mind and thought, But if we could use AI and its ability to review massive amounts of data to see where the pain points were for underserved people.
Whenever I have co founders on here, I always have to ask this one. How’d you meet? How was, how’d this idea? How’d this come together? I can tell you my co founder story. I met my other, my co founder. I always tell people I swiped right on LinkedIn. I met my other co founder here. We’ve been together for eight years ever since a match made in heaven, I say, and I, and I can’t do it without Chirag.
So I always want to hear the co founder story. How’d you come together for this, for this company? Yeah. Yeah. So kind of a similar kind of story where Anne had this fantastic idea of needing to do something better for patients. Yeah. And so she was telling a mutual friend, okay, here’s what I’m doing.
Here’s what I’m thinking. And so she told her, Oh, I’ve got just the person that you need to meet. That’s already doing working with AI and healthcare. And so she introduced us. And so we just. really hit it off and just started talking and sharing about, okay, who does what, how do we want to get the ball rolling?
It’s just been a great partnership just because we really have not only great personality that. Is, you know, meshes well with each other, but each of our strengths it’s perfect as far as me bringing the, the technology and the AI side to it with her healthcare background. So even though I’ve been doing this a while, she’s just a wealth of knowledge.
So it just worked out well and it’s been going stronger since and what’s your story and what’s your side of the story? And I, before I, before you answer that, I have to tell you this. So sometimes I’ll interview co founders separately and they had, and I’ll hear the first one. And then I hear that.
I said, I’m like, that’s not what they said. You have the privilege of you’re both doing it at the same time. That’s the, and what’s your side of the story? Well. I think the first and most important part of the story to note is that Steve has convinced me that we are not on the Terminator timeline. There we go.
Okay. So that’s very important to note . But really, like he said, I approached a friend, I was reviewing opportunities and thought, thought sort of thought pieces. Mm-Hmm. to, you know, challenge my thinking around ai and I saw a question about how could we make evidence. That influences the next 10 to 15 years of health care more actively, and that really flipped the script for me on AI and machine learning.
And so I did approach a friend. So it’s an arranged marriage. It’s not swiping right on LinkedIn, but arranged marriage instead, and he introduced us. And, you know, I thought he was amazing. What he’s doing is amazing. And it complimented my skills and opportunities very well. And I can’t say how grateful I am for him.
It’s been a true blessing to start a business with him. Amazing. So as we start to kind of unpack the topic here let’s start by just defining this, what bias in healthcare, like, like, what does that mean? Like, let’s start kind of basic here. Like, what does that mean? Why does it matter? Yeah, so bias and health care is a very hot topic.
I’ve worked in health equity for about 14 years. I worked in rural health. Started a coalition in vaccines, focusing on delivering the COVID 19 vaccine equitably around the state of Wisconsin. And, you know, joined a large national pharmaceutical company that talked about health equity as well. And really there’s many pieces to bias in healthcare.
There’s implicit bias where somebody I might present as a woman or a person with darker skin, or Without adequate English skills or the ability to really talk about my disease. And of course, those have influence on how a physician or caretaker. Might view me on how they might treat me. And then there’s more overt bias that we can think about, such as denying pregnant and lactating women access to clinical trials and really, you know, or the exclusion of women in clinical trials until the nineties or, you know, inadequate representation from certain populations, like BIPOC or, you know, Asian Americans and stuff like that.
And so now looking at, as we get first, so now, thank you for defining that by the way. So now we have a, like a basis for the conversation. So now if we connect that with AI and with, so how does this piece of it work? Like, how does the fact that things are moving so fast? To where now maybe some of those decisions in the past, those were like pretty, somebody made that decision, right?
Like it was somebody, a group, a committee, whoever that said that somebody could participate or not. But now with AI and just how things are moving so fast, like how do we connect these two? Like where do we start with that? Yeah. So it’s a, I mean, couple great points and a wonderful question. Technology has been.
In the works for quite a while and helping people make decisions both in health care and outside. I mean, if you think about it as far as just in general, something that people probably can relate to, maybe a little bit easier is H. R. Finding job. There’s so many resumes and so technology has been already sifting through those to bring keywords that people are searching for, for a particular job that they’re trying to hire to, to make that happen.
And so you keep evolving that with technology and finally getting to the point where you’re using AI or machine learning to be able to manage that process. And so with this, we’re trying to do some of those same things. We’ve been already using. AI or machine learning for healthcare. For my company, it’s been really more on the, the administrative side, if you will.
We’re not looking at trying to solve, you know, how to better care for somebody. It’s been kind of like what we’re talking about today. How do we bring better care and bring the tools so that people can really focus then. On getting care to those individuals. So what we’re looking at here is not trying to, like we were saying before, not trying to build a new Terminator to, to try to take over things, but we are trying to be more responsible and be more transparent.
So those are transparency as well as the. Protection of the personal data. So those are some of the top issues and in the industry, that’s finally really starting to happen. There are some state laws like Colorado that are trying to put some things forth that require that to happen with different companies.
And some of the new startups are using that as their primary focus is how do we keep. So that’s one of the things that we’re trying to do with this as well as we’re never going to be selling any data. We’re never going to be using it or selling or giving it to anybody else. We’re actually just trying to make it.
A better process for the patients to be able to find this better care by using technology to go through these millions of different types of transactions and all the different kinds of plans based off of location and some of the different characteristics that Ann was already bringing up to make it a little bit easier for humans to be able to find that right.
Plan so that they can get the care they need. You know, what I’ve learned from him is really that AI and the algorithms and machine learning start with a set of data. And as a patient, my biggest concern was these data sets are based on human interactions, right? And if we’ve already established that bias, both implicit and explicit are huge problems in healthcare.
Then, of course, we need something that starts to quantify and highlight where those challenges might be experienced so that we can really start to use AI’s power for good rather than reinforcing what we already know to be potentially very bad experiences for people leading to very bad outcomes. How does this lead to, how does this lead to better patient outcomes?
How can it? Yeah. So I’m going to take that one. Thanks to you. I think about it this way. I, our first project we anticipate being in multiple myeloma. And what we know there is that people with black skin often are diagnosed much earlier. And have much worse health outcomes than the typical, what healthcare might think of as the typical multiple myeloma patient, somebody who’s older, a white male, et cetera, et cetera.
And so what we’re hoping to do is, you know, sort of look at what is the base of evidence? You know, what does it say about what, what experience might be somebody be having with multiple myeloma? And then working with hospitals so that as they are developing their own AI clinical algorithms, we can say if a person with darker skin presents and they’re in their 40s, you know, and they’re in pain or whatever, you should just to be safe, right?
It’s a blood test. It’s not, it’s not a lot of money. It’s not like we’re saying, you know, go on and do a huge biopsy, but just flagging that. Have you considered. multiple myeloma in this case for this patient because the evidence says there is the potential for that. Now it’s not telling doctors what they have to do.
It’s just helping them understand that if they’ve not checked that it’s a potential issue where maybe that patient does have a disease that they’re not immediately recognizing because when they think of multiple myeloma, they think of a 70 or 80 year old white man. Yeah, whenever we have like something like what you’re trying to accomplish, like health equity, like that’s a, it’s a big topic.
I mean, when you think about the, the, the medical industry in general, the healthcare industry in general, you’re talking to healthcare providers, you’re talking to hospitals, you’re talking to doctors. There’s a lot, there’s a huge education piece around this, like, where, where do you start? Like, where does someone start with us?
So I’ll start, Steve, and then I think your experience with SAMHSA is going to be really great. So, there’s a ton of effort going into health equity, and I think that’s part of the reason why I thought now was the time for equitable equity. Plans are starting to be scored on their attempts to create more more health equity and other opportunities.
Thank you. Hospital, we’re thinking about it. How do we avoid these adverse outcomes or worse outcomes? Because, of course, with value based payment that that immediately contributes to the bottom line. So. I think as we start the work and really build the algorithm and understand, we have the opportunity to go in and talk with people who have stated that this is very important to their organization has very good experience with business development.
So I’ll let him step in and talk about what he does. Yeah. So we’ve had great experiences working with some of the doctors, hospitals different practices, as well as patients as we’re developing, designing, putting this together. And so it’s not only working with them upfront to understand, okay, here’s some of the different challenges, here’s some of the different experiences, but then being able to get right down.
into the trenches, as you will, as you’d say, being able to work with them, get their feedback. Some of the times, for example, with some of the doctors, we’ll actually go to their practice and between times between their patients so that we can talk with them, get their feedback and do some changes and such.
But so part of that process of being able to make sure that it is smooth, works well comes down to some of the technology side. So on the technology, we are using the same technology that. Some of the large insurance companies are using banks, the department of defense, actually the CERN super collider which is Oracle’s database.
It’s their autonomous database. So what that means is the database is even using AI. And so what that means is that as soon as the data gets to the database, it’s immediately encrypted and there’s safeguards for the personal information as well, so that everything is masked. And it’s so secure that Oracle can’t even get through it.
So if we, if for some reason we lose the keys, it’s gone. It’s done. So it’s, it’s starting from scratch kind of thing. So it’s super secure and scalable. And being used on a global scale. So it’s one of those nice things all the way from the database to the machine learning to the new gen AI that they’re they’ve also integrated into the database all makes it a complete stack so that it’s all reliable.
And then. Fits into the model that we were just talking about of being able to get it out there for people to use easily from anywhere. And Steve going, going a little bit further, like kind of down that line. You mentioned the department of funds. I mean, I see this as being potential used by, I don’t know, policymakers, think tanks, research groups.
I mean, the applications are pretty wide. Am I, am I off on that? No, you’re exactly right. I mean, this kind of technology is being used in a lot of different areas. And that’s actually some of the things that and and I have already been talking about and is already generating a lot of great ideas on how we might be able to make this available to others.
Kind of like open source, if you will. Yeah. So as far as some of the algorithms that we find so that they’re not biased. So that people are able to see that this is really a transparent type of process and we are keeping everybody’s interests at heart and how we’re taking some of those extra steps to make sure that we are thinking about everyone, making it sure it’s inclusive and not just focused on, you know, the older white guys that are typically the focus of a lot of these different programs.
So we’re. We’re really trying to make sure that it’s available for everybody. Sorry, Steve. I represent an older white guy. And what do you think this evolves into? Like, as we get further and further down that, I don’t know if we’ll call it the continuum of health equity. Let’s just say it’s not static.
We’re getting closer and closer with every step we can take here. With new data, with new insights, as it’s trained more, as the sets evolve. Like, what do you think this evolves into? Let’s, let’s dream for a moment, we’re entrepreneurs. Yeah, I don’t know. No, that’s, that’s all good. Andy, you want to go first or you want me to?
Why don’t you go first? And I will take a moment to compose my thoughts. All right. So some of our customers are actually already excited about this, and they were coming up with some similar kind of ideas and how we’re looking at this. And so some of where we’ve actually already started applying it is the pre certification process, because that’s what this actually fits into.
Is that how can we, their perspective being a hospital and practices, which are, okay, how do we make sure that as we’re bringing in patients for surgery, that there hasn’t been some procedure or some medicines or you know, whatever’s happened in the past to manage their issue is not going to impact our current search.
Surgery. And so what we’re looking at is being able to use AI for being able to. look for some of those different red flags, pull data off of medical documents and being able to search non structured type data as well as the structured. And then being able to allow the people that have been doing this on a regular basis, help train the system.
Okay, here’s the different things to look for. Did we miss something? Okay, training it to look for some of those things in the past. So then that way, again, the patient is having a better experience being able to make sure that everything goes smoothly with their surgery. There’s not anything that’s been missed, but then on the other side helping out the providers, the doctors, their surgical teams by making sure, okay, that they don’t have any surprises as they’re going through and doing that.
So it’s kind of a win, win. And then you can actually see, obviously with equitable. Evidence is that that’s a natural tie in. Okay. How do we make sure that for some of these different programs that With all of that information that we have that we can bring to the table is make sure that some of the people that really need the care that maybe doesn’t have the right kind of coverage right now, we can actually make some recommendations back to them to say, okay, here’s something that you should maybe look at prior to going further down this path of healthcare that Could provide you better care, better coverage so that you can actually get what you need.
So it’s a a lot of this is a natural fit and it’s a win, win, win for everybody. Yeah. Go ahead. Go ahead. Did you want to add to that end? Yeah. So I think from my point of view, I really come at it from a patient and I think about my own experience as a patient. It’s complex. I have two autoimmune conditions and controlling one has made one not fit the standard of evidence.
And so I do think that I am very empathetic to the fact that for many people, The standard of evidence that’s used, whether it’s the clinical standard or the payment standard, does not fit who they are and what they feel in their own skin. And so what I really hope is that we’re able to work and bring together a coalition that starts to understand what is it like to be a complex patient, because everybody talks about people like me.
Costing the system the largest amount of money, but we’re not very good at managing them. We’re also not good at managing people with culturally sensitive needs or, you know, even diagnosing certain types of population like skin disease on darker colored skin. And so for those reasons, I really hope that as we move more and more towards value based payment and all the initiatives.
And I think it’s important for us that we can take some of that burden off in terms of, because we hear about physician burnout, nurse burnout, how much these quality initiatives are damaging our workforce, while also enhancing the real care that people deserve and should be having, that is personalized to them, right, in the state, but also really recognizes, you What they experience in their own skin.
So that’s my hope is that we can bring providers, insurance companies, makers together to really enhance care for every American. It’s great. Well, and Steve, I just have to say it has been great having you on the show today. I mean, what’s next? What’s next for both of you? What’s next for the company? Like what’s next?
Well, we need to do some fundraising. I’ll be really honest, but I think that, like I said, the first disease state that we want to tackle is multiple myeloma, really starting to see and how to frame that so that we can really start to disrupt some of those health disparities. I’ll share a story of why that’s so important to me personally.
I worked as policy and advocacy director at a major major pharmaceutical manufacturer, and in the course of my duties, I went to a light the night Event, which is the leukemia and lymphoma lighting up the night, creating hope for cancer survivors and those in the middle of their cancer, cancer journey and a woman shared black woman shared her story about how it was to be diagnosed late.
Not be recognized immediately and, you know, she had a family, she was young and it just left me with this urge to really help make a city in my own state, Milwaukee, which is known for its health inequities. Better and more equitable place. Steve, any final words in your end? What’s next? Yeah, just trying to Tell me the tech side.
What you got up your sleeve? Yeah, no, I mean, it’s, it’s really, I mean, I’m sorry. It’s really an exciting time for technology. I mean, there’s so many things that are going on. And there are some, there’s some actual really cool tools that I just found out about yesterday. Okay. That I can’t wait to get my hands on.
That’s going to make our process even easier. So again, it’s really kind of funny with what we’re talking about. It’s actually using AI to help generate the code that we need for actually building out these tools so that we can actually do it faster and better. You’re going to have to convince me on that one, Steve.
I’m not sure about this whole AI to train AI thing. There goes your Terminator timeline. Oh, there it goes. It all comes back. I know. And it’s probably so obvious, I’m sorry, but I am a geek at heart and all this technology stuff is, is just really gets me excited. So we know it’s not exactly so it’s making sure that we do the right thing.
And then yeah, and. Putting a lot of this together so that we can actually start showing some of these different models of, okay, this can really work. This can really happen. And it’s here today. Yeah. That’s great. Well, if somebody is listening or watching this and they want to follow up and they want to learn more and continue to follow the journey, or even you mentioned that you’re going to be doing some fundraising, like, like how do people connect?
How do people connect and keep the conversation going? So we do have a website, it’s equitableevidence. com, and of course my email there is anne at
equievu. com, so feel free to reach out and give us a shout and we can’t wait to get to know your audience even more. Awesome and for everybody’s watching and listening just so you know We’ll put all the links to to the website all that other good stuff in the show notes so that you can just click on them and head right on over and Speaking of the audience if this is your first time with mission matters This is the daily show that means each and every day We’re putting out new content for you’re bringing on new ideas new entrepreneurs and hopefully new Inspiration to help you along in your journey if that sounds interesting.
We welcome you hit that That subscribe or follow button so that you get the notification. Cause guess what? Daily show tomorrow. You’re going to get that notification. We’re going to have another episode for you. And Steve, this has been so much fun again. Thank you so much for coming on the show.
Thanks Adam. And I can’t wait to catch tomorrow’s episode. Yeah, it was our pleasure. very much.