Dani:

Welcome back to the Handoff from Trusted Health. I'm Dr. Dani Bowie. Today I speak with Ajay Gupta, public health innovator, founder and CEO of HSR.health, a geospatial data analytics company that supports public health and emergency response communities. We discuss emerging healthcare tech and trends from quantum computing to the role of AI in healthcare and how innovation courage can propel us to achieve the change we need to see. Here's my conversation with Ajay Gupta.

Dani:

Welcome to the handoff. I'm so excited to have a conversation with Ajay Gupta, a healthcare innovator, a leader in the industry. Welcome, Ajay.

Ajay:

Dani, thank you for having me. I'm excited to be here with you and I'm looking forward to this conversation.

Dani:

Me too, me too. So I would love to start the show off by centering us back to our purpose, which is the patient. I would love it if you could share a particular patient experience or story that's had a profound impact on you.

Ajay:

Thank you, Dani. I have a story like that, but it's a little bit maybe unusual. Most of these patient stories are some medical procedure or treatment or some outstanding action by a physician or clinician saved a life and that changed somebody's perspective. And of course, things like that have happened in my experience as well. But for us and for myself and my business partner many, many years ago, my wife who runs a medical clinic, runs among other things, provides medical weight loss assistance or assists her patients with medically guided weight loss efforts. Just came to us really one day from her perspective, casual conversation and noticed that a particular group of her patients, a particular socioeconomic group, outperformed everybody else in the success of the weight loss program. And we always know that there's diversity and outcomes, but this was ridiculous. It was almost like if you were to graph it, it would be a plateau with one peak, one group was doing better than everybody else, and then that is not the way it's supposed to be.

So she just was saying, that's curious. It's odd. And sort of asked if we could look into that. So we did the old fashioned way. This was of course a long time ago before a lot of modern technology is available for medical research. And we just looked into it, chart pulls, seeing what's going on, correlating factors, and we believe we came up with what might've been happening. And we mentioned that to my wife and it was one of those situations where she wouldn't have known in advance, but once you hear it, it's obvious, right? And she was like, well, I can do something about that, right? I can definitely adjust the program to make that scenario change. And a year or so later, when we revisited it, indeed it was like that there was still a diversity of outcomes, but more normal diversity of outcomes.

And there were people across all the spectrum, race, ethnicity, age, et cetera, who were successful in the program. And that was a real eye-opening event for us. Even though it took place over time, you have to measure the impact of the idea, but eye-opening that we really can use data analytics in a real world situation with real clinicians treating actual patients and make a change, make a difference that improves lives. So that was the thing that sticks with me, and I remember that since then, every time we're working on our project and we push ourselves because if we come up with a solution, it can change people's lives.

Dani:

Absolutely. I love how your wife was curious enough to be like, Hey, I'm seeing something different. And also had the gumption to say, let's figure out what's going on so that the provision of care can be multiplied or increased in a way that's across all populations. So I really like kind of the scientific approach of we're seeing something, let's investigate it, uncover it, and the discovery then leads to the way that she can help deliver care holistically to many and translate that element of success across multiple ethnicities or groups of populations that were receiving care. And this brings us to my next question, a little bit more about what you're doing today and how you have this history of looking at data and now technology. So let's talk about H SS R Health, which is your company and it's a technology company using geospatial platforms to map disease rates against the social factors to identify solutions to population health, as you mentioned, such as the opioid epidemic or maternal mortality. Can you describe to our listeners a bit more about your company and what you're doing today and how you're transforming population health with technology and new approaches?

Ajay:

There really are two broad things that we're doing On one, we are analyzing social determinants of health. And for us, that simply means how we define the world we live in. Because if we really understand that, that there are these factors outside of healthcare, outside of what happens within doctor patient interaction within the walls of a hospital, as we like to say. But those things impact our health outcomes and those things are the world, right? The world we live in impact our outcomes. And that's true really for everything, not just for healthcare. The world you live in impacts the kinds of you'll eat, it impacts the kinds of vacations and places you'll go. The education, we are a product of the world we live in. We know that to be intuitively true. We certainly have lots of data to suggest that the social factors around us impact our health outcomes.

So we should be able to study that and learn about our health outcomes. And then we do use advanced technologies, and that's just simply means that we remain curious also to see is there something new in technological front that can be applied to studying these data sets and studying for this outcome. So we use geospatial technologies, which is essentially just mapping data to see how it overlaps. You can see correlations and relationships between various factors and also leveraging artificial intelligence for its ability to extract relationships from what might seem to be diverse and unrelated data sets that maybe are not as intuitive as possible as you would like to be. And so leveraging both of those, and we're always curious. So as technology improves to allow for quantum computing, which if you believe the prognosticators is just around the corner, then we will try to adopt that into our processes also, because our goal is not really to be leaders in technology, although I think you have to be in the world today, but our goal is to find solutions to major public health problems so we can improve outcomes. We can really lower the global burden of disease.

Dani:

I think that's a really important call out that you said the intrinsic motivator solutions. You're trying to find solutions to the challenges faced. How has your company also fared through the pandemic and transition with the pandemic and then post pandemic? Is there anything that you could share there about the transformation and the way that you were able to provide services that were absolutely needed during that time and beyond?

Ajay:

The pandemic, of course, is a public health crisis, and so I don't like to say this, but the reality as a public health crisis helps public health firms. That is a time when decision makers and health systems all the way up to the W H O and their peers do need more of our help and input. The pandemic has been, or an accelerator for us, it's sort of exposed us to more potential users that can benefit from our services, has pushed us to develop new solutions that simply weren't needed before and refine the solutions that did exist. So that has been positive, I'd say now the challenge is that we take what we've learned over the three plus years of emergency response to a respiratory disease and apply that to all public health challenges that were here before, during, and will remain after the pandemic, like seasonal respiratory diseases like influenza increases in other endemic and emerging diseases. And of course, as you mentioned, there are non-infectious diseases that are killers, essentially the opioid epidemic here in the United States and other countries. Though maybe we make the most noise about that problem, maternal and child health of course is a problem that remains a problem for the United States that we want to be able to develop solutions to help, and we think that we have

Dani:

Absolutely, well, what you mentioned too around it accelerated. I mean the pandemic was a great accelerator I think to tackle problems that have been in existence for a while, but gave us the we can't keep doing what we're doing and so we've got to innovate and change. So as disruptive and hard as it was as a healthcare provider myself delivering healthcare, I also saw this is the opportunity. Let's seize the moment and see how we can make some changes and adapt. Now let's talk a little bit more about emerging healthcare technology and trends that you are seeing and feeling particularly excited about. You mentioned something about quantum computing. I know a little about that, but I would just love to get your take on any other trends and thoughts that you have around this space considering you are such an innovator and leader right now, helping change the industry.

Ajay:

Well, you're not alone in that. Most people know very little about quantum computing because there's just not a lot of it, but I don't see quantum computing as a trend today. I see it as a promise of the future. So all you need to know about quantum computing really is that it is supposed to be much faster processing of individual transactions, right? So anytime a computer does an action adding something, adding two numbers, it's transaction quantum computing can do it a whole lot faster and not even one order of magnitude faster, almost many orders of magnitude faster. So it just makes it the ability to analyze data so much quicker that what we would think of today as a slow process will become fast tomorrow. What we think of today as something the computer can do quickly will be so instantaneous. It's almost feels like it knows the answer before we ask the question, but that is for tomorrow.

Today what I am looking at is I see that we have far, far more data than we as humans can analyze, and we're going to get more and more data because every day there are new satellites that take pictures of the earth that can provide insights on potential zoonotic diseases. There are more sensors in cities and communities that can provide insights into air quality that may impact patients, and we know that possibly the very first indicator of the health of an individual is temperature, right? If you're sick, probably the body will respond by raising its temperature. Anything that does to fight the illness will raise a temperature. Now, I've just learned of a company that developing essentially a pill that we can swallow that will track the temperature of our organs, which of course is a little bit more important that the body temperature, the skin temperature and the internal temperature can be different.

So that just means though that the temperature metric itself, which is right now, a single number can become multiple values and can be tracked internally in real time, just completely increasing the quantity of data that constitutes temperature. And as we see this growth in data everywhere, the ability for us to process it in an intelligent manner for diagnosis and treatment is going to be overshadowed. So we will have to use ai. I don't think AI is tomorrow's technology like quantum computing. It's here now. So I think we have to deploy artificial intelligence measures, and really that's just sort of the brand name that it's adopted. It's really advanced data science techniques that analyze with a number of diverse models and approaches to analyze data and extract insights that we want to learn. Some that are specific. We would like to know what is in this particular radiological image is a bone broken and some that are not, that scan this wide set of data and see whether any particular molecule might be best suited to fight a particular or any of a set of illnesses. Those kinds of solutions are things that we will need to go to AI for and we can go to AI for, and we simply need the courage to pilot these solutions and put them into practice,

Dani:

The courage to pilot input into practice. I think healthcare is intrinsically risk averse in so many ways when it comes to thinking about reform of care and the models of delivery. And I was sitting here listening to you and I've worked so many years in health systems, and oftentimes there isn't a lot of dedicated resources to looking at your data sets and making some of these, even on the business side, we're moving in that space, but it's something that oftentimes it just isn't a big huge priority of a health system where it could really lead to better business decisions. And then as you mentioned, delivery of care to our patients. So as a leader, when I was I think a director of eight hospitals, I hired a data scientist to help me with all the staffing data I was getting to create reports so I could do actionable items for transforming the way we were practicing and doing.

So not just out of I feel this, but I also want to see this. And that was one of my most favorite hires was having that type of team and leading in a new way. And so everywhere I went, I would always try and build in this data scientist approach to how we were looking at staffing. When I think a lot of listeners to the podcast, if we can think about how can we bring this into the spaces that we're practicing or partnering with the right type of companies who have this expertise to bring transformation in variety of ways.

Ajay:

That last part is really important, right? You are right that healthcare from the outside appears to be slow to change. That's not really a complete picture of the situation. Healthcare is a high risk industry. If something goes wrong, it typically means people die. And so of course they have to be cautious more than any other industry, maybe save except military. They have to be cautious, but that doesn't really mean that they are averse to change. Healthcare is not averse to change to advances in medical technology. It's averse to change from advances that come from outside the industry. If you look at any history of medicine, you'll see that almost everything we treat has changed over time. How we treat almost every disease or condition has changed over time. That means is they're not averse to change. They have a process though for how they adopt change, how they validate that what we are changing from what we are doing today should be developed, tested, and implemented.

Because what we're doing today to the best of our ability and knowledge today is saving lives. We can't change it just because we think there's another better way. No, we have to have the same level of confidence in what this change represents as we do in what we are doing today, which we know today saves lives. We have a clinical trial process well understood medical technology and the processes for changing the treatment and diagnosis of illnesses does take place. Again, in the pandemic. Early on, we were putting patients on ventilators and quickly we learned that that's not necessarily needed in all cases, and we adapted treatments. So when you say that your listeners who are within healthcare, they're nurses, they're practicing nurses. If we inside of healthcare, look outside to AI and technology companies and say, Hey, bring in what we think we need, what we want from you to help us change healthcare and the provisioning of care that can work and that will probably work much better than an AI company.

Even though we have much larger technology companies than we've ever had in the world, trillion dollar companies, we are certainly not just that we're going to be hesitant to listen to those companies in what they tell us, but we have to be because they don't have the liability of human life. We do. They don't really have any liability. If something doesn't work, they just release a new release the next week to fix it. We can't do that. If something doesn't work, the patient is dead and there's no coming back from that. So I think that we are probably just as innovative as any other industry. We just have a process to test and deploy the innovation that is not understood by other industries and is different from the processes in other industries.

Dani:

Yeah, I think that speaks to the, which is the driving factor, is the patient safety component. I do think on the business ops side, we are not as fast and curious in testing some of the biz ops sides compared to I think the medical component of drug and intervention, and that gets powered fast. But I do think on the biz ops side, we sometimes don't innovate as fast as potentially we could to run a bit more efficiently. And there's a variety of reasons for that. Change fatigue and right. Tired of change. Lots been going on. Other priorities are we under resourced? But I was thinking like, man, it'd be really great to power with a company like yours to be like, how could we really add some wheels to the vision of transformation from different elements? Just like I had one person working with me. What would it be like to have a really dedicated and sophisticated team to do some of this work? What do you believe needs to be done for true healthcare reform?

Ajay:

Yeah, unfortunately, we do appear to be in a place where almost every element of the industry is not working as well as we know it could. We are seeing it in any metric, any metric. We see average length of stays going up across many hospitals across the country. We are seeing workforce shortages because people don't want to do the job. They're not being financially and emotionally rewarded as they were in the past for doing the job, which is hard. We're also seeing, and I think we're all terribly sad to have to acknowledge that there unforced, medical errors are high all across the country, all across the system. And that's a big thing, right? Because there is no nurse, no doctor, no hospital, no system that operates all across the country, all across the industry, which means that the unforced medical errors leading to fatalities is so systemic. It's beyond any individual provider that is, that's harsh. So I guess we'd have to look to the system overall, including the regulators to see what happened.

How did this situation arise where we have unforced errors from the people who are trying to save lives. It's a deep question. All I can say is that we briefly mentioned it, we really need to have the courage to try completely different systems and approaches to providing delivery of care. That's not to say that means of diagnosis change or that standard of care changes, but from the very infrastructure, we may need something new. The current infrastructure works only if you tolerate losses due to errors. A colleague who may be building a hospital from scratch in a place in the country where there's no care, no hospitals right now. So it's truly, truly something like the blank canvas. So I'm strongly advocating that they do. So where they design the hospital, they're in the design stage, there's no hospital there. Completely design it as a complete medical AI facility, artificial intelligence at triage, artificial intelligence for diagnostic radiology, robotics, animation, the works, right? Design it that way. And of course, what can't be built with modern technology in that fashion back that off and staff it and are more and more alike our current situation, but at least there'll be no legacy workflows to have to support. It'll be all starting from scratch and then push ourselves to see how much of new age medicine can we really envision and then slowly put those pieces in place. I think that's something we are going to have to do because what we're doing now leads to a lot of unforced errors.

Dani:

I love that. I think that's a really exciting approach. It's really about more of augmenting some of the delivery or the way that we could diagnose. So you're not saying redefine how we're diagnosing or treating, which all we, as you mentioned, progresses through time. But it is more around thinking about the workflows, the technology support, the way that this can be supported more with AI and technology and think about it differently. That would be, I think a dream come true. And the key factor is how do we reduce error that's happening systemically across the country. It's not related to one provider for the sake of our patients.

Ajay:

It's not related to one provider, one system, one approach. It's everywhere. And that means that there's something fundamentally wrong with the practice, with the industry, and if there's something fundamentally wrong with the industry, we do need to try something totally different.

Dani:

Absolutely.

Ajay:

I was in a conversation with a physician and talking about this general topic of artificial intelligence in the practice of healthcare, and I wanted to convey that I would be comfortable with an AI diagnosis for something simple, but if it's a complex case that I want a human to take a look at, that doctor didn't really have the opposite opinion, but they had something that I hadn't anticipated. The doctor was saying, I don't need any help for something simple, right? I can do that all day every day, but if it's a complicated case, I would like somebody else or something else to give me ideas, which is how medicine is taught. Medicine is taught to leverage the second opinion. You see something you don't understand, call a colleague, get a second opinion together, you can figure it out. So that means there is a role for AI to play in all sides of the spectrum. Maybe we can leverage it to do some of the easy work so doctors don't have to all the time, and maybe we can leverage it to do some of the more complex work. So doctors get a boost, get an aid, get an idea. There's a role for it to play everywhere in the spectrum, and none of it necessarily has to remove the human contact that patients value and physicians value to patients that it can maybe just streamline the process

Dani:

A hundred percent just like I do with my colleagues. Let me bounce my ideas off of you and see what we can figure out. And so I think that that would be extremely beneficial and helpful. Let's talk. We've been talking a lot about reform AI technology. You're also the chairman of the board of directors for Holy Cross Health, which is a multi-hospital social safety net health system in Montgomery, Maryland. This gives you a really unique outlook and lens regarding the healthcare workforce crisis. What are your thoughts on the crisis potential solutions? And again, how do we approach the future around the workforce crisis?

Ajay:

Yeah, that's another deep question. That's something that we spent a lot of time thinking about. I do say that our industry is a little different than others because of the liability, the risk of human life. But that doesn't mean that we can't learn from other industries. I think there are a number of industries that have dealt with the workforce crises and have continued to thrive, have continued to modernize, I think, for instance, the manufacturing industry by leveraging automation, by leveraging advances in an assembly line process as well as integration in their supply chains across organizations. But the whole supply chain integration has allowed them to increase productivity of their workforce. So they still have challenges with the workforce. We do hear strikes in the manufacturing sector from time to time where the threat of strikes, but they seem to have adapted in a way that maybe healthcare can learn from.

I also see that in the technology space using monitoring and remote, well, we would say remote monitoring and an automation. The data center, which is you might think of as equivalent to a large hospital, large facility, a lot of things going on. It used to be a job center too. There used to be a lot of people that required to run a data center, and today you can run very large data centers with very small staffing. So I think that's something that we can learn from how exactly that happens. And I don't know that we can really get away from human staffing in a hospital because the human touch is so and much an important part really of life in general, but also in healing. But maybe we can have more of the staff focus on that important aspect versus rudimentary tasks that have to be done, that are unrewarding and unsatisfying and simply hard.

So there's just, I think, basic management skills and sets that we can learn from those industries and seeing their transitions. I also do think though, that we do need to find ways to deploy more technology to do the things that what you would in a sports analogy would say are the blocking and tackling, but not the critical piece of the industry. That's something that we're trying to explore certainly at Holy Cross, and there's no telling where that ends, right? So if you follow the news, there's a lot of news from technology companies, albeit that claim advances from AI in diagnosing of imagery and that it's so sophisticated now that sometimes they can detect cancers that they weren't even told to look for. I'm not here to say that that's the case, but I do think we need to validate that we need to do some clinical trials so that individual hospitals and individual radiologists or radiology departments can understand if that can work for them and if it can work for them, that is certainly going to speed diagnosis and potentially speed treatment for those patients.

Dani:

Music to my ears, one thing that really stuck out to me when you were speaking, which has been a lot of my lifelong mission and some doctoral thesis, was around predictive scheduling. And I was sitting there thinking about, man, if a new hospital is built from scratch, how I would love to see the rudimentary tasks of staffing and scheduling, which we should and can automate. We can predict, and this can be done without the need of major human intervention, such as a manager to really take off and use the right technology, the best technology from the ground up would just be a dream come true there. Because as you mentioned, and what I know to be true of the nursing workforce is we enter into the profession out of the desire for healing to make human connection something deeper than just the tasks of I delivered medication, or as a nurse manager, I made your schedule. We want to do more. And so being able to remove the redundant work and the work that can be done differently so that we can really focus on our core competencies, which is the patient, and also building up of the workforce as leaders would be completely transformational. And I hope that this idea of a new hospital from scratch does happen, and it incorporates the transformation of how to actually operationalize the workforce with the right technology there.

Ajay:

That's a failing of the industry. And maybe we haven't done that enough build from scratch just to see what we would learn, what we would do. I mean, we see that in the automotive industry. Every now and then a car company is built, funded by an existing company, but with no legacy requirements. Just start from scratch to see what you would do differently. Sometimes those companies don't succeed, but that's not really the long-term point. Can we learn something? If we take everything we know today, just start all over, what would we have done differently? What would we do differently? And that kind of opportunity for innovation is really important to make sure you do something right. You're doing everything. So maybe we need to do a little more of that in our industry as well.

Dani:

Yeah, the opportunity for innovation in any way. Good call. Ajay, this has been so impactful. Really a great conversation. Where can our listeners find you if they want to reach out and just learn a bit more, what would be the best way for them to get a hold of you?

Ajay:

So I'm on LinkedIn, right? I'm sure you can complete post Ajay Gupta on LinkedIn. My firm has a LinkedIn presence too. HSR Health, you can find us there. That's probably the best way. We've got a website, hsr.health as well, but LinkedIn still seems to be a good place to actually engage with people.

Dani:

I would agree. I appreciate LinkedIn as well. What would you like to hand off to our listeners today? We've had a lot of conversation on innovation, but this is kind of, as you think about nursing, oftentimes you do a handoff from one nurse to the next as you're handing off care for your patients. So I would just love it if you could share with our listeners any pearls of wisdom or things that we haven't touched on that you would or what you want to reiterate as a handoff for our listeners today.

Ajay:

The one thing that I would hand off to your listeners is optimism, right? I know we have a lot of problems in healthcare. Nobody is unscathed in healthcare without going through challenges and seeing a lot of sometimes horrible things. But I do think that we really on the verge of truly revolutionizing health and all industries through advanced technologies, if we think about it, even things may feel and look bad today. The state of healthcare we have available to us today is infinitely better than what was around a thousand years ago or a hundred years ago. And this is at where we are today. Also, we have far more technological capabilities than the leading scientists and physicians and clinicians had a thousand a hundred years ago that led to those changes. So I am very optimistic on the future of global public health. I think we are going to learn many things every day.

I do think some of these AI solutions will come into fruition. I do think some of the techniques, like you're talking about with improving management of staffing will help and will make the job a pleasant experience again, which it was in the past. And it will get back to, I do think that we will start to do some of these things of building hospitals from scratch because the technologies are so new that they'll make the most sense to just start a new facility from scratch with these technologies in place. And I am very optimistic about the future of healthcare. I just see ourselves today in the transition. We just keep our eyes open, stay optimistic, stay curious, and as we make this transition, we'll find ourselves in a place where healthcare is, again, much improved than it was in the past.

Dani:

Great call out optimism. And also we were focusing a lot on some of the issues. But that is not to say, as you just mentioned, the transformation that has happened and is going to happen and the positivity of change and the ability to see it transform. So thank you so much for your time and conversation. I really appreciate the insights that you share and the perspective that you've given us, which is a unique perspective, and I can't wait to hear more of the innovation and work that you're doing in the next coming years and how you're transforming global public health and healthcare in general. So thank you so much, Ajay, and just really appreciate your time.

Ajay:

Thanks, Dani. It was a lot of fun to be here with you today.

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