"Clinical, Ethical, and Legal Considerations of AI in Healthcare Innovation"

Hi everyone. Welcome to The Big Unlock. This is Carrying on Paddy’s Legacy and this is after the acquisition of Demo by BigRio. I am Rohit Mahajan. I am the managing partner and CEO at BigRio and now with Demo as well. Very excited to have my Harvard Medical School colleagues with me. We are, we have an exciting discussion ahead of us.

I would like to request, uh, NJ to introduce herself. Well,

thank you, Rohit. I’m so excited to be here with you all. I’m N. J. Udochi. I’m a board certified family physician, geriatrician, HIV specialist, and addiction specialist. I’m the CEO of Summit Medical Group, a multi specialty group in Columbia, Maryland.

As a practicing physician for 35 years and physician leader, I’m in the trenches daily and I’m knowledgeable about the pain points that hamper the practice of medicine. I’m really passionate about finding solutions that will make our lives easier as we deliver the best care to our patients. I’m also obsessed with technology and how this technology can be used to improve access and quality of care to everyone, but especially to underserved populations here in the United States.

And especially in my home continent of Africa. In fact, my entire project during our training at Harvard Was focused on this subject matter, and so I sought other physician leaders whose goals aligned with mine. And voila, here we are. So I’m really excited to be with you all, Rohit, Tarul, and Craig, as we do this first recording focused on AI and its importance in healthcare.

Everybody, I want to say thank you for having me today. I’m ecstatic for participating in this podcast with my Harvard Medical School classmates.  My name is Craig McLeod. I’m the founding member of McLeod Law Group. I’m a health care attorney and founding principal along with with the rule with ellipsis health care leadership.

I reside in Lexington, Kentucky. I have  20 plus years of experience operating the health care space, delivering health care related services to the elderly, physically, mentally disabled. Our health care enterprise currently employs around 1100 people on a daily basis and serves approximately 1050 patients per day.

Around the state of Kentucky. My presence here is for legal input on AI and machine learning, and I’m just glad to be here today to participate and offer my perspective on the matter.

Hi everyone, this is Darul, and my background is actually as a pharmacist. I received my doctorate in pharmacy in 2002, but the last 10 years or so I’ve had a huge passion and interest in healthcare leaderships.

Specifically, and what brought us together is the Harvard Medical School Global Healthcare Leaders Program. Around that time, I decided to leave a job of 15 years really to focus on not only leadership, but affecting meaningful change in the healthcare ecosystem. And so we’re really excited to talk to you today about the implications  of AI from a legal, ethical, and design standpoint, and why this matters in this moment.

So to kick things off, what we’d like to start with is NJ’s standpoint from a practicing MD, and there are a series of questions that I will go through on why this matters in current practice. So my first question for you, NJ, is how is the healthcare landscape changing for primary care provider practices?

What are the drivers? How is value based care impacting things as they stand? And, you know, what motivated you to start your practice? Thank you, Tarul. I mean, in recent years, the U. S. healthcare industry has been transitioning to a value based healthcare system. That’s one that puts people and their health first before profits and shareholders.

And largely, this change has been driven by patients being more proactive in their approach to health care. And also, it’s led a lot of health providers now to begin making changes to the way they deliver care and the platforms they work with. And this is especially important in the chronic care space.

Also, CMS has seen the value of a collaborative approach to healthcare delivery, and they have pilots and demonstration programs in several states encouraging this approach to care delivery. In fact, in Maryland, we have the MDPCP program, which is one that we are participating in. We’ve just applied for that.

And these trends were further accelerated with COVID  19. And more recently, with the pandemic, the issues of workforce shortage, mental health, Physician and help us. workforce burnout have taken a lot of interest and sparked interest in trying to make sure that care delivery is modified in such a way to enhance the way we deliver care and decrease physician burnout.

With technology and innovation, especially with, uh, Gen AI and CHAT GPT and LNMs, The future is in blending of technology as an augmenter in healthcare provision. That’s where I see it. Trying to augment the care that we provide. I believe that these are the drivers in healthcare today. For me, what motivated me to form in my group was what I saw coming.

We would want to use technology to improve healthcare and, uh, dying out of solo practices. The administrative burdens placed already on primary care practices. Especially does not lend itself to being in solo practice, and it’s one in which if you want to provide very good care, it’s better for you to be in a group and collaborate together to provide and deliver the best care for patients.

Thank you. Thank you, NJ. My next question is what are the top concerns of primary care provider, physician, business owners in this landscape, would they rather be acquired by hospital systems or private equity players? Like, what does that look like for you? I think that primary care and business owners really are very focused on trying to survive the administrative burdens that have been posed.

Now, when you deliver health care is a big problem as well as a burnout, and I mentioned earlier about workforce shortage. It’s a big problem for us. Many of us had to look to outsource to help deliver care that the care that we need and also profitability. We are having a very difficult time trying to survive with the way the payers and what has been delivered in terms of payments.

So a lot of, um, providers and health groups and even solo physicians are looking to find a way to be acquired by hospitals or PE, private equity groups, just to help them in terms of delivering the best care possible because of these administrative burdens. Thank you. Now, in relation to this topic, how do you think this impacts patients and the quality of care, which, as we know, you know, from our program, but also just from a place of values based leadership, you know, patients and the quality of care, that’s the MVP.

So how do you think this impacts patients? This is something that is really near and dear to me. I think, you know, when I talk to other physician leaders, In the space and you talk to patients are like where I think one of them what really stands out. It’s the fact that the quality of care that they feel is being delivered is not where it used to be.

And this is where I think that it’s really important to blend technology to care delivery in a way that will be a win win situation for both providers and patients. So this is where I think the value is the value proposition is in this finding a solution that will help decrease the administrative burdens that occur, improve quality and access for patients.

And this is where if you have a solution that is that really meets those needs. That’s really what providers are looking for. And in the short run, it seems as if it’s more expensive. In the long run, it is really a solution that delivers this will lead to profitability. So it’s really taking a long, long look aspect to it.

You know, because right now, a lot of physicians, a lot of providers in the health space, are really burnt out because of all the administrative burdens that we face. and this leads to really workforce burnout and then the quality of care delivered is, is less than you would want. So trying to find or use technology that will help solve these pain points is where we need to be.

Excellent. So I think you actually answered my last question, which was how can technology help reduce the administrative burden and reduce burnout? I believe you’ve touched on those topics. But if you’d like to add anything else, Oh, I would like to add a few things. First of all, I think that it’s really exciting now because what we have now is Gen AI.

We have this large language models. All of you have also heard about chat GPT. I remember being in a conference in San Francisco earlier on this year, and I was able to test drive the proximity GPT and its application in solving authorization problems for physicians. And I can tell you it was wonderful.

You know, specifically, a lot of the physicians there were very excited because this is one of the pain points that we have in care delivery. So using technology to solve these administrative burdens is one way a solution can help improve their care delivery, and that will leave physicians and other health workers time to actually deliver the care that patients need.

So there are so many ways in which technology can even add value, whether it’s in the front end or in the back end, in terms of scheduling appointments. In terms of patient engagement, I know that Rohit, he was speaking about trying to help with care management. There are just so many ways, even in virtual, in virtual care, we’re all very familiar with telemedicine, whether it’s in, in audio or in video format, with reimbursement from the government and other payers.

This has really taken off, and it’s just finding ways in which to improve access to care for all populations, especially those living with chronic diseases. Remote patient monitoring and chronic care management. These are all areas that really lends itself to improvements in technology and care delivery.

So I’m, I’m really excited about this. And it’s solutions that actually can combine both that will be the best solutions for the future. And any company that can bridge these two gaps, they are the companies that really make it for the future.

Thank you so much, NJ. So actually, I’m going to turn it to you to ask me a question now, and we will continue on, but appreciate your comments.

So, Tarou, I know that there are many providers because we are out here trying to talk about how AI and technology can help benefit care delivery. Can you explain a little bit more to people listening what the ethical stand, what are the problems that we should avoid? Because we all know technology is good.

What are the ethical standards and what should we be watching out for? What makes for a good program?

Sure, absolutely. So I think where I’ll start, you know, especially after you’ve, um, given us some very thoughtful comments from the clinical lens, I think I’d like to start high level and just, you know, what is the current state of artificial intelligence in health care innovation.

Why is it such a big topic? So I think I’m going to start there. So we are definitely in a significant moment when it comes to health care technology, specifically powered by AI. And by  being aware and up to date on the implications, clinically, ethically, legally, we can ensure that we’re putting patients at the center of our care, as you described, NJ, which is.

Obviously, the MVP for all of us. AI is a term that’s applied to machine or software, and it refers to the technology’s capability of simulating intelligent human behavior, which includes instantaneous calculations, problem solving, and evaluation of new data based on previously assessed data, which is what we mean by generative AI.

And as you mentioned, NJ, AI applications in healthcare have literally changed the medical field, including imaging, electronic medical records, EMR, lab diagnoses, treatment, augmenting the intelligence of physicians and providers in a variety of different settings, new drug discovery. providing preventative and precision medicine, biological extensive data analyses, speeding up processes, data storage, and access for health organizations at large.

So AI and generative AI are creating massive efficiencies for providers and patients alike. With that, I’m going to turn it over to Craig for some of his perspectives, not only from a legal standpoint, but you know, why we should all be kind of sitting up and paying attention to this topic.

I’m going to start from a 10, 000 foot perspective and a historical perspective, of course.

So, historically, the evolution of technology has been based on a concept or principle called Moore’s Law. In 1965, uh, Moore’s Law was put forth by Gordon Moore, who was co founder of Fairchild Semiconductor and Intel. Intel, he was the CEO of, and everyone knows or should know who Intel is, but has a computer, cell phone, PDA, anything.

So Mr. Moore observed that the number of chips would double each year that would turn into double processing power. This is on a yearly basis from 1965. This law stayed pretty intact until 1975 when the law doubled to reflect that the doubling of chips per integrated circuit and doubling of the resulting processing power.

Would move to every other year more also established his second law, which was also known as rocks law, which states, even though the size of the chips on the relevant integrated circuit are reduced and the processing power doubles the cost of the physical plant. And the necessary R& D related to the advancement increases exponentially over the same time period.

The reason why this is important is it creates pricing issues which are inevitably passed on the ultimate consumer, which is personal on a business model or the personal consumer. Staying with Moore, his laws are based fundamentally on the historical norms of manufacturing and supply chain principles, as well as basic economic and supply and demand considerations.

There’s always physical work necessary to maintain the underlying basis of Moore’s law. However, the explosion of AI, and I use AI to include generative AI as well, and machine learning. Over the past year or so, these traditional considerations are becoming less of a factor to predict and forecast the increase of computing power and evolving technologies.

As AI and machine learning are not customarily limited by these factors, these physical factors as The processing manufacturing principles due to the advancement of self learning that’s done internally and within the relevant AI machine learning systems, the elimination of these factors allow for the exponential increase in advancement of the capacity of the relevant AI and ML systems that’s assuming that the information fed into the AI and ML systems are accurate and factual.

This requirement to effectively process information is being interfered with intentionally, specifically by the artistic community, and more about this upcoming on my next statement and probably future podcasts. Specifically, the Nightshade and Glaze programs that are being implemented by the University of Chicago.

Thank you, Craig. And with that, I think I’m going to lead us into, like, a more general question about, you know, what are the best practices in AI innovation and implementation? And to kick things off, I’m going to start with my sweet spot. Like, what are our ethical considerations and health equity best practices and why does this matter?

So AI is not going to replace clinicians, but it will advance caregiving capabilities. Clinicians must increasingly rely on human skills such as empathy and culturally relevant care. So for example, we have to train our providers in all different modalities, MDs, nurses, all caregivers, in meeting patients where they are.

This real world data feeds the algorithms that AI will influence and ultimately used to improve care and efficiencies. So, for example, in marginalized populations, we have to do a better job of understanding the factors and interventions that will close gaps in healthcare disparities. Another example is trauma informed care, unique ways to speak with and treat patients for people who have lived through gender based violence or other forms of violence.

And there are, you know, some really interesting discussions going on in all over the country on how to carry out this care. So we all must kind of sit up and ensure that we’re paying attention. These intentional practices will definitely influence health equity as AI technology advances. Otherwise, we’re at risk of leaving the same communities who have historically been underserved behind again and again.

And if AI is viewed as critical to healthcare’s future, then a diverse group of stakeholders must be engaged in the dialogue. So a group like ours, right? ethicists, economists, and policy makers. We have to all come together and have honest and wider discussions around how to implement ethical and equitable AI.

Craig, I’m going to turn it back over to you in terms of legal considerations and why it matters in terms of risk avoidance, management, aversion, and other thoughts that I know you have. So I’m going to turn it over to you. All right. So AI and machine learning is evolving so quickly. Everyone should know is the legal field and the resulting legal decisions and lawsuits.

They’re so slow. So this is a very new topic  that we as attorneys are trying to figure out how to speed our pace of evaluation and bringing these to light to expedite these type of decisions. The key here and understanding the AI and machine learning legal perspectives. is understanding where and how the protected and confidential information and or the data is stored, utilized, integrated into local or and or hosted external systems or platforms.

In the law, most usually confidential or protected information is just that until it’s not. And where you get to it’s not is voluntary disclosure, negligent disclosure, or for an authorization by an agent or principal of a business or him or herself. donating or allowing this information to be imported into an AI or ML system.

So that brings us into this confidentiality and these trade secrets. So, and I’m using this generically, meaning any confidential protected information to include protected health information under the U. S. Code states that an owner of a trade secret must take reasonable steps and measures to keep such information secret.

Well, what does that mean? That just means that you can’t go out and and claim you have a trade secret or protected or confidential information and then put it on a billboard somewhere. That’s the historical viewpoint. But however, we’re not in the 1950s. We’re not in Cleaverville anymore. This is, you know, not 2023, where everything happens over a computer.

Information is stored everywhere. And what happens when an employee or an agent of this information, a steward or a custodian of a company or an individual just happens to link an information source or a depository containing this great secret or protected health information or any other confidential information to and it doesn’t even have to be intentionally.

It could be unintentionally be negligently links this information. information or this platform to an AI or machine learning system. Well, the AI and machine learning system, typically, when you feed information into it, it doesn’t really care where the information comes from. It’s utilizing it. And once those processes become active, there’s no going back.

So once the trade secret is input into this AI and machine learning system, more than likely your trade secret is considered public domain. Anyone can use it for any reason at all. There’s about four or five big ticket lawsuits going on right now around the United States. and tentative. If you read the initial briefs and initial decisions, they’re pretty much in lockstep with each other that they are.

The courts are saying, Hey, you gave it up. You fed this into and made it part of the public domain. You’ve lost any protection that the courts can give  you. the patient, the data privacy. So everyone knows how important The protected health information or PHI is from a patient’s health care and societal positions.

However, one problem immediately rises when the AI systems become aware of this data. As I just brought to light, how does the AI system know that this information is still confidential? And how does it know that some of this information that was confidential hasn’t been made unconfidential or public by release?

Or by a court order. It doesn’t. So this is why the importance of safeguarding information from an AI or machine learning environment is so critical. Another issue that I sort of want to bring to light is the facial recognition technology. that may be hijacked for improper, immoral or unethical purposes.

And first thing that came to my mind is it’s akin to the past concerns of DNA information and the sharing of the same on Ancestry. com 23andMe because of the notion that this information really is the last line that police agencies can utilize to find and crack cold cases, this, that and other.

Ultimately, That the jury is still out on how to utilize this information, this DNA information, how it is being utilized after being deposited on a third party site. And I mentioned earlier about the bad actors, the poison pills, there are some participants in the space now that are attempting to mask or thwart AI machine learning’s ability to learn to do just that.

And specifically, I’ve been reading and studying the glaze and the nightshade programs as, as I said before, from the University of Chicago, the baseline notion here are these artists that are seeking protection of their work. They’re artistic and music and things like that. They shouldn’t have their work stolen, which is serious and valid concern.

I think we can all agree on that. But AI doesn’t seem to steal this information. They’re just utilizing it to learn for future endeavors. And my initial observation on this position by the artists utilizing Glaze and Nightshade is, Did they or did they not go to art school? Did they or they not learn from Van Gogh and Renoir?

Didn’t they, weren’t they influenced by those artists? I doubt that they gave credit or anything to those artists, but they learned from them. They created their own style. They didn’t steal it. They just were influenced by their artwork. So it seems like, to me, the artists utilizing these two programs sort of want their cake and eat it too.

Tarul: Thank you, Craig. Thank you so much for your comments. With that, I’m going to hand it over to Rohith to walk us through, you know, based on everything you’ve heard so far today, how does this influence your design best practices?

Rohit: As NJ and Craig, and you mentioned, this is a revolution. I think the AI is becoming household name with chat GPT has reached the inflection point where you can literally, I mean, everyone’s using it for some purpose or the other.

So it took a long time. AI has been around for a long time. I’ve been playing with AI since my engineering school days way back when, but it has now become ubiquitous to the point where. It’s easily available and, you know, one can log in and use it, especially, let’s say, the large language models and the applications are immense and, you know, very, very broad and across industry segments.

But in the healthcare industry segment, as we all know that there are obviously privacy concerns and patients lives at stake, right? So when we design AI systems. We do have to keep in mind all the various aspects that we discussed on the call today, the ethical, the moral, you know, the patient centric, all those angles have to be kept in mind while designing a solution.

And I’ll illustrate it by an example of a. You know, project that we are currently doing for one of our clients, and hopefully that will bring to life some of the design approaches. So we are working with this very innovative digital health company that, you know, NJ mentioned I’d shared with her is working on a building.

you know, a care planning tool. So a care planning platform. And this will apply for, especially for chronic diseases and, and patients with, you know, multiple disease conditions. So here, the challenge was, how do you build a wall so that the large language model is not infringing on any private data of, you know, the patients.

So there are no possibilities, especially with large tech companies who will allow you to create these walls so you can stay in the swim lane where you’re building a large language model, which is custom, which is learning. From the data that is your proprietary data, and it is staying in that, you know, let’s say silo.

So it’s not getting external, at least outside of that wall. And it is using basically the large language models to kind of use that data set, which is then proprietary. So these are some of the design conditions when we approach projects. Typically, you look for a, what we call a POC, a proof of concept.

And then as you build out the proof of concept. And you kind of put it, put it in front of the stakeholders. They can test it. And usually we’ve also found that even in this project, there is. a clinician in the loop. So it’s, you know, it’s still, we need that human being in the loop where someone’s looking at, let’s say the output of this large language model and making sure, you know, before it goes into the next step that someone, you know, certified, qualified, a  nursing The staff or a physician’s kind of looked at it before it is being shared with the patient.

And they’re kind of like signing off on it. So I think this, this whole landscape is changing very fast. There is literally innovation happening almost every day. It’s explosive. I don’t know which law moves law, which law we should call it, you know, uh, now the AI law, the journey I law, but things are changing very fast.

So. We have like, especially in the company that, uh, you know, we have very, very qualified data scientists who keep up to date with all the current literature. And that is how we are able to bring to bear our expertise and technology to create these robust POCs and pilot projects for our clients.

Thank you so much, Rohit.

So, you know, as we kind of move towards wrapping up our discussions, I guess now is the time for the so what. What should our multidisciplinary stakeholders take away from this podcast? Where do we start? What actions should be taken?  And Rohit, I’m going to come back to you first, and then we’ll have NJ kind of describe how advances in this space can improve the lives of providers and patients alike using a real world example.

So I’ll chime in with a very small example. I think this stems from a recent discussion I was having with someone who’s at a very large healthcare provider system in New York. And he was saying, he was saying, Rohit, I have so much data in my repository. I don’t know what to do with it. So I think that is really the starting point.

That organizations have data sets. Providers have data sets. This is a gold mine, right? And how do you leverage that data set? is where, you know, AI and Gen AI comes into play because AI is good at making predictions. So if you have data sets, especially which are retrospective, which are  longitudinal, those are immensely powerful, you know, base for building AI and Gen AI models, which can truly help with patient care.

In my mind it is like for any AI project everyone knows 80 percent is data engineering and 20 percent is data science and that still remains true.

Thank you Rohit.  NJ, from a provider standpoint, from like a practicing MD standpoint, like what do you think we need to do? Like what do people in this space need to be doing in terms of action?

Well, it’s really exciting because the healthcare industry and we know providers, we produce a lot of data like Rohit has said, and these massive amounts of data from multiple sources. Is exactly where A. I. And machine learning can add value. This is especially in health care delivery. I’ve talked so many times and I keep I don’t there’s no day I don’t wake up trying to figure out how to solve the administrative burdens and improve health care delivery here in the United States.

And also around the world. So I think it’s really in helping solve those pain points. So here in healthcare, AI can add value, especially like you said, in healthcare diagnostics, it’s been very valuable with us, you know, many of the providers in radiology, pathology, inpatient monitoring. Also we have AI that has been, and many tech companies have also helped in improving care delivery in terms of.

Predictive modeling. We talked about virtual care, you know, telemedicine, wearable devices. So there’s so many areas in which AI can really add value. But really, I think that the solutions, the technological solution that we want is one that really can blend the pain points, you know, that we experience in care delivery and produce a solution that can solve those problems and at the same time improve patient outcomes and access to care and ensure profitability, you know, for health systems, provider groups and organizations.

So a solution that can combine these elements is definitely some, the solution that, you know, I think the healthcare community is looking forward.  We, like I said, in the conference and meeting that I had in, uh, attended in, in San Francisco, there was so much excitement with the Doximity GPT because we could really use it right there and, and solve the prior authorization problem.

So on a daily basis. There are so many problems that we have, whether it’s on the front end of care delivery or in the back end. And so really, where I see that the value is going to be is in trying to use this, to use AI and LLMs, you know, to solve the problems that we have as we, in care delivery, as we see patients.

So that’s really where I think the value add is. Also, we talked before previously about the back end in terms of coding, patient coding in the RCM process, revenue cycle management. Those are areas I think that technological advancements are needed, and also startups can focus on in the healthcare space.

Because when you can combine both the front and back end to solve those problems, there is where the value is. This is where you’re going to see a lot of excitement. And I think in the end, the problems that we’re experiencing as providers that solving the burnout problems, physician charting, in vitro scribing, these are all areas.

That physicians are looking forward to finding and using AI in trying to augment the care that we provide on a daily basis.

Excellent. Thank you so much, NJ. Thank you, Craig. And thank you, Rohit, for this wonderful opportunity to come together and talk about this important topic. So I’m going to hand it to you, Rohit, to close us out.

Thank you for the excellent panel discussions, NJ, Tarul, and Craig. It is great to get together virtually. We will surely follow on with more podcasts. And I would like to end in closing by giving a shout out for a book that I recently wrote and has been published. It is for people who would like to learn more.

The name of the book is Quantum Care. And it is a deep dive into AI for health research and delivery. So  thank you so much once again and looking forward to the next one.