Paul Rothman 0:00
In fact, one of the roles of a physician will need to do is the interface between the data and the patient, either interpreting the data for the patient or working with the patient to use that data to better their case. I really think in the future and we’re thinking about how we train people to ensure that they have a real knowledge of how to analyze data.
Gary Bisbee 0:20
That was Dr. Paul Rothman, Dean and CEO Johns Hopkins Medicine. I’m Gary Bisbee, and this is Fireside Chat. Dr. Rothman was commenting on the evolving role of the physician to interpret the growing volume of clinical and complex data available to the patient. Stay tuned for his discussion of the three revolutions in medicine currently underway today. Dr. Rothman discusses the future of medicine and why Johns Hopkins has committed together with the data and applied tools such as AI and machine learning to build 50 precision medicine Centers of Excellence over the next five years. Dr. Rothman is a scientist of note who is a significant leader. You’ll enjoy this episode. Let’s pick up the conversation with Dr. Paul Rothman.
So here we are with Dr. Paul Rothman, Dean, and CEO of Johns Hopkins Medicine. There’s a number of hats you wear Paul, but will ‘Dean and CEO of Johns Hopkins Medicine cover it?
Paul Rothman 1:20
Yeah, I think that’s great.
Gary Bisbee 1:21
What a background you have. Originally from Queens, New York, to Johns Hopkins through MIT, Yale, Columbia, and Iowa.
Paul Rothman 1:29
Gary Bisbee 1:29
What a career.
Paul Rothman 1:30
It’s been fun.
Gary Bisbee 1:31
Did you envision back in growing up in Queens that you’d end up at Johns Hopkins?
Paul Rothman 1:37
I probably didn’t know who Johns Hopkins was. No, I grew up in New York in Queens. I went to public school. I was a soccer player in high school. And ended up being good at math and science. So I actually got turned on, really from my high school AP biology course. A guy named Marcus Holland was my teacher. And I got very excited. And the story was he used to read scientific America. I don’t know if anyone still reads it. But we used to read it for AP bio. And I say that the Nobel Prize was won by a guy named David Baltimore. And MIT, discovered reverse transcriptase, and set off the molecular biology revolution. And I decided that’s where I want to go to MIT.
Gary Bisbee 2:28
Did you want to be a physician at that point?
Paul Rothman 2:30
I wanted to be in science. I didn’t know. But I liked science. So I went there really to learn microbiology. And so I went to MIT, I was a biology major. And it was a great route. Still, we had about five Nobel laureates in the biology department. It really was a special place. It was the beginning of molecular biology. And I got to do that and learn and work with some of the world’s greatest biologists. So it was great and I did some basic science and molecular biology and really enjoyed it. But also understood that I was studying DNA repair in bacteria, but we realized that I probably needed something that was more directly related to human health. So that’s why I decided to go to medical school.
Gary Bisbee 3:14
Was there a point you remember it just clicked and you thought, ‘I’m gonna go get my medical degree?’
Paul Rothman 3:21
I was actually torn because I really liked science. And, and it just evolved. So I did that, actually, I spent most of my time in the college rowing crew. I mostly was on the Charles River most of the time, to be honest. And I went to Yale because I could get to be on a great crew team and I can keep rowing, right? I actually looked for medical schools so I could still row and go to medical school. And at Yale, which was a great medical school. I really loved that. They had a Yale system where you really read primary literature and didn’t try to memorize everything. I had a great time in med school, a very academic place. So I enjoyed that. And that’s when I learned to love immunology from a guy named Charles Janeway, who has passed but it was one of the great immunologists of our time.
Gary Bisbee 4:19
Right, which is a great underlie, to the precision medicine that we’ll be exploring in depth here in a minute. So the decision to lead and become an administrator. I know at Columbia some years ago you either fell into that or decided to do it and you’ve been doing it ever since.
Unknown Speaker 4:40
So, that’s interesting. I’m actually trained as a molecular biologist and immunologist at Columbia. Rheumatologists, my training and then the chair medicine was a guy named Mike Whitesville who had come from Hopkins asked if I would become the head of pulmonary allergy critical care because my research was related to how the IGA molecule was being made. And it was actually to my mentors at the time Kais Alacati and Lynn Chess who were division chiefs at the time said that for people like you who really do science. And wanted some leadership roles needed to move into so it actually was at the, at the cheering of two of my scientific mentors that I moved in. And so I ran pulmonology critical care which I was trained in none of those things at Columbia. And I did that for seven years and then became chair medicine in Iowa. And then the dean in Iowa from 2008. And then was asked to take this decision in 2012.
Gary Bisbee 5:57
What was the kind of decision point to come to Hopkins when they came and said, Dr. Othman, will you come to Dean and CEO at Hopkins, What went through your mind there?
Paul Rothman 6:08
Well, the interesting point I had not looked at and I told my children is three of my children and my wife moved to Iowa. And I told them I’d never moved them while they’re in high school. So I had a one year where my middle son was graduating, my youngest son was entering high school that I looked at some places just to see if I wasn’t gonna move. There were four more years in Iowa and I loved Iowa, we loved Iowa City, we loved Iowa, we love the people. But I said if you know, I was at that time, at the point where I probably thought I had one other big job and looked at some things. Then Hopkins came a calling. And, you know, for someone like me, who is a physician scientist, the ability to help lead this institution was a dream job.
Gary Bisbee 6:51
Yeah, a preeminent research, scientific institution. So yep, a big easy decision, I’m sure. Well, speaking of which, can you describe Johns Hopkins today? I’m sure we’re all generally familiar with it, but not with the details.
Paul Rothman 7:04
So Gary, as you said, I serve as both the Dean and CEO of Johns Hopkins Medicine. Johns Hopkins Medicine encompasses both our health system and the School of Medicine and this year will have $9 billion of revenue. In total, it is a preeminent health system, six hospitals, large ambulatory centers and primary care clinics. And we have two hospitals in Baltimore, one in Howard County in the Washington DC area and one in Florida. We have the school of medicine, which is obviously the preeminent ranked number two, but really, I’d argue the number one med school in the country. Actually, in the world is what I would say. But then we also have an international division, we have a large insurance division called Johns Hopkins health care that has several different insurance products. And we have a homecare division. So it’s a large enterprise with very great people-mission driven, which is what I love about it, and really focused on improving the health of our communities that we serve.
Gary Bisbee 8:07
Looking back on the eight years or so that you’ve been here, was there anything about Johns Hopkins that actually surprised you that you weren’t expecting?
Paul Rothman 8:16
What I love about it, is, as I said, it is really mission driven. It is here to help people. We are always focused on helping patients and we serve advancing research. I think just the focus on that, I think was pretty unusual. We have a very unusual payment system in the state of Maryland yet I don’t know about that, that is unique and interesting. And, you know, the truth is, I had never really looked at Hopkins to come to before that once briefly for my first job. But I was just impressed with the depth and breadth of talent, both across the health system in the school. It’s an amazing place. Very collegial and just, it’s a great place to learn.
Gary Bisbee 8:59
Good. Well, they’re delighted to have you here. No question about that. Well, let’s turn to precision medicine, which is an expertise here at Hopkins. Clearly leading the way, as a run up to that digitization and particularly, clinical data has been something that has expanded over the last 10 or 15 years. The hitech act in 2009 actually ended up spending $30 billion in digitizing medicine, which I’m sure is part of the opportunity. And that led to analytical models like AI and so on. But what do you think about clinical data? And what the potential for clinical data is for whether its patient outcomes or costs are?
Paul Rothman 9:53
That’s a great question, Gary. So if you think about it, there were really two drivers that led to us. Think about this. Utilization and precision medicine, the first one was this huge investment we and others made in electronic medical records. We had a homegrown one, like many people did. But the fact and the complexity moving forward, we, like many other people implemented Epic. And that’s a huge investment. And although people think it’s in a financial investment, which it is, for me, the large investment was a time that all folks, providers, nurses and physicians and other providers mid levels, were putting into documentation. I mean, it was a huge commitment. Everyone goes home at night and finishes their charts. My wife is a gastroenterologist, she’s at home finishing her charts. And so it is a huge commitment. And a huge resource we put to it and from my role to go, we’re going to put all of that both the money to run the system and the time and effort everyone’s putting into it, we needed to leverage it to take better care for patients. So you have that driver saying, well, you’re gonna make this investment, you got it, you have to use it. So an increase in value to healthcare. The other major driver was that it really is a time when there are three revolutions that are occurring at the same time that we could bring to bear on questions of precision medicine. And the first revolution is a revolution of measurement. And that revolution has to do with the ability to take a human sample and get the person’s entire genome, every protein and make the proteome, the metabolome, the transcriptome, knowing every metabolite, and so you have this revolution measurement, not only in that of the olmecs, but also imaging, which is nothing like we’ve ever had before. Things like Octa from the back, I believe all these measures, tremendous sensitive measures, we’ve never seen the revolution of measurement, you have the revolution of data. So we have our data scientists who, in other fields, such as astrophysics, have learned how to take large amounts of data and understand them. And the third revolution is a revolution of connectivity. The fact that not only now with implementation via mars, we have all the different parts of Hopkins Medicine for more hospitals, or outpatient clinics connected by one common record. You also have the connectivity that comes from patients who were there I watch, we can be connected, we know what we can find out what they’re doing at home. Right? Right. We have a lot of ways we’re connected to patients in the hospital, we can tell where they move in how fast they move. There’s just all this connection that we have between different patients in the healthcare system and between different parts of the Africa system. Take that all together. It leads us to think of how we can leverage all that to do precision medicine.
Gary Bisbee 13:09
So AI being an advanced analytical technique, we think about it probably in administrative terms, supply chain revenue cycle, and so on. But you’re really using it for clinical terms here. Can you describe that?
Paul Rothman 13:25
So I think, again, if you think that it’s a large collection of data, the question is how can you analyze it to better take care of patients using machine learning AI, and we are doing a lot of things that we think are really good. We have Sachi Surya in our school of engineering projecting if we can use data of inpatients to better predict sepsis. Which is a large problem and other people are doing it, but Sachi’s AI paradigms that she’s developed, and using the data here at Hopkins is terrific at predicting sepsis early.
And so she developed his paradigm, and there are others. I’m not going to go into it, but it’s really good for us. And so we actually now have it implemented in all our hospitals, actually. And the key thing is, you know, false positive rate. There’s a lot of things going through that you learn as you’re trying to understand the clinical utilization of these AI things. We have been a leader in pancreatic cancer for many years, we have a Felix project led by people like Elle Fishman and folks in here that are using AI to see if we can predict pancreatic cancer earlier and better using CT. Again, machine learning. To understand we can do because the earlier you can find it the more likely you can actually have a curative surgery. And the third one we’ve done is radiation therapy where again, the group has taken lots of data and tried to see if we can use AI to better help our radiation therapists or better guide photons. So those are just three ways we’re using it. And that’s a ton more. But it’s at the point where we can use AI to actually better care for our patients. And we think that and you know, others are doing it too. So I think it’s a huge future for medicine. And Hopkins, we’re doing that today.
Gary Bisbee 15:17
Oh, you’re leading the way here. Let’s turn to precision medicine. We’ve kind of built up to this, but it’s a term that is either precise or not, depending on who you are. How would you describe it, Paul?
Paul Rothman 15:31
Right. So when we went down position, we had the same issue. So and it means different things for different people. Some use it as a paradigm for how you bring or mix in. But we thought a little bit differently. We said, for many diseases, what we now presently call a disease is probably more than one disease in which we have a common set of symptoms. But in fact, more than one disease, and when I think again, I used to have pulmonary allergy, croquet would be asthma. So asthma is not one disease, asthma is a several diseases that all have vertebral proko spasm. But we know that there’s allergic asthma, there’s non allergic asthma, there’s aspirin induced asthma. Part of this is exercise induced asthma, there are at least four or five different ones. And if you look at the data from a lot of these complex human diseases, that’s probably more true than that. And the question is, we know that different therapies affect different subsets. And so the idea for us in precision medicine is can we bring all these measurements and, and analysis of big data and connectivity together to find sets of patients that will therapeutically respond in a homogeneous way, or have a homogeneous path way of their disease outcome? And so that’s what we’re trying to do. The way we do different than other people is we thought we want to focus on the patient, and have our clinicians take ownership of it because we thought, or clinicians really rethought we could ask the question, What data do I need to bear on this patient that can help me with decision making. So we began, we want to do it in a disease focused way. And so we started with two diseases, we began with multiple sclerosis, and prostate cancer, they’re very different, but we’d had long standing, naturally recognized disease centers, and we said, we’re beginning precision medicine centers of excellence, each of you will tell us what data you need to analyze. What we then did was, say, well, we’re going to need a data platform, because you can have a lot of different types of measurements, you can have your electronic medical record, you’re going to have your imaging, you’re going to have whatever the measurements, you can have your connected monitoring devices, and you’re gonna have all that can we build an IT platform that can take all that data, and let our data scientists then analyze it. So we collaborated with the Applied Physics Lab here at Hopkins, and ourselves in our School of Engineering. And with collaboration with Microsoft, we build a data lake in the cloud, that can take all that data and make sure that it’s the same patient, whose omix it is whose imaging it is whose electronic medical record and put it all together in a safe and accessible place for our scientists. And we were able to build this. We call it PMap for our IT platform. And now we’re at the point where we’re asking our scientists to use that to identify tools that clinicians can use to identify sets of patients. And so we’ve done it now we started with those two, we now have 17th, precision medicine Centers of Excellence around different diseases, and we’re going to be at 50 within five years for you, and each of them are really focused on a patient with we now have a disease from pancreatic cancer, lung cancer to COPD to polymyositis and scleroderma, a whole variety of diseases to see if we can use this data we’re collecting use all the measurements are collecting to better guide therapy to hopefully at some point the patient.
Gary Bisbee 19:20
Jeff Bezos made Amazon successful by focusing on a consumer. Johns Hopkins is gonna focus on areas focusing on a patient.
Paul Rothman 19:28
Yep. So we’re doing it today. We’re building tools.
Gary Bisbee 19:32
Yep. How do you select which disease entities are going to be the subjects of the centers of excellence?
Paul Rothman 19:41
We just began with some centers that we knew were true nationally, or that we have national expertise. And what we’re doing is we’re doing these centers, we understood that one of the things we really want to do is use these tools to bring values to patient and so as we started to select them, we have a faculty member who just goes to the potential centers of excellence and says, okay, you are great. Clinician societies tell us how this center of excellence can bring value. And as we’re thinking about the centers, and the measurements and the tools we’re building, we’re asking right up front, tell us how this will be valued. And we’re trying, you know, somewhere that rare diseases, but we think at Hopkins, we see a lot more than other places do so we can bring value to the patient, because we have expertise here that’s unique. And we see a lot of our patients with scleroderma, for example. And we can bring value to people who might see that’s an empire of a quality to diseases like COPD, where we’re trying to bring value to health systems and payers, because we’ll build paradigms that can help get the needed variation out of care. And better direct therapies that are more specific to the patient’s needs. So we think of value both in the quality and also the affordability component.
Gary Bisbee 21:08
We talked for a bit about how Washington is thinking about research, academic medical centers and clinical research. How are they viewing? It is obviously a broad term, but how does our congress elected officials view precision medicine?
Paul Rothman 21:24
Well, we actually remember Rob Califf. We presented to him and he was really thrilled within and you know, I think people want to say, okay, you have great ideas here, let’s see things of value. And so we’re really focused on demonstrating that. What we’re trying to do brings value to the patient, both in terms of improving outcomes and decreasing costs. And so that’s what we’ve built in tools around.
Gary Bisbee 21:49
Direct use of the $30 billion of high tech act dollars, you think they’d be very pleased with the work that you’re doing? What about scaling, precision medicine, and we can use it here at Hopkins top research center. What about out back at the University of Iowa, for example?
Paul Rothman 22:08
So when we think of scaling, when we think about scaling up and scaling out, scaling up is what we’re doing, building more centers and getting more tools built. Scaling out is an important thing, because you’re right, if we just build a tool that’s important for our health system, it’s great for us, but really not making the impact. So we are partnering with some other health systems, that we’re going to build tools with them, we’re now working with Allegheny in Pennsylvania into its partnership to see can we build tools that for at least two different populations, one here and when Hopkins can bring value in a disease, like COPD, and so we’re trying to, we think scaling out is really important for what we do. And we want to partner with health systems who are interested in trying to see if we can develop tools and talk about a variety of patients, and they might have a different level of a different type of patient.
Gary Bisbee 23:07
We talked about connectivity. And I’m thinking now of interoperability, which is a big issue with the EHR. Do you see that as a challenge to scaling?
Paul Rothman 23:21
Potentially, I actually think that they’re gonna get the interoperability of the EHRs resolved. I think given the $30 billion you just quoted that if the government doesn’t make that happen, it’s the government’s fault. To be honest, I mean, they made this investment they need to make sure that then everyone has access to everyone’s data when we need to, and that’s for the patient.
Gary Bisbee 23:44
What about the clinicians now? I’m just listening to you, of course, with your fabulous background, but will the average clinician think he or she has to become a data scientist in order to execute precision medicine?
Paul Rothman 23:59
So that’s actually interesting. I’ll answer that in two components. So today, what we’re trying to do is to build tools for a clinician, so that’s why we have these being developed by clinicians in the disease. And the idea is to build tools that physicians and nurses and other providers can use at the point of care. The other point that he raises is that physicians have to be data scientists moving forward. And actually when we think about the future of medicine, I actually think that a physician will in the future and even today will need to be very savvy in analysis of data in fact. And so patients will have a lot of the data, they’ll have it on their iPhones, they’ll have their whole genome on their iPhone, they’ll go to know their predisposition to that or that disease. And there’ll be collecting data at home about their blood sugar or blood pressures that in fact, one of the roles of a physician will need to do is the interface between the data and the patient. Either interpreting the data for the patient or working with the patient, to use that data to better their case, I really think in the future, and we’re thinking about how we train people to ensure that they have a real knowledge of how to analyze data. I think the truth is most of our med students today are much more savvy, in analysis of data than I was growing their age, they grew up on it. So I think it’s the truth. It’s folks my age that I think, need to ensure that they understand data better than we do, because we didn’t grow up with it.
Gary Bisbee 25:41
What about medical education? I mean, this is a fairly advanced view that you have here. Is that true? Do you think that medical educational track along with this?
Paul Rothman 25:53
I think most people are thinking about their policy decisions. I mean, you know, AI is, you know, when you look at the studies, AI is really helpful. The physicians are really helpful. And together, they synergize. And so, for anyone who isn’t thinking about that enhanced care, we can provide patients through the use of data through analysis of data and utilization of the data we’re collecting. I think he’s not thinking about the future medicine. So I think most of medical education, certainly at Hopkins, we are thinking about how we ensure that our students and our fellows and residents both understand how to analyze that and understand how you’re going to use that data.
Gary Bisbee 26:35
Let’s think about the future. Five years from now, 10 years from now, how diffused will this kind of precision medicine approach be? Do you think?
Paul Rothman 26:45
I mean, there’s your quote, that this change occurs in a longer timeframe than you think and it’s disruption is greater than you. I don’t see why that would not hold true here. So if I, some of it’s happening now. It’s but I think I can’t predict it. I had several mic departments reviewed last year, and I asked experts in pathology, radiology, as I said, When are you going to have your fields really changed? And the range of experts is yesterday to five or 10 years? And so I think no one knows. And I think it’s going to be different in different fields. Some are happening today. I mean, as I told you, using AI in sepsis, early sepsis section is happening today and here in other places, while the real, you know, use of AI in imaging. analysis is occurring, how long will it take? I have no idea because I just could be wrong? It’s probably longer than some people think, but it may be, you know, a larger change than others predict.
Gary Bisbee 27:49
About the exponential increase in analytical techniques and data and so on. You could be right here.
Paul Rothman 27:56
I just don’t know the answer. But I think the idea is, it’s changed, it’s going to occur. And so let’s embrace it, and have better care for our patients, because it is going to happen.
Gary Bisbee 28:06
What about the governance process here? How have you discussed precision medicine with your board?
Paul Rothman 28:12
Oh, the board is very well aware. We are putting real resources in this and the board is aware because we think it is the future. And I think what we’re working on, what we want to do is make sure we have the partnerships because we have a great one with our Applied Physics. But we really are attempting to build partnerships with a variety of people who we think will help in this space. And that’s it. That’s the next stage.
Gary Bisbee 28:39
So one final question. If a young person comes to you who is interested in molecular biology at MIT and wondering about medicine, what advice would you give him or her?
Paul Rothman 28:52
I think medicine is the best career anyone can ever have. I think it is something where if you’re interested in science, or you’re interested in patients there is room for you. And I think I still feel privileged to be a physician on a daily basis for having patients to put their trust in you. And to be able to have a career where, you know, we are so dedicated to improving the lives of patients and dedicating our lives to that and and it’s a career that I know everyone, you know, some physicians bemoaned changes, and it is more difficult to practice now than it was 20-30 years ago. That’s more administrative. It’s more documentation. And I get all that because we have the ability to help people with their lives and make a difference in their lives. And, you know, it could be hundreds of thousands so if you have the right scientific breakthrough millions. And it is a privilege to be able to do that and change humanity and improve the lives of communities is something that’s very special in medicine.
Gary Bisbee 29:59
You’re a unique and outstanding leader in the field. We’re delighted you’re in medicine and you’re delighted that you’re here at Hopkins. Thank you so much for coming on.
Paul Rothman 30:08
Thanks for having me.
Gary Bisbee 30:10
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