Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 71: AI in Business - Healthcare Use Cases

Episode Date: August 2, 2023

How is AI taking form when it comes to helping businesses in healthcare? Dr. Nadia Boutaoui, Founder of NanoNares Inc,  joins us and shares valuable insights and use cases, discussing how AI can improve operations, enhance patient care, and revolutionize personalized medicine.Newsletter: Sign-up for our free daily newsletterMore on this: Episode PageMore on this topic in today's newsletterJoin the discussion: Ask Dr. Nadia and Jordan questions about AI in HealthcareUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:[00:00:18] Daily AI news[00:05:55] How AI improves healthcare[00:07:30] AI's role in caring for aging people[00:11:17] Mistrust of AI in healthcare and hallucinations[00:17:14] How AI helps with chronic diseases, cancer, and new drugs[00:19:41] AI's role in healthcare customer service and burnout[00:21:58] Fragmented US health systemTopics Covered in This Episode:- Caring for aging individuals in their homes    - Growing issue with the aging baby boomer population in the US.    - Anticipation of integrating AI technologies for remote healthcare support.- Future of personalized healthcare    - Data monitoring and chatbots trained on personal data for treatment.- Concerns about mistrust of AI and hallucinations    - Inaccurate information provided by language models.    - Need to address concerns as a society.- Using chatbots to answer FAQs and provide personalized responses    - Segmentation of patient population for targeted communication.- AI to identify bottlenecks and improve staff utilization    - Burnout as a significant problem in healthcare.- Supercomputers for personalized treatments in cancer    - DeepMind and IBM Watson using large data sets.    - Collaboration between Regeneron Pharmaceuticals and Geisinger Health System for new drug development.- Human oversight in advanced data analytics and AI    - Human validation of recommendations.- Concerns about data safety and third-party access    - Importance of complying with HIPAA regulationsKeywords:technology, healthcare, regulations, burnout, complexity, cost, training, workflows, safety, compliance, HIPAA, adoption, AI, electronic health records, intervention medicine, prevention, asthma, scheduling, specialists, patient care, aging individuals, diabetes, heart disease, centralized data centers, statSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. How is AI going to change the healthcare industry?

Well, there's probably a lot of ways, and that's one of the things that we're going to be talking about today on everyday AI. This is your daily live stream podcast and free daily newsletter to help everyday people, like you and me, not just learn about what's going on in the world of AI because there's a lot, but how we can also leverage it, right? Because that's the important part. We can read all the new stories and look at all the new tools and techniques, but if we can't put it into use in our day-to-day lives and understand what it means, then we're just going to get left behind. So let's keep up together. And let's first, before we bring on our guests and we talk AI and business

and healthcare use cases, let me just remind everyone, this is a live show. So if you have any questions, please drop them. But before we get to that, let's first talk about what's going on to the world of AI news. So Warren Buffett came out and said that unlike just about everyone else, he's not all in on AI as an investment yet. So we talked about this earlier, but how just kind of the AI related companies are really driving the U.S. stock market. And, you know, Warren Buff, the steamed investor essentially said, even though he's amazed by Chat Chbitt, he is still cautious about investing in the AI industry. All right.

Second, Instagram, kind of a leaked memo, just showed that they will be warning users of AI content. So this is, again, a leaked document. A leaked document just kind of came out, and it shows that they're testing labels to warn users if the content generate was either generated with or edited via AI. So that one's going to be interesting. to see how they even label it, how they warn other users.

But I think that signals a bigger shift, right? I think we're going to start to see this a lot, especially with images, video, and audio. I don't know how it's going to work, especially for text, but Instagram here, one of the first bigger companies, you know, kind of announcing this. All right, last but not least, and kind of relevant to our show today, Google and DeepMind. So, DeepMind is kind of Google's AI arm, so to speak. but they announced Med Palm M. So we've talked about Med Palm on the show before, but without getting too technical, but we'll actually dive into it maybe a little bit today. Med Palm M is a new

multi-model large language model. So that's a mouthful, but think chat GPT for medical. That's Med Palm. And they just announced Med Palm M, which is multi-model. So what that means is, you know, the ability to process text, images, and even genomes. So why? wild, right? So, you know, this new Med Palm M will be able to encode and interpret biomedical data. Very exciting things happening in the medical field with AI. So speaking of that, let's talk that. And we already have a lot of comments coming in. Fantastic. So as a reminder, if you have questions for our guest today, if you want to know about AI and business and healthcare use cases, please drop a comment. But I'm very excited to bring on our guest for today.

So maybe you've seen her in the comments for the Everyday AI show before. Maybe not. But regardless, very excited to bring on Dr. Nadia Bhutawi. She is the founder of Nanonair's Inc. And Entrepreneurs for Healthcare Tech. Nadia, thank you for joining us. Absolutely. Very happy to be here to move from the comment into this page with you.

Yeah, absolutely. That's something I love. about, you know, kind of the everyday AI show is we have people not just, you know, even here as an example, we have Dr. Harvey Castro jump in the comments here saying, glad to be here. He was on the show before. So, you know, Nadia, there's a lot of things going on in the AI world, right? It's kind of hard to keep up, even on a daily show, right? What are your thoughts specifically with what's going on in the healthcare field and AI because there's so much going on? What are your thoughts in some of these recent developments?

I think AI is here to stay. It's not going to replace humans, but a person who knows how to leverage AI will replace somebody who doesn't. In the healthcare field, the Accenture, they estimate that AI applications can generate $150 billion in annual saving for the US health system. So that's huge, especially that the US health system is among the most most expensive in the world. So there are so many areas that where AI can bring value, not only improving the outcomes

and also improving efficiencies for the healthcare systems. Yeah. Absolutely. And that's even that right there, right? I have friends and family who work in health care. And efficiency always seems to be one of those major words that comes up. You know, when we talk about how can AI help in health care? healthcare or, you know, large language models even, right? Because from the patient side,

you know, one thing I think we always talk about is, hey, it's so hard to get in to see my primary care doctor or to schedule a time with a specialist. So how do you see that improving in your field, even for, you know, I guess the on the patient side? I think you hit the nail on its head here. I think AI can improve operations for electronic health records, which using large data analysis to help the medical professionals identify the high-risk patients and move from intervention medicine into prevention. So if somebody is flagged for some reason that they may have an asthma episode in the coming six months, instead of doing the intervention when they show up in the ER, they can be flagged to see

their PCP, the primary care physician, or even a specialist, even before they get worse. So I think that's one potential application. Another one is for having chat boxes that can help with scheduling and removing that friction of availability of the specialists that we know they are very tight on time. So they can better manage the delivery as well as the satisfaction for the patients. Another one, a lot of people, especially in the US,

there is an aging population and they would love to age in their homes instead of, you know, in other facilities. And having those monitoring over time can help identify almost in real time their conditions, especially chronic conditions that trust diabetes, heart disease and so on, so they can get the care they need before it's too late. And that's so important because, you know, it is all a timing thing, right? It is. Yeah.

And just real quick, because I do want to follow up on that. But I do want to shout out those of us, those of you joining live. So thank you, Juliet, for joining us from Connecticut. Carlos, tuning in from Spain. Thank you, Carlos. Ellington. Wow, so many people this morning. Woozy from Kansas City.

Lynette, thank you. I can't get to every, all of them. but if you do have a question for Nadia about AI and healthcare, please drop it now. So one thing that you mentioned that I think is going to be paramount, really, and how we can actually use AI and all these, you know, advancements with large language models, but is caring for the elder, or not even, I'm not even to say the elderly. I'm going to say aging, aging people in their homes, right? Because that's a reality with the baby, at least here in the years,

US, you know, we have the baby boomer population is aging, and that's going to be a huge issue. So how do you, I guess, what would be your recommendation to people maybe who are starting to look for those options and now you see all these advancements in AI? And I'm sure in the next year or two, there's going to be, you know, options to, you know, somehow integrate or chat with a doctor like that to be able to care for your elderly in the home. So how do you see that playing out? And what advice can you give to people to balance the, I guess, the very personal level of caring for an aging person versus these new technological advancements? Yeah, I think the AI powered virtual assistance and chat boxes, they will become the norm in the future. We're patient. They will have their

own virtual assistant to track that is trained on their data, on their history, on their genome, so they can track their blood pressure, you know, sugar levels and so on. And they can give them almost feedback instantly, whether to flag it in a way that you may need to see your PCP in the coming, you know, week or two. The key is to keep this technologies accessible so that it will be used by these aging population. These chat boxes, chat bots also, they can support and ask the questions about their medication. Medication compliance is also an issue. Many people, especially with chronic conditions, they don't adhere to their medication regimen,

so they can help them with potential side effect if they are on multiple drugs, so that can prevent an adverse effect for these aging population. They also can help them with streamline their appointments and their care. So I think another one in terms of wearables and so on, we will move from the wearable technologies where you have the device on your body to technologies that are within walls in smart homes. So they will monitor almost 24-7. And whenever there is a change in their patterns,

it can directly send a signal to their healthcare provider to contact them. So I think that's the two main applications I see. The key for technology is get feedback from the users that are going to use your technology. So it's based on the problem they experience, not your assumption of the problem they have. Yeah, so what you talked about there, Nadia, for someone like me, you know, I'm a self-admitted dork. I love AI. I love data. But that's not the case for everyone, right? So when we talk about that future, right, where it is, you know, your data is being monitored and you're able to have much more personalized healthcare and maybe almost like a chatbot that's trained on

your data to help treat you, right? One thing that comes in my book, mine. And, you know, Ben here has a question as well, which I think is great, saying, is there a barrier, or sorry, a higher barrier in healthcare to overcome mistrust of AI and hallucinations, right? Because for anyone out there who has used a lot of large language models, hallucinations are a problem where essentially you get inaccurate or just straight wrong information. So, you know, when we talk about that application in healthcare, that's obviously something a lot of people are going to be worried about. Like, what if my personal, you know, chatbot that's trained on my help data gives me wrong information? Like, how can we address that just as society? And, you know,

because it's exciting to think about. But obviously, you know, big, big things to take into consideration. That's a great question. Thank you, Ben. I believe that for any advanced data analytics tool or AI. There should be some human oversight. So whatever the recommendations that gives you, it's not, it will be checked by a physician or a trained professional before giving the get-go for the treatment. That's one. The second is when it comes to a hallucination and for the chat boxes, especially, I think having that human oversight will mitigate that. The second thing that a lot of people worry about is the safety of their data, especially, you know, could it be accessed by a third party, especially with HIPAA. I think it's very important to the identify the information and to have diversity within the development.

teams to to avoid biases because we know if these chat boxes or these AI models are trained on existing data that may have embedded biases it's going to be amplified so having that diversity even in the development stage and having a trained professional to you know valid to have the go-no-go from that output is key yeah and you know I was I was kind of thinking when you were talking there about just privacy and data and kind of, kind of related, you know, and I think when people are hesitant maybe about AI or large language models in healthcare, I think so many times when I'm seeing my primary care physician or something, that person is then just turning around and Googling something a lot of times, right?

So even with that, you know, is this, you know, is using large language models in health care that big of a step forward if, you know, so many times, you know, physicians, you know, nurses are just turning to the internet anyway. So I guess how different is it or what other implications are there that maybe the average, you know, patient may not be aware of, you know, when starting to think of a future of more large language models in health care? just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's Creative Agent, Firefly AI Assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the Assistant. The Assistant orchestrates multi-step workflows, drawing on 60-plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator, Premiere, Lightroom Express, and

more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. I think technology is here to stay.

And as humans, we've evolved to adapt to new technologies. In some cases, there are tight regulations, especially in healthcare. So there's this resistance to applying or, you know, these new tech in their workflows. And it's a very, not only the regulation, but the burnout is real when it comes to health care providers. And if, for instance, your tech needs a 10x to be adopted in a different field, it needs to be 50x in order to be adopted in workflows in healthcare.

Because those, when you think about adoption, it's not just applying that technology. It's training the personnel, changing the workflows. So the cost of acquisition is not just that purchase initial price, the service maintenance, the safety, the HIPAA compliance, the regulation. It's way more complex. And because of that, healthcare is a little bit on the back end. They are not the early adopters of new technology because of that.

Right, which I guess, you know, makes sense, you know, because, you know, when you think about your data and privacy, I think most people go to two things. They go to their personal health information and they go to their money, right? Which is maybe why we've seen maybe faster implementation in other sectors versus, you know, some areas in finance and just medicine in general. So, you know, you mentioned appointment scheduling is maybe one area that is, I guess, ripe for some innovation. What other, so whether it's on the physician side, you know, aspects of this new technology that may help physicians or even just the relationship on the patient side, what areas do you think, given what you just said about how, you know, in medicine or in the medical field, it's not always early adopters for tech.

But what else do you see happening, you know, maybe first or next in your field? Yeah, I think you mentioned DeepMind by Google. There's also IBM Watson, so supercomputers are using very large data sets to do predictions for chronic diseases, personalized treatment for cancer treatments. That's another area that will see a lot of advancement. And cancer research and personalized medicine in cancer treatment is generally at the forefront of innovation compared to other. that area. So I see there will be a lot in that area. Also, when it comes to combining genomics and electronic medical records to create new drugs, one example that come to mind is a collaboration between Regener on pharmaceuticals and Geisinger Health System. So a few years back, they sequenced

up to a quarter million people, their whole genome, and the health system had the electronic medical records for years in that region. And that allowed with the large data analysis to find drugable targets for new drug development. So I see a lot of that also moving forward collaborations to create more personalized drugs for specific populations because we know what's on the market now was largely developed on white populations, and it doesn't work as well for African Americans or other ethnic groups. So we'll see more development in that area, too. Yeah, and I do think that that piece is exciting. I think it's much needed. So, you know, one other question that I had for you, Nadia, is, you know, we've talked about different applications in the, in the healthcare field, you know,

like medical technology, like personalized medicine, so many things. But I know that you also have use cases on the business side as well. So, you know, maybe I actually know that we have some listeners who have their own practices or, you know, maybe they work in a smaller healthcare system where maybe they have a little bit more flexibility to grow their business. But even on the business side, you know, real quick, maybe what are some of your, maybe personal use cases or, I guess, different aspects of GPT and AI technology that has you excited for growing things on the business side? Absolutely.

I think you can create chat boxes to answer FAQs based on your data instead of going to the internet. This way it's personalized to the people you serve. It's based on feedback from the patients you serve. So that will be one application. Another application is segmentation of your patient population. This way, when you create, let's say, leaflets. if you're serving a Latino population, it will take into account their culture, the language

that they that would resonate the most with them. So having that personalized communication in medicine is critical. Processes, look at your supply, your not supply chain from like the get go by internal processes. Find bottlenecks and use AI to relieve those bottlenecks. It will free up your time. It will increase your time. It will increase. the utilization of your stuff. So if you have a bottleneck and you know that you get only 50% of usability out of, you know, a nurse or a PA or so on, and 150 in a different step of the process, you can strategically move your staff around. It will help you the retention. It will increase productivity and it will reduce burnout for your staff. And we know it's a big problem.

Yeah. Oh. Absolutely. I think anyone who has a family member or friend in health care, I think burnout is usually one of the first things that comes up, right? And I think that the AI technology is hopefully something that can help in that. A couple of questions wanted to get your thoughts on here, Nadia. So Dr. Rastafa is asking, so, you know, will these systems that we put in place for privacy or discrimination, are they going to vary with the political landscape? You know, that That is an important question. It's like also, how can these things even roll out, right?

Will it be the hospital systems, you know, putting these in place regionally? Will it be state laws? Will there be federal mandates? You know, again, we can't ask you to predict the future. But, you know, what do you think of this question from Dr. Rastafah here? That's a great point, Rastafa, because the health system in the U.S. is very fragmented by state, by health systems. and so on. The insurance companies, I think, are seeing the value in congregating large

data sets from different health systems, and they're working actively to put all data in one big place, and then different health systems across states can have access to that. So I think having those centralized data centers, either that cross-stays, that cross-stays, state borders would be key. This will allow, for instance, in emergencies or pandemics or other, you know, disasters to get access to these data that could be critical in dispatching the different, not remediation, but plans cross borders, cross states. I agree that, you know, there will be because of how the U.S. is set up that. each state will have their own, you know, laws.

But again, I think it's a combination of having local autonomy versus having access to larger data sets if needed, especially if it's funded by the federal government. Yeah, yeah. That's going to be an interesting one to see how it plays out, right? Because we hear all of these advancements in technology and, you know, what we've talked about, like burnout. It seems like a natural fit. But, you know, like you talked about, it's not that simple to implement these things. A lot of systems, they know that the value is in the data and they protect their data. Yeah. And not willing to share it with other people. Yeah, absolutely.

All right. And I think, I think to wrap up, we have one last question here from, from Harvey Castro saying, do you know, so specific question here, but asking if you know of any specific, you know, chat GPT or maybe GPT companies in healthcare that you can recommend. Yeah. So are there any, you know, we kind of talked about the deep mine and the palm M, but are there any other, you know, companies or entities that are working on getting this thing that you talked about, this personalized chatbot, have you seen that yet or is it still a while's off? I wouldn't recommend like one specific one because I want to see how they evolve first. But I think between Amazon, between Apple, who has like the monitoring even for your brain waves in their AirPods,

we will see more like healthcare related from the big tech giants. And we know healthcare is a difficult area to get into, even when Amazon worked with two other companies and they created Haven within three years, they dismantled it because it was so difficult. So that's why I'm hesitant to recommend one particular company because of the fragmentation in the system. Yeah, yeah, that's a good point. And even Apple, yeah, I'm excited to see what happens there too. You know, they've been, they tease kind of their Apple GPD.

And like you said, they've been saying that they want to focus their AI on health care and wearables. So, yeah, so many things going on. Exactly. And their Apple Watch monitors almost everything when it comes to health care, your heart rate, you walk, your patterns and so on. So I think in the future, even if they're not doing it themselves, they will have strategic partnerships with other people that do. Yeah, absolutely.

I think you hit it right. You know, there's so much data out there. And I think the future of healthcare is exciting, but so much going on. So thank you, Dr. Nadia Bhutawi, for joining us on the Everyday AI show. We appreciate your time and insights. Absolutely. Thank you for having me. All right.

Thanks everybody for stopping by. And if you have questions, watching this on the recording. session, you can drop them in the comment and I'll come back and answer them. Oh, that's true. Nadia's great at going through and making sure and connecting with others. I think that's one thing that I love about everyday AI is just, you know, experts that are going in and they're just helping people learn because we all have to learn together. So speaking of learning, make sure to check out our daily newsletter. So go right now and sign up at your everyday AI.com.

So Nadia has actually other resources. We didn't even have time to talk about some exciting initiatives that she's working on. We're going to be sharing about those in the newsletter, breaking down today's conversation even more. So thank you, everyone, for joining us. And we hope to see you back tomorrow and every day with Everyday AI. Thanks. Thank you, everyone. Thanks a lot.

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