Ngan MacDonald - Opening Opportunities by Liberating Healthcare Data

The conversation this week is with Ngan MacDonald. Ngan is the chief of data operations at the Institute for Augmented Intelligence in Medicine, where she is bridging computational methods with human experience to advance medical science and improve human health. Nana's, a 2022 cohort candidate for the Women In Bio 3/8 Initiative, named for the March 8, International Women's Day where her team seeks to increase startup board leadership for women in Chicago's Life Sciences. Finally, if that isn't keeping her busy enough, she is a healthcare senior advisor and McCormick BI

If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future AppliedAI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events!

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• AppliedAI Meetup

Resources and Topics Mentioned in this Episode

• Institute for Augmented Intelligence in Medicine
• WIB-RTP 3.8 Initiative
• McCormick BI
• SDLC
• 3rd Coast AI for Health Bowl
• Health Data Gymnasium
• 21st Century Cures Act
• AHIP
• Invitae DNA health and medical testing
• Follow Ngan MacDonald on Twitter

Enjoy!

Your host,
Justin Grammens

Transcript

Ngan MacDonald  0:00  

A lot of people when they talk about AI, they talk about artificial intelligence. And I think like in healthcare, we're just not ready to go there. There's just too much data, too much information. And it's, it's very disparate, you're not very well understood. You know, when you have a problem that's difficult to understand it's complex. The data underneath it is in many ways deficient, then I don't think that artificial intelligence can really solve that problem. But when we do need and healthcare is, we need that augmented data.

AI Announcer  0:32  

Welcome to the conversations on applied AI podcast where Justin Grammens and the team at emerging technologies North talk with experts in the fields of artificial intelligence and deep learning. In each episode, we cut through the hype and dive into how these technologies are being applied to real world problems today. We hope that you find this episode educational and applicable to your industry and connect with us to learn more about our organization at applied ai.mn. Enjoy.

Justin Grammens  1:02  

Welcome listeners to the conversations on applied AI Podcast. Today we're talking with Ngan MacDonald. Ngan is the chief of data operations at the Institute for Augmented Intelligence in Medicine, where she is bridging computational methods with human experience to advance medical science and improve human health. Nana's, a 2022 cohort candidate for the Women In Bio 3/8 Initiative, named for the March 8, International Women's Day where her team seeks to increase startup board leadership for women in Chicago's Life Sciences. Finally, if that isn't keeping her busy enough, she is a healthcare senior advisor and McCormick BI Thank you, Nan, for all the work that you do to enable better health care by liberating data.

Ngan MacDonald  1:39  

Hi, Justin, how are you?

Justin Grammens  1:41  

I'm doing well doing well. Well, great. Now it's really a treat to talk with you. We haven't had a whole lot of people on here that have really been focused, like, like you have been really around health care about all this data that we really have in healthcare. And I know one things you talk about is this kind of spread out in a number of different areas. And I really want to get into that as we talk. But to sort of kick things off, I guess maybe you could give a little bit of a background with regards to sort of how you got into this chief of data operations role and, and some of the other organizations that you're a part of sort of what was the path in your career,

Ngan MacDonald  2:13  

when I graduated from Northwestern, I had a communications degree. And the first job that was awkward to me was in HR. And my reaction was, wow, somebody's going to pay me to talk to people all day. And so after that, I spent some time in HR and got involved in PeopleSoft implementation, which is a HRIS. system. And what I found is that in HR, a lot of what you're doing is answering questions about how do you translate business strategy into like, people, when we implemented the system and made it that much easier to answer questions, I decided them that I wasn't as much as I liked people, I actually liked computers more, or went back to school. And at that time, the whole area around business intelligence and data warehousing was just kicking off, came out of school, and was working in consulting, doing data warehouses, analytics, all that good stuff. And then I had started having kids. And the whole travel with consulting was not as you know, as fun as it used to be. And so I was offered a job, which I thought was going to be kind of a small hiatus from consulting to work at this little known company called BlueCross, BlueShield. So, eight years later, you know, the ACA was passed. And you know, all of this healthcare data and sort of the need for data became more and more relevant. I've been so we're getting it out of the policy insurance side of the healthcare industry, as well as the management consulting for the last, I would say, 15 or so years,

Justin Grammens  3:46  

how do those sides differ when it comes to insurance? And then you said management, health care? What are we talking about here?

Ngan MacDonald  3:51  

Well, management consulting is just the Deloitte and Accenture as of the world, I worked for more boutique companies, they tend to hire people who have a lot of experience to come in and help companies do their data strategies, or their IT strategies or like how do you pivot from a regular SDLC to a more agile methodology? So you know, definitely kind of that higher order thinking that people usually when they're in the thick of things have a hard time kind of pulling themselves out and looking at the big picture. So kind of help them do that.

Justin Grammens  4:26  

I get it. And you know, you talked about software development life cycles as like SDLC. Some people maybe not aren't familiar with that term. I've been doing that for many, many years myself. So it feels like you're kind of bringing agile into healthcare,

Ngan MacDonald  4:38  

agile into healthcare, agile into data, just kind of realizing that. Actually, I think data warehousing is perfect for the agile framework, because a lot of the impatience people had around a like core sort of standards, software development lifecycle in data was that you know, like data is ever changing, and you're constantly having to bring it back up. and look at it again. And so it marries better with agile than it does with SDLC.

Justin Grammens  5:05  

So then, okay, so yeah, so you're doing all this management consulting, I guess, right, haven't having a good time looking at data. But something changed, because you're you kind of got into into Northwestern, right is that there's this department center that you're a part of right now that you're nearly,

Ngan MacDonald  5:18  

you know, having spent all this time trying to create data solutions for health insurance companies, I was sort of looking for, like, right before the pandemic, looking for a rest, you know, and doing something that was meaningful. You know, my whole mission is deliberate data in healthcare, working inside insurance companies, where it tends to be very conservative. And so there's not like innovation just takes a lot longer. And so I thought I'd take a break, and go back to my alma mater, and, and sort of volunteer my time. But at the time, the Institute for augmented intelligence and medicine was just launching. And it's then instead becomes my full time job, as opposed to just kind of this volunteer six month hiatus that I was planning to take. So it's been a couple years, and we've done some really fun things. And I don't really see my role here ending as I anticipated.

Justin Grammens  6:13  

That's cool. So augmented intelligence, can you elaborate a little bit on that? A lot

Ngan MacDonald  6:17  

of people when they talk about AI, they talk about artificial intelligence. And I think like in healthcare, we're just not ready to go there. Right. There's just too much data, too much information. And it's very disparate, and it's not very well understood. So, you know, when you have a problem that's, you know, difficult to understand it's complex, and the data underneath it is in many ways deficient, then, you know, I don't think that AI can really, artificial intelligence can really solve that problem. But what we do need in healthcare is, we need that augmented data. So I think of it as you know, you've got all this data that, you know, the human mind can't wrap its head around. And you know, like, how do you bring that data into, like, the context and personalization, that is healthcare, because the problem with you know, like, there's billions of us on the earth, right. And so you think about, it's like, there's probably somebody that's like, you accept that, you know, like, that's just from a pure biology standpoint. But then, you know, no one really lives in your zip code, with the exact same like access to doctors and specialists as you do. So when you think about augmented intelligence, it's bringing those two things together, like the technology and the data and the AI that can actually help inform humans to make decisions. So augmenting their decision making.

Justin Grammens  7:42  

I love it. That's awesome. Maybe some of these and this, I think this applies in some of the areas that I've worked with regards to AI and machine learning, which is really in the Internet of Things space. So you've got these companies that are have been building physical products for 50 years or so. And that's just the way they've always done it. And they need to sort of rethink their entire business model around services around sensors around things that they're normally not really used to dealing with. And so it's a little bit of a of a culture shock. And it can actually take a long time for business to sort of pivot and move into that space. And it sounds like you're saying something similar when it comes to health care?

Ngan MacDonald  8:15  

Yeah, so in healthcare, it's interesting, because we have all these different stakeholders. It's not a widget, it's more that you have all these different incentive systems. So you have healthcare providers, who are physicians, nurses, you know, physical therapists, and they are incented, to do certain things. And they're incented, to kind of look at you in terms of like your condition, like, what's the problem that you have, that they can help solve? Your insurance side is the people who are paying the bills, and they want to know, you know, how can they keep you healthy? How can they stop you from actually, you know, having to use those services. And then you have your employer who is not really the person receiving their services. And they're ultimately the ones who are paid for. So you just have all of these different stakeholders that have like different lenses that they use to look at your health care delivery. And so then they generate their different types of information gets a shock, when somebody from the provider side comes into the insurance side of the house and sees what type of data that lives there. And then it's a shock for like, say somebody who's doing basic science and pharmaceutical research comes into like the hospital and sees what data is there or into insurance company. So we're all like looking at data, but in different ways, then, like the data is being used with different end results. Sure, yeah, you're

Justin Grammens  9:43  

right. I mean, AI everyone's driven by their own motivation, I guess, depending on which side of the fence they're sitting on. That makes that makes a lot of sense. I didn't really sort of thought through that. So as an institute, are you trying to provide like, like, what's the output are you providing like tools are you providing a collaboration opportunities? What Where are you goes in this.

Ngan MacDonald  10:00  

So it took us a couple years to sort of like find our reason for being. And I think that real reason is, you know, like I said, being able to take different types of data computing and marrying it to your human expertise, and what that results in or what we want that to result in as a community. Because, yes, you have the technology, but it's really about who is in this space, who can share and build upon each other's work. So we've got a couple of things that we've done. One is, we have this third Coast augmented intelligence for health bowl, which is incentivizing student teams to come in and come up with solutions for, you know, health disparity using healthcare data. And that's a six month long competition in which we're nurturing the student teams through and kind of showing them things around, you know, health care, data bias, aesthetics, cybersecurity, all these things that I wish I'd known when I was just starting in this space. And we want to provide for the students as they're starting out. And then this other aspect of what we're trying to build up as we want to create a health data gymnasium, which marries the people expertise, with the datasets that are available in healthcare, and the tools. And then you know, just like the word gymnasium implies, it's a training ground, right? You know, you're probably very skilled in AI. But if I asked you to, you know, write a bunch of algorithms for healthcare, you wouldn't really know like, what kind of data would you go after? Why don't you go after this data versus the other data? So like, trying to trying to bring in like what I call data Sherpas to help you figure out where best to point, you know, your technology at? That's sort of the role of the gymnasium is to be that bridge builder.

Justin Grammens  11:46  

That's exciting. That's, that's really, really cool. So I guess, what are some some examples? I know, I think when you and I sort of talked talked offline, I think you're talking about ethics, I guess, like specifically, there were some examples. I know, there was like, wow, I hadn't even thought about it that way.

Ngan MacDonald  12:00  

Yeah, so one example is, you know, you think about a population of patients or individuals, right. And if you have, like 1000 people, out of those 1000, people say 600 of those people actually have some kind of medical symptoms, and some subset and maybe like, 300 of them, will end up saying, Oh, I think I'm gonna go to either my doctors or, you know, an immediate care, out of like that 1000, less than 1% of those people will end up going to an academic medical center for their care. And, you know, like the, the perspective that I want people to think about is, that's where we're collecting the data. For clinical studies. In 2020, there was a JAMA article, the journal of the American Medical Association, they basically said that, you know, out of all of these algorithms, that clinical algorithms that we use, most of those were taking place in the east or the west coast, I think it was like 34 states were not even represented. So states like Mississippi, Louisiana, Alabama, states, when you think about, where's the health disparity in those various states, and yet, we're creating these algorithms based on data that's not even in those states.

Justin Grammens  13:13  

That's crazy. Yeah, we're only looking at pretty much the half of the data, or it sounds like a very, very different weighted version, you know, of it. So people that live in those states aren't even being represented in any of these situations.

Ngan MacDonald  13:25  

Well, and it's not intentional, right? You know, a lot of times what happens in the bias is that people get access to data, and they are answering question and they go and get data that's easy to get, they're not necessarily even thinking, Oh, well, you know, like, where's that data being collected? You know, is it being collected in a medical center? Is that representational of like the people that are actually going to get sick? And so I think that in the last, I would say, 10 or so years, I feel like we are trying to be more intentional about where we're collecting data. So there are definitely there's an Alzheimer's cohort up in Wisconsin, where they have intentionally gone out and recruited people who have a family history of Alzheimer's, and trying really hard to be broad in who they get data from not just the people who happen to be coming in the doors of academic medical centers.

Justin Grammens  14:17  

I mean, how can people participate? I know there's in well, I'll include these in the liner notes and stuff like that as well. But people want to be a part of the data gymnasium. I know there's, you've got websites and stuff set up, right?

Ngan MacDonald  14:27  

We have a website called Health Data gym, we've got, you know, ways to get in touch with us there get on that website, we have a small curated list of datasets that we've worked with, which we think are good datasets. Right now we just have a list of tools, but eventually we will have sort of a sandbox space to allow people to play with the data.

Justin Grammens  14:47  

That's interesting. Yeah. So if people are you're obviously looking for other people to contribute data as well. Are you trying to create just an overall repository here? Is that kind of what your vision is in the coming years,

Ngan MacDonald  14:57  

we want people to provide data That's realistic, but that you know, other people can play with. And you know, eventually there'll be some data that's behind a firewall that researchers can get access to. But our first mission is to try and get it out there for an educational perspective, because frankly, most of the people that are working in this data field like myself, we kind of fell into this space, it wasn't intentional. And so my bigger picture for where the institute needs to go, is to eventually create this health data gymnasium, and then reach out to groups like, you know, that are historically underrepresented, as well as underprivileged and you know, create a natural progression for them to get into working in the health data field. Because I don't know about you, but everybody I know who is working in data today, they have like a PhD and 10 to 15 years of healthcare experience. And I always ask people, I'm like, Who do you think has a PhD and 10 to 18 years of healthcare experience? Right? So definitely not diverse. I mean, as much as I love working with, the reason why they are that way is because right now, everything that we do in healthcare, data, and AI is so bespoke, we haven't yet created what I would call like the open source of healthcare data yet,

Justin Grammens  16:19  

why do you think that is, I guess, are people afraid to share it is? Are there regulations and stuff around it? Do we just not have the data, we not looked deep enough,

Ngan MacDonald  16:27  

we have more data in healthcare, it's an embarrassment of riches. Right? The problem is that it's who has traditionally claimed ownership of that data. So in the past, you know, for instance, your clinical data, the data about, like, what gets done to you, and how your diagnosis gets held, with the providers or with your doctors offices with your hospital. And we haven't had any incentive to share that data. Because it's kind of like, you know, the gap sharing with, I don't know, some other retail, or sure, like, their customer data, you know, like, that's kind of how they viewed it, it's my data, I did stuff to you. And if I want you to come back, so I'm not going to share it. And on the insurance side of the house, they've had these Gag Rule contracts with these providers that say, you're not allowed to share this data outside of like, what we do health plan operations. And in recent years, you know, kind of in the waning days of the Obama administration, there was a bill called 21st Century Cures Act, which was trying to free up both healthcare research, as well as, you know, data itself. So there's a line in it that says that patients shall have access to their data, at no additional costs, or don't no additional effort. And what that meant is like, we can now make rules around giving people access to their data. And so then the ownership of the data has with you and I, and then they'll get to be the ones who provide data, to research and to like other uses.

Justin Grammens  17:57  

Interesting. Yeah, I was not aware of that. So so at the end of the day, I own my own data, all the prescriptions I take, or whatever it is, all the physicals, I've done, whatever. And it's up to me to basically say, Yep, I'm okay. With you sharing this publicly. Do you still think people are are worried about sharing that? Right, I think if you went to the general population and said Your data is not going to be open source, if you want to have it be like a part of that? Do you send some public worry in that?

Ngan MacDonald  18:21  

I think that probably the most recent worries that I've seen, have to do with the recent Dobbs decision. We don't have a national privacy law, our privacy is regulated at the different state level. So for those of us in healthcare, it's been not fun to, you know, like have to figure out oh, well, I can share mental health data, you know, for anybody over 14 in this state, but I can in this state, right. So by removing by not having a consistent national statute around privacy, and then now you've got different states making different regulations about, you know, abortion, and who has access to that there's going to be, we're going to have to figure out how to manage people's privacy, and how they share data. And really, one of my hopes is that we really work to anonymize data, create synthetic data, I think those are some of the ways that we can kind of still use data to further research without endangering patients themselves.

Justin Grammens  19:25  

Yeah, I guess that's what I was gonna say, as a follow up was, maybe people don't understand, but your data is anonymized. Like they don't really know that it's Justin who has all this all these conditions, for example, or whatever that has gone to the doctor for X, Y, or Z. They just know that somebody did and maybe they'd be a little bit more comfortable. I just think a lot of people seem to be in some cases, I think overly paranoid but that's that's just my perspective, and they don't maybe understand sort of how it works because I would say in general, most people aren't data analysts right they just they hear my information is now going to be out on the internet. Well, that's, that's a non starter for me. Well,

Ngan MacDonald  19:59  

even as some of The basic information that you would say, Gee, don't your new people in healthcare have good access to this? Like, who are all of the physician? And what are they? What are their different licenses? Are they allowed to practice across state lines, all those things, we don't really have a good handle on it, because we've got the system that, you know, we have multiple insurance payers who contract separately with these various adapters, and they're all holding their own records. And they're all held responsible for the accuracy of that record. But that's still way too many cooks in the kitchen.

Justin Grammens  20:33  

Yeah, I think they're probably all vectors or targets for people to be attacking anyway. So it's, it's it's a difficult thing. If you have all this stuff, manage in multiple areas, you it's hard to have one law or one regulation, making sure that everyone is following best practices in some way. Right? Yeah. Yeah, exactly. Well, that's, this is cool. So you, so you and the other people at the institute, there are sort of thinking about how how you can sort of bring this data to bear together, I guess, and then work with students and teams, to start thinking about new and creative ways in which you can solve some healthcare, the current healthcare challenges that we have today, is that an effective way of sort of summarizing what you're doing? Yeah,

Ngan MacDonald  21:13  

I mean, we're only at the tip of the iceberg in terms of trying to put that data and the tools in front of people, I would say, there's other people working in this space that I'm really fascinated with. So there's people working around like machine intelligence safety. For instance, we have a New York Institute that was just created at Northwestern, like in a broader sense about Gee, like, as we figure out machine intelligence, how do we make it safe? I think that that's fascinating, right? Like, just kind of think about this idea of like people not wanting to share data, what if they didn't have to what if you had read, you know, multi party computing, where you don't have to share your data, but you share your results, and that those queries can cross different platforms. And you can still do research, but you're not exposing that data? By moving it across different organization

Justin Grammens  22:05  

feels like maybe there's some blockchain, there could be some blockchain applications. Is there as you were sort of talking through that that's sort of kind of popped in my head.

Ngan MacDonald  22:12  

Yeah, I think blockchain would be a great application to use for, for consensus. I think a lot about, you know, there are situations in which you want to be able to allow different parties to know who is who should have access to your data. But you know, there are other times when you want to be able to immediately release that access, say, a divorce situation, for instance, you know, one minute somebody is, you know, your emergency contact, and the next minute, it's the last person you want anybody to call, right? I don't think blockchain is going to solve everything. I think originally when blockchain came out, we talked about it as hey, let's put the entire electronic health record on blockchain. Well, that's a lot of data. Blockchain is like, is really expensive in terms of processing, and compute. So we don't necessarily want to do that. But I think it's right for something like consent.

Justin Grammens  23:06  

Yeah. Yeah, for sure. For sure. And just the whole idea of a distributed computing model in some ways that, you know, there isn't there is not once one general, central organizer that holds all that information. Right. That's sort of the beauty of it, I guess, is that it's sort of spread within the network.

Ngan MacDonald  23:23  

Yeah. And there's a lot of distrust across the healthcare spectrum. And so that's kind of the other problem I think, that blockchain is trying to solve for is these computable contracts of, you know, parties that don't necessarily trust each other, but still have to work together.

Justin Grammens  23:39  

Sure, for sure, yeah. I want to work with you to stay at arm's length, right? What is the day in life for you? I guess why, as you're working with with this organization?

Ngan MacDonald  23:49  

Well, so my role is generally on the side of talking to people like you finding, you know, that network of folks who are working in this space companies, sometimes they're startup companies, sometimes they're companies that are looking to innovate, and trying to essentially be the matchmaker between people who have a problem that they need to be solved, and people who have the resources to solve that problem. At the end of the day, you know, like, I joke a lot that I do a lot of hustling. So I also do a lot of fundraising for the Institute as well. And just, you know, it's really about community building, which I know you know, a lot about,

Justin Grammens  24:26  

no, I love it. I love it, ya know, we can't make an impact on the world alone, right? It really does take a village, I think and the best ideas come when you collaborate, work work with a bunch of different people and rates, sort of one plus one equals three scenario. Are you guys publicly funded? are you funded through grants or how does that work?

Ngan MacDonald  24:42  

We are funded by Feinberg School of Medicine and Northwestern University. And we kind of you know, when it was launch, we had initial funding or like the first three years to say see if we can make something of ourselves. And so we're kind of getting to your Serena, and it looks like we're gonna, we've definitely made a name for ourselves. And, you know, we're gonna get be getting some faculty members as well, and just trying to create a bigger footprint at Northwestern.

Justin Grammens  25:13  

That's cool, you probably think back wow, you know, two years ago, I'd never really thought I would have been doing this right.

Ngan MacDonald  25:18  

Like I said, I thought I was gonna do sort of this volunteer thing for about six months. So come a long way from there.

Justin Grammens  25:26  

Yeah, that's a great story. It's a great story. So one of things I do like to talk to people about that have kind of, you know, gotten to a certain point in their careers, sort of have a chance to look back and see how they got to where where they are at. I mean, what sort of advice would you give anybody maybe wanting to break into this into this space, or coming out of school and kind of has a little bit of an interest in this? Where do you think they should probably go,

Ngan MacDonald  25:45  

you know, the problem with where I am, is the most people I know, either they're physicians who are practicing or doing research and go, Wow, there must be a better way to do this. Or they're on the insurance side, say they're like a statistician, and they say, Well, you know, like, there's, there's a wider world beyond this than just in the health insurance space, for instance. So there aren't really programs that I know of, but there are professional organizations that kind of feed into it. And so you know, AHIP, which is America's Health Insurance Plans has a bunch of educational material that I think is really good. If you're interested in the insurance side of thing, there is American College of Health Care executives, that also has a lot of this curriculum around the healthcare system. And then there's hemmens, which as you know, another organization, their conference, if you get a chance to go, it's probably it's huge. Every year, it used to be a McCormick Place in Chicago, and then it grew too big for that. And now it's I think, either Orlando or Vegas, everybody who works in the space shows up there some time at some point, also O and C and CMS, O and C is the Office of the National Coordinator for Health Information Technology. That's Adams, the government and CMS is the Center for Medicare and Medicaid Services. And so I think there's a lot of places where this information resides. But there's really no great framework for people to get into the space yet, which is what helped a gymnasium it's trained and saw, we want to create a bunch of materials for people, whether it's links to other sites, or material that we create that help them to get into different levels, outdoor data.

Justin Grammens  27:31  

Wow, this is this is great, it's great. I know, you know, I having you reach out, I guess. And I would say a participate in these in these conferences is great, I want to get you to attend one of our applied AI conferences and speak there, but even just one of our meetups to, you know, as well, we meet the first Thursday of the month. And so I know you and I are kind of working on a schedule to get you to sort of present and talk there. You know, are there any other topics or things that maybe I didn't cover that you'd like to share? I guess with the with the listeners,

Ngan MacDonald  27:59  

we sort of touched upon it, but I want to be a little bit more explicit, in that, for a long time, people haven't had ready access to their own healthcare data. You know, especially if you are somebody who's dealt with a complex medical condition, whether you've had cancer or some other chronic disease, you know that in order to go to the next specialist, you basically have to carry around binders full of your own information. And, you know, the regulations have been changing. In the last, you know, five or six years, where now, both your insurance payer as well as soon to be your hospital health system have to be able to provide for you your data in an API format, that is in a fire standard format. And so that what that opens the door for is different apps to be built, that allow you to use that data, however you need to use it. And so I think like what, I guess the message what what I was trying to say with everybody is that you now have the power that you didn't have before. You don't have to go to the basement of like the hospital and trying to make photocopies of the health record. Like they have to give it to you. And they have to give it in a format that you can ingest. And so I think that's huge. And and that not everybody can appreciate that. But people who are dealing with complex medical issues, that's something that they they should know that they have access to.

Justin Grammens  29:35  

I was not aware of that. Does that now provide, I guess, startups or entrepreneurial business opportunities for people to sort of start building these apps?

Ngan MacDonald  29:44  

Absolutely. Absolutely. There's a lot of different apps that are out there. My current favorite although it is really targeted towards specific complex medical conditions is a company called Citizen. They're now bought by another company but called in vitae who Does genetic testing. But just imagine if you had all your health records, then you also had your genetic data pulled together. And I'm not affiliated with them. So I'm not trying to say any, like endorse them in any way. But I just think this idea of being able to go and get your data, and also, you know, marry it to your genetic data, and be able to take that to your physician, I think the next part of applied AI that I'd like to see is have the artificial intelligence, synthesize that data in a meaningful way for you and your physician. And you know, like, I think I heard it on your podcast, with Neil Sahara, who was talking about youngest AI bot, and wouldn't be great if that AI bot understood you your data, your family history, and was able to synthesize all that data for you.

Justin Grammens  30:52  

Wow, that'd be amazing. I mean, that I think one of the powers of Well, a couple different thoughts. Number one is the powers of API's, you know, really allow you to create sort of this this mashup, right, sort of where you're mashing up all these various, these various data, or information, things to create something that is, you know, new and unique. And then what's, what is really cool to me is that yeah, the power of AI now is, you know, you can apply into intelligence, I guess that more than one doctor would have, right, you could bring a lot of different, I guess, knowledge to this data. So it's not just one doctor having take a look at it, you can sort of create a model or a power a numbers, I guess, to allow you to Yeah, again, eventually sort of act on it using this augmented idea. I really love love your augmented concept. So yeah, that to me is sort of fascinating, right, that we'd be able to, and that's probably what your what's your what your ultimate goal is, I guess, right at the institute,

Ngan MacDonald  31:44  

that's our ultimate goal is, you know, to actually use data to augment human intelligence. And we have a ways to go. But there's, there's so much new data, and new technologies that are being developed. It's pretty amazing.

Justin Grammens  31:58  

Yeah, cool. Well, man, how do people reach out to

Ngan MacDonald  32:01  

you? So I'm on LinkedIn, I'm also on Twitter. That's probably the easiest way to reach me.

Justin Grammens  32:07  

Oh, good, good, good. Cool. Yeah, like, well, we'll be sure to put your LinkedIn information and your Twitter handle in the show notes here on the applied AI podcast. And, again, like I say, I look forward to having you potentially here in a future month having you present at one of our meetups, because I think this is information that a lot of people need need to learn about, I certainly learned a ton here over the past 35 minutes or so as you and I have been talking about what is happening with my data, I probably and a lot of I guess a lot of people aren't even in the space. So they're they're just not really aware of, of even what's possible with the with these new technologies,

Ngan MacDonald  32:39  

there's a lot of things possible. There's also a lot of false starts. And so if you look at the history of AI and health, there's been a lot of money thrown at it. And I think with the intention that, wow, we have all these really smart people and smart technology, surely, we can solve healthcare. And, you know, the problem is, is that, you know, if that's not enough, you really need the healthcare expertise. You need the people who had to do it from scratch, not just with a bunch of technology, and a bunch of money, but like really had to work on

Justin Grammens  33:14  

it. Yeah, yeah, for sure. For sure. He is somebody who understands sort of the core problem, I guess, at its root sort of be the subject matter expert. Yeah. And, and create a collaborative space for everyone to sort of like yeah, learn like learn from each other and and build the best solution past exactly what we're trying to do. That's great. That's great, man. Well, I appreciate you for all the work that you do. Hopefully things go well here in the coming year. I'm gonna have you back here and we're gonna have like an update a future episode here are things are going so thank you so much for your time today and sharing the story and everything you've been doing.

Ngan MacDonald  33:46  

Great. And I love your podcasts. I learned a lot from it. So thank you for having

AI Announcer  33:51  

you've listened to another episode of the conversations on applied AI podcast. We hope you are eager to learn more about applying artificial intelligence and deep learning within your organization. You can visit us at applied ai.mn To keep up to date on our events and connect with our amazing community. Please don't hesitate to reach out to Justin at applied ai.mn If you are interested in participating in a future episode. Thank you for listening