Welcome to Season 5 of The Big Unlock. I’m Rohit Mahajan, Managing Partner and C.E.O. at BigRio and now, Damo. This season carries forward Paddy’s legacy and joining me for an exciting discussion, in this episode, is Vineela Yanamreddy, Chief Information Officer for United Medical Center also known as Not-For-Profit Hospital Corporation in Washington, D.C.

Q: Welcome to Season 5 of The Big Unlock. I’m Rohit Mahajan, Managing Partner and CEO at BigRio and now, Damo. This season carries forward Paddy’s legacy, and joining me for an exciting discussion in this episode is Shahidul Manan, Chief Data Officer for Bon Secours Mercy Health, Cincinnati, Ohio. Tell us a little about yourself and your organization.

Shahidul: I’m Shahidul Manan, the Chief Data Officer at Bon Secours Mercy Health and Vice President, Digital Health at Nordic Partners. I’ve been in this space for almost 20+ years now— in financial, for over a decade, then in high-tech software space running analytic software, and finally, in healthcare for the last seven to eight years. It’s an exciting time to be in technology, especially given emerging innovations in data and AI. 

Currently, Bon Secours Mercy Health is an enterprise of about 70 hospitals—a hospital system and six subsidiaries—and about 70,000+ employees and 20 million patient lives. We’re in seven states and through an acquisition, in Ireland, as well. As the CDO, I manage pretty much anything and everything that touches data, including data platforms, the cloud, digital innovation, and focus on analytics while serving all the systems, data productization, and AI innovation. I’m also focused on our platform and data AI product commercialization to some extent. Commercialization entails sharing the goodies with other systems and in the healthcare space, so that our innovations can actually touch other systems and their patients while garnering revenue for us. 

Overall, healthcare, right now, is a great place to be in because of the innovation and all the direct impact that we can have on patients and our healthcare services. Historically, healthcare was or has lagged a little in terms of digital transformation. Our first true digital transformation began with the ACA—Affordable Care Act—where the EHR became almost like a mandate. The digital platform—the EHRs—started capturing all digital footprints for our patients and providers and everything we did to create the data that we needed. In the next phase, we will use that data to drive everything we can from building insights, data-driven decisions and operations, and data analytics-driven innovations for patient outcomes, quality of services, and various patient engagements. 

Q: Prior to Bon Secours Mercy Health you were at Mass General in Boston. Tell us what got you excited about getting into the healthcare industry? This was not where you started originally but this is where you are spending most of your time now.  

Shahidul: Absolutely. I was with Mass General before and in a similar role as now. I drove many innovations with data AI there, as well. Today, healthcare is becoming more and more interesting for two reasons.  

One, it has been a little bit of a greenfield compared to other industries as the focus is more on digitization. There are tons of greenfield innovation opportunities that are emerging, today.  

Two, healthcare is likely to show highest growth in data sets. The AI revolution we’re in is eventually going to come down to who has the most differentiating data and who has the highest quality of data and healthcare, I think, has an edge in this matter— having the highest growth and volume of data. That also places on us the burden of harvesting that data and enabling it. There are plenty of challenges to this, starting with the fact that 80 percent of healthcare data is actually unstructured data in the form of physicians’ nodes or X-ray images. Therefore, building the truth, getting insights, or even building predictive power from these, is more challenging than ever. In terms of challenges and opportunities, this is a great place to be in.  

Lastly, this is very much a mission-driven effort and a mission-driven space, which also motivates me for I know that anything that we do or our team does in terms of innovation actually touches human lives and makes a difference in patient outcomes and the cost of healthcare—that we, as a nation, and actually globally, are struggling with. That’s why I get excited about health care and find it extremely rewarding.  

Q: You spoke about the explosion of data, especially in healthcare and I couldn’t agree more. We are seeing this increasingly with remote patient monitoring and all the devices that help the patient’s journey. How do you think about two things—building a robust data engineering and data infrastructure and leveraging these and AI to generate use cases? 

Shahidul: This is a pretty complex one in terms of how we are going to tackle this big challenge. My strategy is—and that’s what I would suggest to the others—to think of large-scale enablement, first. How are you going to collate all your data in an efficient, consolidated, aggregated manner such that you can leverage high quality or better processes to technically enable your enterprise to generate all kinds of use cases?  

No longer are we in an era of daily and weekly reporting. We’re getting into real-time analytics and insights to build that infrastructure, platform, and enable data aggregation with proper quality measurements. With quality oversight, proper data dictionary and governance, and AI comes a larger responsibility of using data governance for AI governance. For all these to come into play, there has to be some enablement. That’s what I call the foundation. Anyone, thinking about this space then, needs to start with that.  

The foundation needs to be bigger than what your EHRs offer. It needs to be a more independent and innovative workspace with these various bells and whistles of governance and innovation tools so that it can move from real-time analytics to AI-driven applications, productization, and building workflows so physicians, nurses, and hospital administrators can use it. The more you operationalize it, the better benefit you get. Even on the Gen AI front, it’s no longer a science project of some kind where you try it on an experimentation level for that’s just the beginning. You want to think about how to operationalize it and actually put it in production—in the hands of the field players—so you can actually see tangible value coming out of it. Therefore, my suggestion is start with that foundation—think big, but start small.  

In the next phase, look at harvesting various types of value use cases. You want to pick the use cases that actually provide that tangible value and start with all these field stakeholders so you get a pragmatic use case you can build, validate, and then, actually put to use because the value comes out only when you use it. Keep that in mind, start the innovation there, and put these in the hands of the field level players—physicians to hospital administrators—and that’s exactly what I’d call innovation space of data and AI.  

The final one is never ending continuous improvement geared toward value extraction. This will enhance operational maturity levels of your data and AI. When you are at a stage where you have built your foundation and proven your value through various high value use cases, you can aim at operationally efficient levels and make it a part of your business or enterprise strategy. Your strategy and your AI initiatives cannot be separate—all of this needs to be at a space where systems, healthcare providers, payers, or any stakeholder in healthcare, when they build their next level of strategy for service, or product, or improve what they do, need to think of the additional value services or products they can provide with AI.  

Therefore, it needs to be embedded with their strategy and they need to feel confident that they have the capability and the value use case or value proposition to connect them to and build their next level of strategy together. That’s how I would approach this.  

Q: You’ve mentioned strategy and innovation. You must be participating in many C-suite meetings at your organization and across the healthcare segment. What are the business problems that you hear of from stakeholders and senior management teams? How are chatGPT and Gen AI influencing these?  

Shahidul: Everyone’s excited about Gen AI and its potential but in many cases, they don’t know where to start. In many cases, they’re worried that they will be left behind. So, it’s an interesting time actually for the C-suite to be challenged with this opportunity presented by AI. What I hear most from my colleagues and the leadership that I work with is that it is several fold. 

One, everyone’s worried about the enablement. Many are wondering if they are ready for undertaking this type of innovation or even taking advantage of Gen AI, machine learning, or even advanced analytics type of activities. Honestly, most of them feel they’re not ready. I don’t want to put an exact number to this, but it would easily be between 70 to 85 percent that think they’re not fully there with their data technology and AI capabilities to even explore to the extent that the right value can be extracted for their enterprise.  

Two, the interesting part is that 90 percent or more—and this is also from various studies—feel compelled to do something with this. They are committed to some extent with funding or with their strategy to do more with data AI from platform to innovation. So that’s the good news.  

Three, I have also been observing aspects to do with governance and rightly so, because we’re in a highly regulated environment. Being a part of the healthcare domain means we are the custodians of patient data so we have to be extremely careful not just from the regulatory perspective, but also from the ethical and clinical usage standpoints of what we do. All of this needs to be orchestrated with the strategy and then, only must tactical approaches be undertaken as many healthcare organizations are doing now.  

Lastly, on the governance front, I must add that we will see a huge change in the next six to 18 months in the regulatory landscape. The AI Act is already in place and when it gets executed, it will start to demonstrate new opportunities and new challenges for businesses and organizations such as ours in healthcare. The White House has set up various task forces to come up with new regulations for managing AI better and these will start hitting the ground in this time horizon. As technologists, practitioners, and leaders in the innovation space, we must comply with these regulations and leverage the opportunity while ensuring we are prepared and have fortified our organizations to manage it appropriately. 

I think that the next six to 18 months will be more like a warmup. We will need to see how we can better prepare ourselves and enhance our capabilities to manage the regulatory environment better for what we want to do and what we see as the opportunities with Gen AI in the next couple of years.  

That said, we already are tinkering with a lot of Gen AI type innovative use cases. Many organizations are actually trying these out, which is a good thing. It means, sometimes you have to get your feet wet just to understand where things are. It’s at that stage. However, with these maturing, there’ll be—even though it may sound overestimated in some cases—some slowness in the healthcare innovation space. But in the next three to five years, there may be a tsunami of products and services in this space.  

Q: With regard to regulatory oversight, how do you innovate in your space? How do you encourage your team and yourself?  

Shahidul: Besides the technology hat, we have to also wear the leadership hat. In addition, our evangelism hat has to help build that influence with the senior leadership and drive that influence to motivate our organizations.  

Starting with the evangelism, I have in place major programs to educate and train across the enterprise so that everyone gets on board and understands what this is and how it can be utilized. We focus on democratizing the data and AI capabilities so everyone can try them out, get onto the field, and benefit from access. That is also an enablement that we constantly work on—how to democratize data and give it to the data scientists, any department, or any business executive so they can see the data and make data-driven decisions.  

Another big part is about building the strategy, showing the value incrementally, and convincing the leadership about the value proposition of this whole digitization with AI. All these need to be orchestrated and so I also work closely with the team to help them comprehend the vision, understand the value proposition, and get the inputs and feedback so they feel excited about what we work with. It has to come from all sides—top down, bottom up, and sideways. We have to talk to the stakeholders, understand their pain points and how we can help them better. So, an upscaling of the organization is another focus area because the technology is exciting and constantly changing. It’s important to ensure that the team is excited and focused on constant learning—a state we are in since everything is coming together.  

Lastly, I have actually established a pretty robust governance model, even though I think there’s more to work on and do in terms of maturity. I work very closely and am lock in step with my Chief Privacy Officer, the Chief Security Officer, the legal counsel, and the IT teams so everyone feels they have a voice in this. We need everyone’s voice because it’s complicated and we are in a highly regulated environment. It is important to balance the compliance with innovation and the best way to do that is build that partnership, education, and understanding across the board. 

Q: We have launched a new offering recently in the market— it’s an Gen AI workshop that we have actually conducted at one of our client locations in Houston and given the positive response, we conducted another one at the Harvard Club of Boston. That’s our way of bringing some upskilling and education opportunities to our clients as well. In that context, can you elaborate on the data platform that you have built and are now commercializing? What does that product offering do? How can it benefit other healthcare systems and perhaps digital health startups?  

Shahidul: Thank you for bringing that up and for evangelizing on Gen AI. I’d love to hear more about the workshop because that’s exactly what we need. We need those words out and get everyone to understand what this means, the challenges it entails, the opportunities involved, and how one can get started, better. I certainly look forward to that.  

In terms of our platform, we have taken it to the next level by making it multi-channel capable and building into it greater de-identification and anonymization capabilities. Those are our custodian responsibilities—to make sure we are, as guardians, doing everything right to protect the data. 

That said, we are also looking at opportunities and partnering others—pharma companies to data vendors—to look for opportunities where we can help them innovate and share this anonymized or de-identified data for greater good while garnering revenue for the system. We almost have such a partnership with six vendors and partners. Not only is that garnering revenue for us but we want to build it as a more robust platform. We have more interested parties approaching us from across startups and pharma companies and system organizations because, I think, the more we can share data in this way, the higher our chance to become a larger ecosystem that can help solve the larger healthcare problems.  

Sharing innovations will also help those who do not have the budget or the capability to do everything in-house. For us as a system, we want to diversify our revenue and become a revenue center instead of a cost center.  

Lastly, we are also looking at several AI products that we have built and proven in-house from general health to COPD, patient risk stratification, hospital stay prediction or various readmission prediction models, all of which can help drive value-based care. We can certainly help bring those to market. 

The goal is to start looking at opportunities to bring to the market, provide that product and service to others, and garner more revenue. That’s a very, very strategic step and definitely, we’d be interested in learning more from Damo Consulting. 

Q: Would you like to peek into the future and tell us what is coming our way in terms of some disruptions and changes?  

Shahidul: I don’t have a crystal ball, but looking at the trends and the exciting movements, it’s a good time to be in data AI, healthcare, and technology as a whole. The next few years will be more exciting because we are going to see the outcomes of all the initial innovations that we have seen with Gen AI, LLMs, machine learning, natural language processing, data platforms, and Cloud driven analytics capabilities. There’s a plethora of technology capabilities that we have set up and are innovating with, today. So, stay tuned.  

I can see that personalized medicine, for example, will become big and that will actually make patients’ lives better. It will actually impact our day-to-day lives. I can see that healthcare cost and efficiencies will become more and more robust with the predictive power of data. That will help us manage our healthcare costs, better. We do need systemic improvement to solve the entire problem, but these are going to help tremendously. We are the biggest spenders on healthcare, but our return is unarguably one of the median or lower than median. We need to solve for that and this is a great innovation space that I believe is going to have significant impact; tangible and real impact in the coming days. I’m looking forward to it and hope you also get to test it, see the benefits, and participate actively in it.

Welcome to Season 5 of The Big Unlock. I’m Rohit Mahajan, Managing Partner and C.E.O. at BigRio and now, Damo. This season carries forward Paddy’s legacy and joining me for an exciting discussion, in this episode, is Vineela Yanamreddy, Chief Information Officer for United Medical Center also known as Not-For-Profit Hospital Corporation in Washington, D.C.

Q: Tell us, Vineela, what interested you in the health care industry segment? How did you become the C.I.O. of a hospital system? 

Vineela: For this, I have to tell you a small story of how and where it all began. 

Right about when I was in the seventh or eighth grade back in India, my grandfather was diagnosed with some blood clots in his brain and we were seeking treatment for this. Around 30 years ago, when this happened in India, MRI technology or the CT technology was not as advanced as it is right now. Given that, some of the blood clots were missed. He could have lived longer, I think, and because I’m very attached to my family, losing him was the saddest thing ever for me. 

That’s when I decided that we needed to get into this field of technology where we could help not just the clinical world but a lot of people. That’s where it all began. I went on to pursue Biomedical Engineering from J.N.T.U., India and then, did my Master’s in Biomedical Informatics from Rutgers School of Health Professions, New Jersey. 

Q: How long have you been in the leadership position at this hospital system? Where is it located? What kind of population does it serve? 

Vineela: I’ve been in this leadership position for about six years. I joined as Director and then, became the C.I.O. 

This hospital is located east of the Anacostia River in D.C. We currently serve 200,000 residents in our primary focus area and then, about two million in our secondary population area. While it’s not a very diverse population, we do serve a majority of Medicaid patients at our facility. 

Q: How do you feel, from the technology perspective, about supporting the business needs of the hospital? You’ve done over 200 applications and transformed the EMR system as well. Would you like to share some of the thought processes that drove those changes? Do share some successful implementations.

Vineela: Upon my arrival, the hospital underwent some changes in management. Every year, there’d been new management so, it had been a very rough road for the hospital and the population itself. There were about 50 projects that were on hold or in progress that, because of the changes in the leadership, every team would want to pursue a certain way. 

That being said, there were many incomplete projects as well—some of them could not be integrated with our EMR since the hospital was so old or because the EMR and the applications were in fact legacy systems so, new technology wasn’t compatible for integrations. 

What we had to do was, determine the overall goal for the management and prioritize all the projects that would immediately bring significant success to the management team. I learned somewhere that such arrangements—the silo I.T.s where every department would purchase their own application without consulting the centralized I.T.—existed here, and the teams would want to just implement it or use the many web-based applications. In fact, the departments could procure these themselves and start putting the information everywhere. 

What we did therefore, was, we worked closely with all the leaders, the stakeholders, and the end users themselves, because at that time, the leaders also didn’t have the complete picture of what was happening in their department. We had to collectively form a task force and reconcile all applications. So, we reached out to every single end user to know their workflow and prioritize these projects that were on hold or in progress. We tightened all role-based access and worked on all data sharing mechanisms. 

The next step was to revise old and the ongoing policies and implement any new ones if we thought we needed them. We got our security risk analysis done because of the plethora of applications we had and got an external entity to fix our security issues. 

I mentioned some projects that were in progress earlier, right? We couldn’t attain completion there so they couldn’t be integrated further in our EMR. For those though, we put together our creative heads and built the bridge solutions because the ones in the market wouldn’t be compatible with these. 

We worked out different kinds of solutions to bring something functionable—what we thought at that time would be standardized and customized—to our hospital.

Q: That’s pretty significant—bridging the gap across a very diverse set of applications and technologies—and being able to bring all the stakeholders together. What do you think about your digital transformation efforts? Can you talk about a few of these which have impacted the patient population?

Vineela: I found these efforts very useful to measure against our peers or basically the industry standards to see if we were on the right path or not. That self-check is always important. 

To answer your question better, I would like to go back to the point where I started. There were some things like morning huddles, afternoon huddles, interdisciplinary action and a lot of other measures that were ongoing in the hospital—every hospital has these. 

In the morning when I attended my first interdisciplinary meeting here, the meeting was run by that shift supervisor and they were presenting all the numbers to the team that attended the huddle. I noticed them jotting down all those numbers in a book and then, emailing those numbers to a bigger group after some time. 

This continued on the second day and the third. I couldn’t stop myself asking the whole team—“How are you going to analyze this data? How are you going to make some informed decisions? How are you going to predict something if you are going this way?” 

I didn’t mean to criticize the way it was being done, but they were doing the best at that time with what they had and with all the changes that were happening in the hospital. That day, we just decided on how to proceed—and I had huge support from each and every one over here. I presented the idea, and it is not new because rest of the world was already on it. The way forward was to have live dashboards, robust and advanced data analytics. 

This was the biggest challenge because we were using so many island applications and a legacy EMR. Again, the readily available market tools were not going to work for us so we had to customize and build our own analytics platform. We put together all our skills, people, and we built ourselves an analytics platform. It took us about a year and a half, but we were really into it. I saw that hunger in all the physicians and the leadership because they wanted to take informed decisions. We were able to get that. 

We now have analytics throughout the hospital, and it provides real-time updates, alerts, and on-demand information, while enhancing the patient care and ensuring operational efficiency. Every department now relies on it.

Q: How did you manage to change the mindset of the people to adapt to this new technology—AI and Gen AI, for instance? How did you bring about a change in behavior and culture?

Vineela: I’m sure changing cultures and behaviors is very challenging. It’s challenging and time consuming, but I must say that the leaders and the directors, in envisioning the goals of the hospital, knew what they needed to do. The only thing they did not know was how to approach the changing landscape—and that’s where my specialty lay. 

My expertise comes in here because my specialization lies in EMR and health care applications. I had a lot of cooperation from everyone and despite the delays, once we had proper planning, could visualize the big picture, and saw some samples, we knew it would be easy.

Q: Can you share one example of how these technologies you’ve implemented are accessible and usable by a variety of users within the hospital and outside it?

Vineela: My focus has always been to identify and prioritize critical technology needs for immediate applications that can bring significant benefits to the hospital and both, clinicians and patients. I give them equal weightage. 

The more time the clinicians can spend with the patients, the better it is for us. Right now, with all these regulations, I know the clinicians are always complaining about the time they spend on documentation and going into different types of applications rather than being there with the patient. 

I tried to engage myself and get my team to evaluate how we could ease things for the clinician. I always look for that because it’s basically about balancing the need and preparing the staff. That is crucial. So, we developed a clear strategy, outlined the goals, timelines, and the expected outcomes. I put more time into that because I knew the team had the skills to take this ahead. 

We started off with a small notion in our team—we do whatever it takes to get the outcome. We were open minded enough to not limit ourselves to our roles, sit there, and do just that. We cross-trained ourselves. We became continuous learners. I enrolled myself into a number of courses that I probably couldn’t even finish, but I kept on learning. I kept encouraging my team to do the same thing. We assessed the current skill sets, identified the areas that required improvement, provided training and if we required specialized skill sets, we always had the option to hire seasoned professionals for that role. 

Our main focus has always been the end users. We prioritized user friendly designs, making it easier for the users to adopt these. We would constantly think about how we could improve their workflows. In this context, one of the most important items we implemented was a robust Change Management System. We asked—“What is the current workflow? What are the changes that we are going to bring? What will it impact and how? Is it simplifying it? Is it making it complex?” If we felt it was making things complex anywhere, we just cut back because it was not worth it. The goal was to try to simplify the end user’s workflow. 

Q: What are your thoughts on innovation—Do you involve external parties? Do you have an innovation-focused department? Or is it something everyone contributes to?

Vineela: The main idea here is to address two things. First, I am always in contact with the C-Suite leaders—the directors and the managers. I ensure that I am aware of all their application needs. 

Second, we’ve implemented the IT Steering Committee. I’ll explain what it does through an example. If the Anesthesia Department wants to go completely electronic and they bring up an application and say, “Hey! I picked that because some of the Anesthesiologists have experience in one application, and they would want to go into that”, we ask them to put it through the formal Project Request Process. Here, they have to enlist all their requirements including the technical specifications. The whole team—the entire hospital and all the leaders—will then look into that requisition and vote on whether to move further or not based on the budget. The group may decide, “Okay, we have this budget but what is our dire need?” The prioritization happens at that level. 

Once we all agree that this department needs this application, the C-Suite level work gets done. When that is complete, we go to the stakeholders and the end users because up above, they may have the overall idea, but when it comes to the day-to-day activities, I’m a 100 percent sure even I will not have that kind of a hands-on experience. 

What I would love to do and have always implemented is to go back to the end user and observe. It’s not only just about you being somewhere 1000 miles away but also that we don’t know what’s physically happening. So, we observe their workflow—the physical workflow. We calculate and take account of the amount of time the complete workflow takes. And then, we see how putting this application can enhance not only their day-to-day activities but their digital activity, as well. That is the level of thinking and the thought process we adopt.

Q: What are your thoughts on these new technologies, especially AI and Gen AI? Have you been experimenting with any predictive analytics or large language models? What would your advice be to other health care leaders on how to go about this journey of exploration?

Vineela: AI and advanced analytics are indeed shaping the future of care delivery and treatment plans. There are several ways in which these technologies are making significant impact, such as, through personalized medicine. 

Analyzing vast amount of data allows for the development of personalized treatment plans tailored to the individual patients and again, the use of predictive analytics is widespread for disease prevention. That’s what we use over here. 

We analyze historical patient data and patterns, so that the algorithms can predict the likelihood of certain diseases or health events, way ahead of time. That’s one thing that helps lowering the health care costs. 

I also always look towards efficient resource allocation. Advanced analytics can, in fact, optimize resource allocation within health care organizations by analyzing patient flow, bed occupancy, and staffing schedules. That’s definitely another way to reduce wait times and improve overall patient experience. 

Everyone knows remote patient monitoring and Telehealth especially given how it proliferated during the COVID pandemic. We are into Telemedicine as well and AI-driven analytics enables continuous remote monitoring of patients with chronic conditions which allows health care providers to detect early signs of deterioration. This enables prompt intervention and swift adjustments to treatment plans without the need for frequent visits. 

Another thing I have been looking into is fraud detection and prevention, where you can use AI algorithms to analyze claims data and identify patterns, projects, and activities in health care. Not only these, but advanced analytics may also be utilized for population health management, clinical trials, natural language processing for data extraction, enhanced imaging and to reduce costs and contribute to more patient centric and proactive approaches in health care.

Q: Lots of uses and applications for all these new technologies. In hindsight, everything is always 20-20. So, standing here and looking back, if you could change one thing or do it differently, what would you do?

Vineela: It may not be doing it differently, but one of the best examples in recent times that I can cite is from during the COVID 19 pandemic. Our patients and clinicians definitely stepped up and went full scale on Telemedicine for the care they needed. 

Not only were we trying to expand Telemedicine back then, but we had adaptation issues faced both by clinicians and patients. Switching to Telemedicine from traditional methods was difficult but you know how the saying goes, necessity is the mother of invention. The COVID pandemic definitely brought about a different kind of reality but since our Telemedicine platform was ready and embedded in our EMR with minimal infrastructure, we were able to serve our patients. It has been tremendous collaborative work by both our physicians and the patients to utilize the digital platforms. 

One thing I am working towards is, given the population I described earlier, we are trying to implement a tech bar where when the patients are in the waiting room, we can try to educate them about our patient portal, how they can access it, look up their summary on their dashboards and the like. We’ve already started doing this upon patient registration itself but we want to take one step ahead and show them, while they are waiting, that there’s help available. We try to encourage them to take steps toward the digital advancements and show them how this can help them better. 

We also have the Help Desk for our employees and our internal communication system which we have implemented within all of these projects. This system tells us our response times and it’s tremendous. If you text me in that, for instance, my response time is two seconds. That’s how far we have come in catering to the patient’s needs.

 Welcome to Season 5 of The Big Unlock. This season aims to carry forward Paddy’s legacy. I’m Rohit Mahajan, Managing Partner and C.E.O. at BigRio and now, Damo. Joining me for an exciting discussion in this episode, are my colleagues from Harvard Medical School. 

N. J. Udochi: I’m J. Udochi, a Board-certified family physician, Geriatrician, HIV specialist, and Addiction specialist. I’m also C.E.O. of the Summit Medical Group, a multi-specialty group in Columbia, Maryland.

As a physician who’s been practising for 35 years and a physician leader, I’m in the trenches, daily. While I’m knowledgeable about the pain points that hamper the practice of medicine, I’m also 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 it can be used to improve access to and quality of care for everyone, especially underserved populations here in the United States and in my home continent of Africa. In fact, my entire project during our training at Harvard was focused on this subject matter, so, I sought other physician leaders whose goals aligned with mine. And voila, here we are! I’m really excited to be part of this first recording focused on AI and its importance in healthcare. 

Craig McLeod: Thank you, for having me today! I’m ecstatic to be a part of this podcast with my classmates from Harvard Medical School. I’m Craig McLeod, Founding Member of the McLeod Law Group, a healthcare attorney, and Founding Principal along with Tarul at Ellipsis Healthcare Leadership. I reside in Lexington, Kentucky. I have  20 plus years of experience operating in the healthcare space, delivering healthcare-related services to the elderly and the physically and mentally disabled. Our healthcare enterprise currently employs around 1,100 people on a daily basis and serves approximately 1,050 patients per day around the state of Kentucky. I’m here to offer legal input on AI and Machine Learning. 

Tarul Kode Tripathi: I’m Tarul. I’m a Pharmacist. I received my Doctorate in Pharmacy in 2002, but over the last ten years or so, I’ve been very passionate about and interested in healthcare leaderships. What brought us together was the Harvard Medical School Global Healthcare Leaders Program. It was around that time that I decided to leave my job of 15 years to focus on not only leadership, but effecting meaningful change in the healthcare ecosystem. So, we’re really excited to talk about the implications of AI from clinical, legal, ethical, and design standpoints, and why this matters in this moment. 

Q: I’d like to start with N.J.’s standpoint as a practicing MD. There are a series of questions that I will go through on why this matters in current practice. My first question for you, N.J., 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? What motivated you to start your practice? 

N.J.: Thank you, Tarul! 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, much before profits and shareholders. 

Largely, this change has been driven by patients being more proactive in their approach to healthcare. 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. This is especially important in the chronic care space. 

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—the one we are participating in. We’ve just applied for that.  

These trends have been further accelerated by COVID-19. More recently, the issues of workforce shortage, mental health, physician and helpers’ workforce burnouts have sparked a lot of interest in trying to make sure that care delivery is modified such that we not only enhance it but also decrease physician burnout. With technology and innovation, especially Gen AI, ChatGPT, and LNMs, the future is in blending technology as an augmentor in healthcare provision. These are the drivers in healthcare today.  

For me, what motivated me to form my group was what I saw coming—the administrative burdens already placed already on primary care practices and the dying out of solo practices. We would want to use technology to improve healthcare, ease administrative burdens, and enable collaborations to provide and deliver the best care for patients. 

Q: Thank you, N.J.! What are the top concerns of primary care providers, physicians, and business owners in this landscape? Would they rather be acquired by hospital systems or private equity players? What does that look like for you?  

N.J.: I think that primary care physicians and business owners really are very focused on trying to survive the administrative burdens that have been posed now when you deliver healthcare. The big problems for us are not just burnout but also, as I mentioned earlier—workforce shortage. Many of us had to look to outsource to help deliver care that we needed and also, profitability. We were having a very difficult time trying to survive with these payers and what had been delivered in terms of payments. So, a lot of the providers, health groups, and even solo physicians were looking to find a way to be acquired by hospitals or private equity (PE) groups just to help them in terms of delivering the best care possible because of these administrative burdens.  

Q: From a place of values-based leadership, how do you think this impacts patients and the quality of care?  

N.J.: This is something that is really dear to me. When I talk to other physician leaders in the space and patients, what really stands out is that the quality of care that they feel is being delivered is not where it used to be. This is where it’s really important to blend technology with care delivery in a way that will be a win-win situation for both, providers and patients. This is where the value or the value proposition in this lies—finding a solution that will help decrease the administrative burdens that occur while improving the quality of and access to care for patients. 

If you have a solution that really meets those needs, then, that’s truly what providers are looking for. In the short run, it may seem more expensive. However, in the long run, this sort of a solution is what will lead to profitability. It’s all about really taking a taking a long-term look at it.  

Right now, a lot of physicians and providers in the health space are really burnt out because of all the administrative burdens that we face. This workforce burnout impacts the quality of care delivered—which is less than you would want. Trying to find or use technology that will help solve these pain points is where we need to be. 

Q: You’ve actually answered my last question on how technology could help reduce the administrative burden and reduce burnout. Would you like to add anything else? 

N.J.: I would like to add a few things.  

It’s really exciting because what we have now is Gen AI and these Large Language Models. You’ve also heard about ChatGPT. I remember being in a conference in San Francisco early on this year, and I was able to test drive the Doximity GPT and its application in solving authorization problems for physicians. It was wonderful. A lot of the physicians there were very excited because this has been one of the pain points that in care delivery. Using technology to solve these administrative burdens is one way a solution can help improve care delivery—it will leave physicians and other health workers more time to actually deliver the care that patients need. 

There are so many ways in which technology can even add value at the front-end or the back-end, in terms of scheduling appointments or even enabling patient engagement. Rohit was speaking about trying to help with care management but there are just so many ways, even in virtual care—we’re all very familiar with telemedicine, whether it’s in audio or in video format—with reimbursement from the government and other payers. This has really taken off, and it’s just about 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 are all areas that really lend themselves to improvements in technology and care delivery. So I’m really excited about this. It is solutions that can actually combine both, that will be the best for the future. Any company that can bridge these two gaps will be the companies that really make it in future. 

Q: There are many providers who are trying to talk about how AI and technology can help benefit care delivery. What are the ethical standards? What should we be watching out for? What makes for a good program? 

Tarul: Where I’ll start, especially after you’ve given us some very thoughtful comments from the clinical lens is, from the high level with what is the current state of artificial intelligence in healthcare innovation and why is it such a big topic.  

We are definitely in a significant moment when it comes to healthcare technology specifically powered by AI. By  being aware of and up-to-date with the implications—clinically, ethically, and legally—we can ensure that we’re putting patients at the centre of our care 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. That is what we mean by Generative AI. 

As you mentioned, 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 enabling 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 why we should all be sitting up and paying attention to this topic. 

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

Historically, the evolution of technology has been based on a concept or principle called Moore’s Law. In 1965, Moore’s Law was put forth by Gordon Moore, who was Co-founder of Fairchild Semiconductor and Intel. He was the C.E.O. of Intel—and everyone that has a computer, cell phone, PDA, knows or should know who Intel is.  

Mr. Moore observed that the number of chips would double each year and that would turn into double processing power. This was on a yearly basis from 1965. This law stayed pretty intact until 1975 when it doubled to reflect that the doubling of chips per integrated circuit and doubling of the resulting processing power would move to every other year. Moore also established his second law, also known as Rocks Law, which stated that, even though the size of the chips on the relevant integrated circuit was reduced and the processing power doubled, the cost of the physical plant and the necessary R& D related to the advancement would increase exponentially over the same time period.  

The reason why this is important is, it creates pricing issues which are inevitably passed on to the ultimate consumer which is either a business model or the personal consumer. Staying with Moore, his laws are based fundamentally on the historical norms of manufacturing, supply chain principles, as well as basics of economics 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 and machine learning—over the past year or so, has made these traditional considerations 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 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, their elimination allows 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 this is with regard to the Nightshade and Glaze programs that are being implemented by the University of Chicago. More about this coming up on my next statement and probably, future podcasts.  

Q: What are the best practices in AI innovation and implementation?  

Tarul: 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. For example, we have to train our providers— MDs, nurses, all caregivers—in all different modalities in meeting patients where they are. This real world data feeds the algorithms that AI will influence and ultimately use to improve care and efficiencies. 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 which needs unique ways to speak with and treat patients who have lived through gender-based or other forms of violence. There are some really interesting discussions going on all over the country on how to carry out this care. We all must 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. 

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—data scientists, engineers, clinicians, patient advocates, ethicists, economists, and policy makers—have to come together and hold honest and wider discussions around how to implement ethical and equitable AI. 

Q: What are the legal considerations and why it matters in terms of risk avoidance, management, aversion etc.?  

Craig: AI and machine learning are evolving very quickly but as everyone knows, the legal field and the resulting legal decisions and lawsuits are so slow. This is a very new topic that we as attorneys are trying to figure out how to speed our pace of evaluation and bring these to light to expedite these type of decisions. The key here in understanding AI and machine learning legal perspectives is comprehending where and how the protected and confidential information and or the data is stored, utilized, and integrated into local or and/or hosted on external systems or platforms. 

In law, usually confidential or protected information is just that until it’s not. Where you get to the “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. That brings us to confidentiality and trade secrets. I’m using this generically to mean “any confidential protected information” includes protected health information under the U.S. Code. This states that an owner of a trade secret must take reasonable steps and measures to keep such information secret. What that means is, you can’t go out 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 we’re not in the 1950s. We’re not in Cleaverville, anymore. This is 2023 where everything happens over a computer. 

Information is stored everywhere. 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? It doesn’t even have to be intentional. It could be that they unintentionally and even negligently link this information or this platform to an AI or machine learning system. Typically, when you feed information into the AI and machine learning system, it doesn’t really care where the information comes from because it’s utilizing it. But once those processes become active, there’s no going back. Once the trade secret is input into this AI and machine learning system, it’s more than likely your trade secret will be 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. If you read the initial briefs and decisions, they’re pretty much in lockstep with each other. 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.  

That leads us to patient and data privacy. So everyone knows how important the Protected Health Information (PHI) is from a patient’s healthcare and societal positions. However, one problem immediately arises 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? How does it know that some of this information that was confidential hasn’t been made un-confidential or public by release or by a court order? It doesn’t. This is why it’s so critical to safeguard information from an AI or machine learning environment.  

Another issue that I want to bring to light is the facial recognition technology that may be hijacked for improper, immoral, or unethical purposes. The 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 and 23andMe because of the notion that this information really is the last line that police agencies can utilize to find and crack cold cases etc.  Ultimately, the jury is still out on how to utilize this DNA information and how it is being utilized after being deposited on a third party site.  

I mentioned earlier about the bad actors and 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. Specifically, I’ve been reading and studying the Glaze and the Nightshade programs from the University of Chicago. The baseline notion here is, these artists that are seeking protection of their work. They’re artistic and into 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. My initial observation on this position by the artists utilizing Glaze and Nightshade was, “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 to me that the artists utilizing these two programs sort of want their cake and eat it too. 

Q: How does all of these that we’re discussing influences the design best practices.

Rohit: As N.J., Craig, and you mentioned, this is a revolution. Today, AI is becoming household name with ChatGPT and that has reached inflection point. Everyone’s using it for some purpose or the other. It took a long time. And AI has been around. In fact, 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. One can log in and use it especially those Large Language Models and the applications which are immense, very broad, and across industry segments. 

However, in the healthcare industry segment, there are obviously privacy concerns and patient lives at stake. So, when we design AI systems, we do have to keep in mind all the various aspects that we discussed here, today—the ethical, the moral, the patient-centric etc. 

I’ll illustrate it by an example of a project that we are currently doing for one of our clients. Hopefully, that will bring to life some of the design approaches. We are working with this very innovative digital health company that N.J. mentioned I’d shared with her. We are working on building a care planning tool and platform applicable to chronic diseases and patients with multiple disease conditions.  

The challenge was—how do you build a wall so that the Large Language Model is not infringing on any private data of the patients?  

There are now possibilities, especially with large tech companies which 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 and learning from your proprietary data. It is staying in that silo. It’s not getting externally released, at least outside of that wall. These are some of the design conditions when we approach projects.  

Typically, you look for a POC—a proof of concept—and then, as you build it out, you put it in front of the stakeholders so they can test it. Usually we’ve also found that even in this project, there is a clinician in the loop. So, we still need that certified, qualified human being—a nursing staff or a physician—in the loop to look at the output of this Large Language Model and like sign off on it before it goes into the next step of being shared with the patient.  

This whole landscape is changing very fast. There is literally innovation happening almost every day. It’s explosive. I don’t know which law—Moore’s law or AI law or what we should call it—but things are changing very fast. In the company, we have some very qualified data scientists who keep up-to-date with all the current literature. 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. 

Q: As we wrap up our discussion, 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? Rohit, I’ll start with you and then, N.J. can describe how advances in this space can improve the lives of providers and patients alike using a real world example. 

Rohit: 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. He was saying, “Rohit, I have so much data in my repository. I don’t know what to do with it.” I think, that is really the starting point.  

Organizations and providers have data sets and that is a gold mine, right? How do you leverage that data set is where AI and Gen AI comes into play because AI is good at making predictions. So, if you have data sets, especially those which are retrospective, longitudinal, and immensely powerful, these can be a base for building AI and Gen AI models which can truly help with patient care. In my mind, for any AI project, everyone knows 80 percent is data engineering and 20 percent is data science. That still remains true. 

Q: From a provider and a practicing MD’s standpoint, what do you think people in this space need to be doing in terms of action? 

N.J.: It’s really exciting because the healthcare industry and providers produce a lot of data like Rohit has said. These massive amounts of data from multiple sources is exactly where AI and machine learning can add value. This is especially in healthcare delivery. I’ve talked so many times about this that there’s no day I don’t wake up trying to figure out how to solve the administrative burdens and improve healthcare delivery here in the United States and around the world. So, it’s really about helping solve those pain points.  

In healthcare, AI can add value in healthcare diagnostics. It’s been very valuable for many of the providers in Radiology, Pathology, and 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—telemedicine and wearable devices—there are so many areas in which AI can really add value.  

But really, the technological solution that we want is one that really can blend the pain points that we experience in care delivery. A solution that can solve those problems and at the same time improve patient outcomes, enhance access to care, and ensure profitability for health systems, provider groups, and organizations. That is what the healthcare community is looking forward to.   

Like I said earlier, in the conference I had attended in San Francisco, there was so much excitement with Doximity GPT because we could really use it right there and and solve the prior authorization problem on a daily basis. There are so many problems that we have, whether it’s at the front-end of care delivery or at the back-end. The value add will be in trying to use AI and LLMs to solve the problems that we have in care delivery when we see patients.  

We talked before about the back-end in terms of patient coding in the Revenue Cycle Management (RCM) process, too. That is another area where technological advancements are needed. Perhaps startups can focus on this in the healthcare space because when you combine both the front- and back-ends to solve those problems, there is where you’re going to see a lot of excitement.  

In the end, the problems that we’re experiencing as providers—the burnout, physician charting, in vitro scribing—are all areas where physicians are looking forward to using AI to try and augment the care that they provide on a daily basis. 

Rohit: Thank you for the excellent panel discussion, N.J., Tarul, and Craig! It is great to get together, virtually. I would like to end by giving a shout-out for a book, Quantum Care, that I recently wrote and which has been published. It is a deep dive into AI for health research and delivery.