Season 5: Episode #141

Podcast with the Harvard Medical School Global Leadership Alumni

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

paddy Hosted by Rohit Mahajan

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In this episode, four colleagues from the Harvard Medical College Global Leadership have come together to discuss the implications of AI in healthcare in the light of clinical, legal, ethical, and design.  

Dr. Njide Udochi, CEO, Summit Health, talks about how technology can be used to improve access and quality of care for everyone, including the underserved populations, in the United States. She also explores the changing healthcare landscape for PCPs, the drivers, impact of value-based care, and more.   

Craig McCloud, the founding member of the McCLoud Law Group, comments on the rapidly changing Moore’s Law, and its complexity. He also talks about the impact of ML/AI on legal aspects and why it matters in terms of risk avoidance, management, aversion, and the “poison pills.” 

Tarul Kode Tripathi, Founding Principal, Ellipsis Healthcare Leadership discusses the current state in healthcare innovation and the clinical, ethical and legal implications of AI/ML on innovation.  

Rohit Mahajan, Managing Partner and CEO, Damo and BigRio, talks about how all of these will influence the design best practices in AI/ML. Take a listen.

Show Notes

03:43how 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?
06:08 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?
11:40How can AI and technology help benefit care delivery? Can you explain what are the ethical standards and what should we be watching out for? What makes for a good program?
16:39What 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?
18:23We have to all come together and have honest and wider discussions around how to implement ethical and equitable AI. What does it matter in terms of legal considerations, risk avoidance, management, aversion, and other thoughts.
24:47Based on everything you've heard so far today, how does this influence your design best practices?
28:47What should our multidisciplinary stakeholders take away from this podcast? Where do we start? What actions should be taken?

About our guest

Dr. Njide Okonjo-Udochi MD,MPH,MBA,MS FAAFP has over 20 years of experience in healthcare management, policy, and research, I am a passionate and visionary leader who strives to improve quality, access, and outcomes for all patients. I am the CEO and President of Summit Medical Group, a physician-led multi-specialty group of independent practices that focuses on reducing disparities and delivering value-based care in the Medicare Advantage and Commercial space. As a founder and president of Millennium Health Group, PC, and a medical director of several organizations, I have successfully built high-performance teams, executed strategic plans, and integrated horizontal and vertical services. I am also a digital health innovator and consultant who works with medical device companies to provide transformative care that bridges the gap for minority populations. I hold an EMBA, an MPH, and an MS in Healthcare Policy and Research from prestigious institutions, and I have published multiple articles in international journals. I am committed to advancing global health and creating positive change in the healthcare industry.

As a founder and president of Millennium Health Group, PC, and a medical director of several organizations, I have successfully built high-performance teams, executed strategic plans, and integrated horizontal and vertical services. I am also a digital health innovator and consultant who works with medical device companies to provide transformative care that bridges the gap for minority populations. I hold an EMBA, an MPH, and an MS in Healthcare Policy and Research from prestigious institutions, and I have published multiple articles in international journals. I am committed to advancing global health and creating positive change in the healthcare industry.

Tarul Kode Tripathi is a purpose-driven executive and community leader with two decades of progressive experience and success in the healthcare ecosystem. Her key strengths include low-ego, values-based leadership, high emotional intelligence, and consistent execution through curiosity and growth. She's passionate about transformational leadership, healthcare equity, technology, and innovation. She currently serves as Founding Principal at Ellipsis Healthcare Leadership, Advisor at Redesign Health, Remedy Product Studio, AMCP Heath Disparities Committee, and various equity focused community organizations. She earned her Doctorate in Pharmacy with Honors through an accelerated program in 2002. She completed the Harvard Global Health Care Leaders program in 2022. Her past professional experiences include Chief of Staff at Spora Health, Vice President, Senior Benefits Consultant with Segal, Director of Account Management at MedImpact Healthcare Systems, Blue Shield of California, and UW Medicine.

Craig L. McCloud, Esq. is an executive leader, general counsel, litigator, negotiator, and advisor with twenty-five years of success in health care and various other corporate sectors. Rich expertise in organizational and operational planning and growth. Recent highlights of professional training include the Harvard Medical School Global Healthcare Leadership Fellowship and Yale Executive Healthcare Management Program. Founding partner and General Counsel at Ellipsis Healthcare Leadership. Passionate about opportunities to lead through natural executive presence, with a focus on strategy, growth, and risk management in the healthcare ecosystem.

Q. Tanya, can you tell us a bit about LCMC and the populations you serve?

Tanya: It’s kind of a long journey, but I’ll try to wrap it up there, summarize it. We are originally founded by Louisiana’s only freestanding Children’s Hospital. LCMC originally stood for Louisiana Children’s Medical Center. We just go by LCMC Health now. We have since grown into a healthcare delivery system serving the New Orleans market and the communities in the Gulf South. We kept the legacy of children and pediatrics in our name, which is the LCMC Health piece. We are now in nine hospital locations and Children’s Hospital of New Orleans and several other community hospitals, and we are the area’s only level one trauma center with Tulane Medical Center of New Orleans. We also recently acquired Tulane University Medical Center and its Associated Hospitals. So, we are an academic teaching organization. We train the next generation of health care professionals in partnership with LSU and Tulane Medical Schools, amongst others. For allied health students, where about 3 billion in revenue, 3000 physicians, 14,000 employees, a couple thousand inpatient beds. And we’ve kept the legacy of our founding member, which is Children’s Hospital of New Orleans, in place. But we’ve expanded our services beyond pediatrics. I am the first chief information officer for this organization. It’s formed very rapidly over the years through these mergers. And I have been in my role now for eight years.

Q. In this podcast, we talk a lot about digital health and digital transformation, and I want to focus on that as it relates to LCMC. Can you give us a little bit of an overview of your digital health program? What does Digital health mean for you and talk to us a little bit about the digital health program at LCMC.

Tanya: Sure. We are an organization that did grow through mergers and acquisitions, and so our original goal in our digital health program was to come up with a standardized methodology for systems, for strategies where we could get synergies and really integrate across our continuum of care because we are very locally based here in the New Orleans market. So, all our hospitals geographically wise are very close. And so, it is common for patients to visit any one of our facilities. We really needed to have an integrated digital footprint or electronic health record, which is where we started to make that more of a better patient experience, as well as the opportunity to make that more efficient for our organization and make it a happier or more efficient place to be for our caregivers and our workforce. We were running and somewhere around dozens, if not hundreds of various applications and systems. So, I would like to say you name the electronic health record and platform, and we had it. So that’s what I spent most of the first initial years forming was an electronic health record strategy to again, really integrate care across our continuum and remove some of those redundancies, creative efficiencies, and make that again, a better experience for our workforce as well as our patients. So, step one was to set down that path of creating a centralized shared services model and that common vision. And we did end up selecting Epic as our electronic health record. So, our initial phase of that was in 2017 and we did do Big Bang. So, everything from ancillaries to inpatient to ambulatory to revenue cycle, all of it was big bang and we rolled out. At that time, we were five hospitals and that was all conducted over the course of about a year. So, between the end of 2017 through mid-2018, we were up and running on all of those facilities. And then we acquired another hospital in the middle of the pandemic in 2020. So, we spent the last couple of bringing them into the fold onto the platforms and we are now embarking on that same process for our latest acquisitions with Tulane, which is another three hospitals. And we plan to have them up and running within about a year. So, all of that said, that’s been keeping us very busy and just putting the foundation in place. And now we’re really looking forward to moving past, you know, having the foundation and really leveraging additional digital capabilities for advancing what we can. So right now, we’re really focused on our journey towards systemness. So really developing those standards across service lines, across our continuum of care, because again, our patients in the geography that we serve is very close in proximity. So, we want that to be a seamless and common experience and focus on systemness. We’re also really focused on patient access, and we’re very aware that patients do have a choice and we want to make sure that we make it as easy as possible for patients to access our system. So, we’ve done a lot around that. And then lastly, also not just focus on the patient, but also continue to focus on our clinician experience. So, almost just as much rigor and focus on the clinician experience and happiness and creating user friendly tools that makes it easy to do their job and yet meet all the regulatory requirements and compliance things that are always coming at us for documentation.

Q. Can you give us a couple of examples of what you’ve done to improve the patient experience, especially from an access standpoint.

Tanya: Sure. One of our most recent experiences, which I will tie into even that systemness category that I just mentioned, we just recently did a full redesign of what we call our online scheduling tools and platforms. So, we do have a patient portal there. We were allowing scheduling of it when we went live with Epic a few years ago. But on this journey towards integrating care and making it a common seamless experience across service lines. We revamped, revised all of that and ensured that it was easy to create, to schedule a patient through our platform for, let’s just say, primary care. So, if for some reason my normal physician that I normally see wasn’t available, but I really needed to get in for an appointment, we now make it very easy to search our entire database of availability to get in with the next provider, even if that might not be at the same clinic that that I normally would have seen. So, that has been a huge improvement just in terms of schedule utilization and visit volume increases. So, it’s been a win-win not only for the patients to have easier access, but also, it’s a growth opportunity for the healthcare system. We’re going to start with that and continuing to look at ways for how we improve access. Referrals is another area that we’re going to start looking at again, just making that an easier process to get patients to where they need to be within our system.

Q. What about the clinicians? You mentioned that you’re also trying to provide features and functionalities to help make their jobs and their lives better, right? Can you talk about an example of what you’ve provided for them?

Tanya: Sure. We just recently, it’s still in progress, none of these things are ever done right. As it’s a continuous evolution, continuous improvement. So, one of the projects that we also launched this past year was called Project Joy, and it was a very targeted effort to focus on nursing specifically because I’m sure we are aware of the nursing shortages that many of us are facing. It’s a real challenge to not only retain the nursing staff we have, but also attract and recruit new nurses. How do we make sure that we have an environment that they like? Project Joy, in partnership with our Chief Nursing officers, was an effort to evaluate utilization of our electronic health record. So, now that we have the data in a digital format, it makes it much easier to do some targeted analytics and analysis on where our nurse is spending their time and then really dig into. At a glance we found that some of our nurses were spending an inordinate amount of time in flow sheets and responding to what we called non required best practice alerts. It was almost just kind of an FYI sorts of messages, but not actionable. We spent a lot of time in partnership with our chief nursing officers to identify how can we make these glow sheets a little bit more user friendly and how do we reduce the amount of clicks or interruptions that the nurses face with these alerts that may not really be effective. On our first phase of rolling out the changes to that project, we were able to calculate savings of over 1000 hours per month to give back to our nurses to do other things such as care for our patients at the bedside.

Q. That’s another great example of how you’re really making it work for both the patients and the caregivers. What are your patients telling you at a high level? What are the one or two things you’re hearing from them that are driving your priorities and your investments?

Tanya: We started to get a lot of very positive feedback when we did these revisions around online scheduling and ease of access. And the other thing that was probably another good example, although it’s a little outdated now, but our ability to respond to the pandemic. Obviously, that was a rapid change and we stood up telemedicine overnight. We also did a great deal on what we called mobile testing. So, if patients weren’t in a place that they had easy access, we had busses that were out in our community offering testing and then also did the same thing for vaccinations. When those became available. We really stood up the technology pretty much almost overnight and to be able to have a massive vaccination location that made it easy for patients to get in and out and even looked at some rideshare type of programs for ensuring that transportation wasn’t necessarily an obstacle or barrier in terms of where to get access. So those are just a few of the examples of the great feedback in our community that patients are excited about.

Q. Let’s talk a little bit about the tech. You have got a lot of technology choices. Your major EHR system, which is Epic, is doing a lot in terms of building out their product on their platform with their digital capabilities. You also have a thriving ecosystem of independent software solution providers. This could be everyone from, very well-established firms, but also startups from the digital health ecosystem. As the CIO, how do you go about making your choices and talk to us a little bit about your thought process.

Tanya: That is such a great question, and I don’t think any of us really have the perfect answer. I think we’ve made a lot of strides over the last few years. I think, again, the pandemic really pushed us into this agile, innovative space of not having the ability to wait for perfection and needing to take some chances or risks, hopefully calculated risks. I don’t have a perfect answer, but what we try to do is really align with our overall strategic plan. We do we do have an Epic first mentality, meaning let’s not reinvent the wheel if Epic already can or is doing it, we’ll probably look at that first just because it is already part of the tool that we’ve purchased and invested in. And there is something to be said about complete integration from the start. So, we start there, align with the strategic plan, and then identify where those gaps are. While I think that does an awful lot, they don’t do everything so really targeting and again what are our strategies, where are the gaps and identify where those possible solutions can fit. And even what we call interoperability and integration has really come a long way too. We’re not stuck with just HL7. There are so many more capabilities now in how we can integrate with our core platform. So that’s not so much a barrier as it used to be in years past, but it is something important to ensure that integration is hopefully seamless as can be for both the user experience as well as just continuity of care. If we are talking about patient information.

Q. How has the macroeconomic environment impacted your investment decisions this year? You’ve got a labor shortage; you’ve got an interest rate. There’s a lot of there’s a lot of forces in play in the market.

Tanya: Yeah. Another great question! In the healthcare industry, we are facing issues with reimbursement rules changing and the inflation also continues to rise. So, we really do have to make sure that we’re managing our costs and being good stewards, which is difficult to do when at the same time we just talked about innovation and new tools and investment. It really is a delicate balance. So, while we’re working on enabling new digital technologies that will hopefully drive revenue or improvements, and that’s a key to making sure that we continue to measure that. But also, where can we eliminate costs or really push on opportunities? So, a big opportunity for us because of all the mergers and acquisitions we did was application rationalization. So, as we brought these nine hospitals together, they had a little flavor of just about every application you can think of. That was a huge part of opportunity, is let’s standardize on the application footprint, let’s archive that data as necessary and let’s stop paying maintenance on those systems. So, we’ve done a lot of that over the years. So, some good stories to tell there and making that a priority, but also looking at new cost models. So of course, cloud computing is a whole new method of managing infrastructure compared to the sort of traditional way of buying servers and trying to predict what you were going to need, five years in advance. Now it’s a little bit more consumption based. That’s just a new cost model to evaluate. We already talked a little bit about innovation, but because of the shortage of whether it’s nursing or revenue cycle, where are the opportunities to use some artificial intelligence or maybe what we can call the digital employee experience, where we can get creative on how we can automate certain functions within our organization there where we are having shortages of labor. That’s also not an easy answer, but let’s continue to explore that. And then I already mentioned the project your way around. How do we just keep our clinicians happy and save them some time along the way?

Q. You mentioned artificial intelligence and the use of data analytics. How far are you along in that journey into. Terms of using your data and what have been some of the successes that you’ve had in applying advanced analytics to help to drive your outcomes.

Tanya: I would say that every one of our projects has some sort of metrics or analytics attached to it, and we make that a priority or a requirement before we launch any initiative. How are we going to measure this, what are our goals? Let’s make sure we’ve got a baseline and we’re prepared to measure both during the implementation and then post implementation. It’s something I’m very passionate about. I do have the business intelligence team. It’s good that we can really partner up with our EHR analysts and then our business intelligence data miners to marry that conversation. If I use EPIC for an example upon implementation, for every single module or service line, we did establish goals and we’re prepared to measure those goals during the implementation. I already mentioned the online scheduling. We just completely revised that, and we made sure we were ready to measure. We set our baseline and one month into the implementation we were able to show the metrics like – this is what it looks like last month and this is what it looks like last year and look at the improvement that we saw in just one month. I mentioned – Project Joy, we were able to measure how much time nurses were able to save just by fewer clicks and able to put more documentation at the bedside capabilities through the flow sheet modification. So, we were able to track that to how many minutes we were saving. So those are just a few examples.

Q. There’s a lot of innovation that is taking place in the market right now in terms of digital health solutions. If one of their founder CEO is listening to this podcast and wants to reach out to you, what’s your advice to them before they send you, their pitch?

Tanya: I think we covered a lot of it during this conversation. But if I could summarize maybe the key things to take away. One is really partnering so the CEO and the CIO or operations and IT collaboration to really understand the strategic initiatives or priorities of the organization and prepare to partner on that conversation around measuring accountability and on all parties, whether that’s a vendor solution, internal IT, nursing. Make sure everyone’s on the same page with what we’re measuring and why and the accountability around that. I like to say that even in data conversation, it’s one thing to produce the data. We now have lots of data, but accountability and responding to the data is I think kind of the next step of really making it meaningful. Then the other thing I think is just having conversations like this and staying connected to what the industry is doing, what others are doing, learning from others, just staying connected in the healthcare community. I truly do believe while we can learn from other industries, healthcare is a unique industry when it comes to technology, and it is really a small world at the end of the day for the healthcare IT community at least. So, leverage those conversations and that network to continuously learn from each other.

Q. What does your org model and governance model look like when it comes to digital health investments? How are you organized? How do you make the decisions? Is there a committee?

Tanya: Sure. We have a tiered approach. I call it sort of three layers of the triangle or the pyramid. At the base of the pyramid as your foundational pieces of the structure. So that’s where our subject matter experts get together routinely to talk about what the priorities are, whether they’re changes or optimization or new ideas that start there. And then above that, we call our operational layer. This is where our chief operating officer, our chief nursing officer, our chief medical information officer, sit. Their goal is to oversee trying to ensure that one group doesn’t necessarily make a decision that might negatively impact a different function down the road. They’re looking at that continuum of care for the decisions that we’re making. And then at the top level is the executive team. So, we do have what we call it together, which is our IT steering committee that is comprised of a handful of executives, including myself. Our goal is to really set the strategic priorities for the organization and ensure that there’s alignment within the framework. We also ensure that we’re utilizing resources in a shared fashion across everyone’s needs, which is tricky to do because like I mentioned earlier, we have pediatrics, and we have level one trauma academics. And so, making sure that all needs are met within that shared model can be tricky. Every committee has a chair and a co-chair. The chair is somebody from operations. We like to use the motto operationally led and supported. So, the chair is somebody from nursing or radiology, etc., and the co-chair is somebody from the IT functions or a leader on my team. And they are partners in establishing the teams and the cadence and the conversations. And then every facility is represented through that subject matter experts’ layer. And so, if you have additional questions after that, but that is how we’re structured.

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Disclaimer: This Q&A has been derived from the podcast transcript and has been edited for readability and clarity.

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.

About the host

Paddy is the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor & Francis, Aug 2020), along with Edward W. Marx. Paddy is also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He is the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He is widely published and has a by-lined column in CIO Magazine and other respected industry publications.

About the Host

Rohit Mahajan is an entrepreneur and a leader in the information technology and software industry. His focus lies in the field of artificial intelligence and digital transformation. He has also written a book on Quantum Care, A Deep Dive into AI for Health Delivery and Research that has been published and has been trending #1 in several categories on Amazon.

Rohit is skilled in business and IT  strategy, M&A, Sales & Marketing and Global Delivery. He holds a bachelor’s degree in Electronics and Communications Engineering, is a  Wharton School Fellow and a graduate from the Harvard Business School. 

Rohit is the CEO of Damo, Managing Partner and CEO of BigRio, the President at Citadel Discovery, Advisor at CarTwin, Managing Partner at C2R Tech, and Founder at BetterLungs. He has previously also worked with IBM and Wipro. He completed his executive education programs in AI in Business and Healthcare from MIT Sloan, MIT CSAIL and Harvard School of Public Health. He has completed  the Global Healthcare Leaders Program from Harvard Medical School.

About the Legend

Paddy was the co-author of Healthcare Digital Transformation – How Consumerism, Technology and Pandemic are Accelerating the Future (Taylor &  Francis, Aug 2020), along with Edward W. Marx. Paddy was also the author of the best-selling book The Big Unlock – Harnessing Data and Growing Digital Health Businesses in a Value-based Care Era (Archway Publishing, 2017). He was the host of the highly subscribed The Big Unlock podcast on digital transformation in healthcare featuring C-level executives from the healthcare and technology sectors. He was widely published and had a by-lined column in CIO Magazine and other respected industry publications.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation

The Healthcare Digital Transformation Leader

Stay informed on the latest in digital health innovation and digital transformation.