This is a complete, edited transcript of Episode#2 of the You+AI podcast.
Ranga 0:00
Youplusai is pleased to present a podcast series on healthcare and AI. In this season, the focus is on doctors. I meet doctors to get their on-the-ground perspective of healthcare, understand challenges, and we explore together where artificial intelligence may provide the best outcomes for all.
I am Ranga, creator and host of the youplusai podcast and the human face of youplusai in Twitter, SoundCloud, YouTube and Medium. I have a background in engineering and I’ve been a technologist in the networking domain for more than a decade working on switches and routers, packets and buffers wired and wireless. If you’d like to know more about me, go to LinkedIn and search for Rangaprasad Sampath. I am passionate about artificial intelligence and this podcast is my effort to bring awareness on both sides, that is, for technologists like me to know the problems on the ground in healthcare, and for medical practitioners to know where AI may intervene to drive the best outcomes for them. Today, our guest is Dr. Goutham Mehta. He’s a consultant in multi organ transplant surgery and hepato biliary pancreatic surgery at Manipal group of hospitals. He was trained in transplant surgery from the University of Pittsburgh Medical Center in USA. He has more than 10 years of experience in transplantation and he’s interested in research related to technology in transplantation, genomics of liver cancer, and xenotransplantation.
Welcome Dr. Goutham Mehta. It’s a pleasure to have you.
Dr. GM 1:57
Hi Ranga. Thank you for having me here.
Ranga 2:02
Okay doctor, let’s get started with our podcast. Now, could you sort of talk us through what kind of organ transplants are prevalent today?
Dr. GM 2:12
So, in India, at this moment, the most prevalent organ transplantation is kidney transplantation. Right now, only after USA, we do the highest number of kidney transplantations. Another organ we transplant very, very frequently is liver transplantation. And now, there are some centers which are coming up with transplanting hearts, particularly in South India. There are a couple of centers in Bangalore and Chennai. We also started a program in pancreas transplantation which is again now upcoming in in India though it is a very established transplantation in the Western world. So one or two centers are doing lung transplantation. So we have pretty much all the transplantations which are done in the western world, in India right now.
Ranga 3:15
Good, thank you, doctor, but like you said, there is probably variation in what we do more and what we do less, probably kidney transplants, we do a whole lot compared to what you said, and some of the others like lung and probably pancreas to a low degree, I guess, in the number of specialty centers that are available in India. So, just wanted to sort of pick your brain on this. Now, you talked about the US how transplants are or the Western world and what kind of transplants are there and you gave us an idea about what’s there in India. So, are there specific challenges to India and India like developing countries?
Dr. GM 3:53
So, in India, the most important challenge is first trained professionals which slowly, we are training enough doctors to understand transplantation and how they go about doing it. But more important challenges are one – regulatory networks and overall regulation part of transplantation in which, so basically the organs, which we get it from brain-dead donor, deceased donor, we call it our state property. So it has to be allocated to a certain guy who’s listed in the registration list. We have a waitlist and the overall organization of the waitlist, overall allocation of the organ, overall regulatory framework with which it works is quite rudimentary. So those are the challenges which are, which can be solved with more input from the government. But these are definite challenges right now, compared to USA where they have agencies for allocation, agencies for procurement of the organ, agencies to take care of the overall logistical process.
Ranga 5:26
I see. So you see in the US or in the West, there is a more structured process, but here it’s not so structured.
Dr. GM 5:32
Exactly.
Ranga 5:34
Also, it would be a good time to talk about, when you talk about organ transplantation – do we have a sort of a waitlist, where let’s say I have a defective kidney or a liver and I am on a certain state or a nationwide waitlist, so that I know that when I can get it or at least when an organ becomes available, as you say from from a brain dead person. Who is it allocated to? So how is this known?
Dr. GM 6:03
So I’ll break this question into couple of things. There are registration lists. So individual state maintain their own registration list. So in fact, if you, if you need an organ, let’s say liver or kidney, you can get registered in one center in all the states. You don’t have to be registered in only one state, you can register in all the states. So the allocation right now works this way, is initially, the patient in the local hospital in which the organ was generated is a priority in organ allocation, second – the zone, third – the state and in the end – national allocation.
Ranga 6:55
So there are degrees of where it can be allocated.
Dr. GM 6:59
Yes, but there is no national registry. So, if a person wants to get him or herself registered, he has to decide one – which hospital, because sometimes it happens so that you registered in a hospital and they get organ immediately and you’re on top of their list and you get organ faster than probably another. The patient do not have that kind of information. In fact, doctors can’t have, even doctors don’t have that kind of information. So, that kind of information is not available. So you do not know how long do you have to wait to get the organ and overall there is concept of national allocation of the organ but there is no maintenance of national registry or who needs organ transplantation
Ranga 7:57
Okay, so which means that even if I register as a recipient, I have a problem. It’s unlikely that I know how many people are on the list or what’s my number on the waitlist and when I can likely get an organ that’s not something I can know.
Dr. GM 8:12
So, that kind of information probably you will be able to get from a particular center. Okay, so the center in which you’re getting registered, they could probably tell you that you have four more recipients waiting ahead of you. But if you want to know how many patients are waiting ahead of me in the entire state or national, it will be slightly more difficult to get into. State ,you may because there is a Jeevan Sarthak in Karnataka which maintains this kind of list and if you go ask them, they’ll tell you – you’re waiting from that center at fourth, fifth, and in rotation based on how you get the organ. But it’s very difficult to know when we look at the organ.
Ranga 9:02
Okay, because I’ve seen when I was looking at the US or Canada, it seems like there is kind of a waitlist at which the recipient can know, it’s easily accessible to the recipient to know what number is he likely on the waitlist. Of course, that doesn’t mean he or she knows exactly when they will get an organ because that I think has still so many factors and we’ll get into that shortly. But they can at least know how many people are there in their state or how many people are there nationwide looking at this particular register.
Dr. GM 9:34
Yes, that way, USA is very, very methodical and systematic about it. The waitlist, also you can get a lot of data about average waiting time in individual region of USA. How often people like for a liver, how often people die waiting for the organ – that kind of data is also available in USA. But if you ask us for similar kind of data in India, I don’t think we have that kind of data. We have an organization, Liver Transplant Society of India and we are working towards developing that kind of registry data. But I think we have a long way to go.
Ranga 10:28
Sure. I mean, but certainly, that’s a good thing, because then that means there is opportunity for technology to come in to sort of pull the data together. Of course, government policy has to be there. Definitely. Regulation policy is definitely part of the process, but certainly I see opportunity to better manage the data that we have.
Dr. GM 10:47
Sure. Yeah.
Ranga 10:49
I wanted to just ask you – what are the factors that determine whether an organ will really match i.e. whether a donated organ will really match and what what in your opinion would be such factors?
Dr. GM 11:04
So, this all depends on individual organs – different organs has different matching criteria. So but the main matching criteria we follow is blood group matching. So that is the most common – that’s a basic matching criteria and that’s how the organ gets allocated. For a liver, that’s about it. The next step is the size match – how big the liver of the donor is and how good the liver is. Sometimes you have donor liver which is fatty or we do not know the information how much fat is there in the liver. The fatty liver is not so usable, the quality of the liver is not that good. So, and also the size – if the larger liver, if the recipient is smaller, so – size match is important in liver and the quality of liver is important. We somehow intuitively try to do this based on our previous experience – what we try to do is try to give a very good liver to a very sick patient and quality wise, a not so good liver to not so sick. So, our outcome, what we’ve seen is outcomes are better in that.
Ranga 12:44
Okay. But that is more based on your experience and your gut feel of what you see in the recipient.
Dr. GM 12:51
That is in the liver. In kidney, they become slightly more complicated. After blood grouping matching, we need to see if the recipient antibodies match – the HLA matching. So, some of the recipients are very sick and they developed a lot of antibodies. If they had, let’s say previous transplantation, then they already had a foreign body and our immunity would have developed against a foreign kidney So, they are more difficult to match. So, based on what kind of antibodies, based on what kind of HLA, based on the cross match, kidneys are matched. So, it’s actually more tedious process in kidneys. I would say the similar thing about pancreas. Lungs, hearts are not, we do not know much about that. But overall, the kidney match would be probably the toughest to make decision and takes a lot of your bandwidth make that decision.
Ranga 14:00
Okay. So obviously a better match means a better transplant outcome for the patient, correct?
Dr. GM 14:08
Yes.
Ranga 14:10
So I have heard about this. I guess in the US they use a score for kidneys, kidney donor risk index, KDRI or something like that. Maybe it’s, maybe it accounts for some of the factors that that you just talked about. Besides these, would you sort of look at other factors also – for example, I think you mentioned about the patient’s medical history, but things like is the patient allergic to something odd, if the patient has had a prior, let’s say genetic history of a certain disease or a certain condition in their family, in genetics? So are they particularly suited or sensitive to certain drugs – so how about these factors and apart also do lifestyle factors matter? Like you know, I understand the recipient is a sick patient and that’s why you’re doing it a transplant, but in general what their lifestyle is, are they like a technologist like me or doctor like you or you know they are they are more sedentary or have a more active lifestyle. So do any of these count?
Dr. GM 15:16
Okay. So there are many questions in this. Okay, so basically let’s say the first part of your question was KDRI, kidney donor risk index, that is a KDPI, a predictive index to see the quality of the donor organ. Now, the matching part does not even come into the picture – the donor factors are taken in to see the outcome in that KDPI. So that is being used in USA to decide whether to accept that organ or not. But the problem in that is again we do not include the recipient factors and in organ transplantation, the logistical factors of keeping cold ischemia time that is the time where you haven’t taken the organ out of the donor and transplanted in the recipient, the time gap, that is ischemia time. We want to keep it very low because outcome very much depends on how much ischemia time we keep. So, those factors also are not included. So, there are multiple factors which are not included and they do have effect on outcome. So KDPI is one of the aspect of it, other logistical and third is recipient base factor.
Your second question was whether the genetic content of the makeup of the individuals, it does make a huge difference, not just in our outcome, also how they react to the certain immunosuppressive drugs we use. Like we do use a very strong drug called tacrolimus. And we measure the levels of the drug – what we have seen is some individuals need quite a bit of that medicine to maintain certain amount of levels. As in USA, we’ve seen that in African Americans, we had to give quite a bit more than Caucasian population to maintain that level. So there is a pharmacogenomics aspect of it, which definitely improve the outcomes if we do it properly. And also it helps us in medical decision making that how much drugs we need to maintain for tacrolimus. We need to maintain certain levels of tacrolimus to make sure that the individual is adequately immunosuppressed to not reject the organ.
Ranga 18:06
So which basically ties into transplant success.
Dr. GM 18:08
And third part, sedentary lifestyle – yes, it does matter. In liver transplantation there is something called frailty index. So that a lot of research coming out with not just in liver transplantation but in surgery per se, patients who are frail have worse outcome. So how do we check for frailty – you do some CT scan, see the muscle mass, overall activity. In liver transplantation, we ask them to walk for six minute walk test and see how much they walk, how much speed, grip strength – all those things we use, we various adjunctive parameters to see if the person is frail or not and what we’ve found that frail people don’t do so well. Compared to guys who are more active more muscle mass, they didn’t have a sedentary lifestyle, they could walk for longer time. So those people, the outcomes are better. I have to say that it’s not just in transplant, there are papers coming up even with the small procedures – the frailty can be little problematic post operative.
Ranga 18:09
I think that’s a very interesting point, doctor. Thank you. And does gender matter here? I mean, is there is any study to say that, women versus men who received organ transplants do better or worse?
Dr. GM 19:35
Yeah, there is. So interesting that you bring it out. There is a gender bias in transplant. More men get transplanted and in living donor scenario, more women are the donors. Lesser women get transplanted. These are social aspects of transplantation – at least in India. Yeah, at least in India and abroad also – I’ve seen not so glaringly different, but India, it is glaring that, you know, the men get transplanted more often than women. And women are more often donors than men, and in small kids, the girl child usually doesn’t get transplanted. These are the social aspects of it. I just wanted to bring that out.
Ranga 20:37
I think this is an important aspect of the world that we’re living today. And where I see doctor is an opportunity for technologists, right. So we talk about, you know, when we use AI, or let’s say, if we were to use technology to do better matching, probably the first step is whether it can surface this bias, whether people can become aware that there is a bias and it comes out. Let’s say, you know, for all the transplants that are done in the last five to 10 years, there is a data driven approach which surfaces the fact that this is what has happened, then basically that awareness hits everybody. Otherwise, as you say, I mean, obviously, you’re practicing in India. Now, you know, the reality about what happening, but once it surfaces from the data, then nobody can deny – the data is in your face.
Dr. GM 21:25
Yeah, yeah. So Data is the King.
Ranga 21:28
Exactly. So that’s where I think we see definitely, an opportunity for technologies like AI to actually mine this data. And if you could bring the bias to the surface – I think that would be a super beneficial thing.
Dr. GM 21:43
in this field, you will see women are not getting transplanted, and the AI may pick it up as it’s a normal behavior.
Ranga 21:51
Exactly. So I think that’s a very nice discovery probably, that we hit upon here. Yeah, that’s good. So let’s continue on. So certainly I think as you made the right points about, you know, genomics is important and you know, other lifestyle factors – frailty is important. So are these factors that you mentioned, are they being looked at in a in a standard way, let’s say in India across the different transplant centers or even in the US?
Dr. GM 22:18
Yeah, that’s what makes it very interesting. People do it. As a doctor, we are trained to do it. We consider a lot of things before thinking of transplanting a certain organ into a recipient. So we first look into the recipient, how is the recipient, what is his size, and then we look into the organ and see if the organ is very good and then we try to say – Okay, this organ might work well in this recipient. So in that, we do take frailty as a parameter, how MELD score – another score that we use in Liver transplantation we use to see how sick the patient is. Then we also see whether he’s getting getting up walking around, we also see whether you can climb flights of stairs. So, there are a lot of things we put it together and come up with the best match. But I feel there is sometimes innate bias in all of us to take those kind of decisions day in day out, particularly at nighttime when you are sleeping and you have to make that kind of decision. And I personally feel AI may come very, very handy in these kind of scenarios where those decisions which could be data driven and given to the doctor saying that see, according to AI matches, this is a problem. Then it will help doctors take a better decision. That decision making in transplant is something which can be supported by AI.
Ranga 24:18
Great. I think what you’re hinting at doctor as I understand is, if you make it data driven, you’re ensuring some kind of standardization because doctors, I’m sure you guys do all checks and balances, but like you said, there are times when doctors are stressed out, there are times when they’re sleeping, and they’re coming out. So it could be possible that one thing is missed out, or, you know, half the thing is missed out. So somewhere the consistency across, let’s say across transplant centers, across doctors all over India may not be there. But if we bring a technology here, which doesn’t need sleep, doesn’t need to eat, which doesn’t need vacation and it’s working all the time behind the scenes – as you rightly said, it may pop up the key things that are of most interest to the doctor. Then of course it’s in the hands of the transplant physician and the transplant coordinator to say – okay, now I’m going to look at these and am I going to go ahead or not. So in some ways, it becomes a an assistant to give you the most relevant information.
Dr. GM 25:25
Most of the time what happens is when when I get a call for the organ – for the donor organ we have to see through lot of the blood test and we have to see how the quality of the liver is through ultrasound, sometimes CT scan, and sometimes biopsy. In recipient, normally they are on the waitlist but we don’t see them often. So we are actually seeing them after three months after they got registered. So we do not know how they are right away, like at this moment, how are they, some people do get admitted a couple of times before they come to get transplanted. So then that aspect, you have to see the recipient, and you’re not doing all these things – there is a system and they’re giving you information and that’s when you have to make a decision. So again, a lot of not just junk, also good, the relevant information we do not get because it’s lost somewhere or we do not think of it that at the time to ask or try to get that because sometimes you are overwhelmed with so much information and you’re also managing the logistical issues. So transplant like I always say is an event in itself. Whenever there is a transplant happening, there are multiple people working at different stages and all those things should come together, transplant should happen and all those individual guys make a huge difference in the outcome.
Ranga 27:09
So something that I was researching some time back – I found the startup called OmniLife. So what they have done is on th lines of what you said – they also recognize that the transplant is an event in itself. From the time the call comes in that the donor organ is available, till the time it actually gets transplanted – there is a whole workflow. There’s a whole chain of events and there’s a whole number of people who get involved. So what these guys have done is they have actually built a chat application. Think of it like WhatsApp, right, but the beauty of this is that it is optimized only for the transparent communication. So while I’m sure much of the communication that you do today is probably over a chat application like WhatsApp or there’s a phone call or there’s an email but there are multiple things and it falls on you as the physician or some of your staff to actually piece all the information together to get the full picture. So what this startup did is actually put all of that information into that chat, kind of popularized this among the transplant community and I think they, they’re probably doing some trials or deployed in the hospitals or centers across the US. So, to your point, transparent communication is key, transplant itself is a big workflow and anything to sort of optimize that is probably a step in the right direction.
Dr. GM 28:38
Exactly. I cannot refute the points and absolutely necessary to have some communication which brings in everything together.
Ranga 28:52
In India, though, we are talking this in English and India is a land of languages. I mean, right from across state to state, the language in which it is communicated maybe challenging. It’s not that if a physician has to travel from one state to another, that doesn’t mean that he knows the language of that state. So this brings in additional complexity I think, of language and understanding the nuances because translation isn’t always you know, successful. So whereas in the US or in a western country, a common language like English may do wonders, right. So even if somebody is building this in India, probably they have to be sensitive to the languages and even the dialects that are used in different parts of India.
Dr. GM 29:34
You are right. We transplant organs in Manipal Salem, which is in Tamil Nadu. We also transplant in Manipal Vijayawada which is in Andhra Pradesh. So I find it very difficult, in fact to even talk to the transplant coordinator in Manipal Vijayawada. And because I know a little bit of Tamil probably I do a better job in Salem, but overall there is, and having said that, I don’t want to be sounding biased, most of the nurses come from Kerala, who speak either English or Malayalam. And then we have a bunch of doctors who speak only Hindi or English. So there is a huge, huge communication and yeah, English could be a common language, but there is still a lot of transplant communication gap with regards to language.
Ranga 30:35
And like I said, anything that can push the needle a little better, I think is better outcome for the patient.
So doctor, just touching upon your speciality, I think earlier, you mentioned this about the MELD score, right. So could you just talk us through what the MELD score really is what does it account for in terms of, I guess we’re talking about liver transplant and the context of that.
Dr. GM 30:59
Yeah. So MELD score is a score developed in USA to predict the three months survival of a patient based upon three parameters – one was total bilirubin, second was creatinine. Bilirubin sort of gives you an overall picture of what the liver condition, creatinine gives you a picture about the patient kidney. And third is INR. INR is a parameter we use, the test we use to see the coagulation, which needs proteins produced in the liver – so if our blood needs to clot, our liver should produce these coagulation factors. So all these three things put together, they created a MELD score. Now, they’ve used this method. So even now, they use this MELD score to allocate the organ. So, it could be between 6 to 14, maximum is 40 and 6 is the least – people who have 40 gets priority. So, they have created a system in which sicker the patient the quicker the organ you can get, but we do not use that allocation in India, we have some different way of allocating.
Ranga 32:33
Okay. So, because again I was reading upon on this aspect, and I noticed that Baylor College of Medicine in the US – they were doing matching as you said rightly in the US based on the MELD score. So, now they want to take in some other factors. So, what they have done is they have done a collaboration with another startup called InformAI and so what the information said is that they have given about 30 years of transplant studies or data of transplants that have been done in the Baylor College of Medicine, successful vs. not successful, transplant that happened, the donor match that happened, somebody who rejected – all of this data. And the hope is that they would come up with a better matching methodology than just using the MELD score. I think that’s what InformAI is working on. Of course, they don’t have a product yet. But I think this was the collaboration between sort of the academia or the hospital and this startup in this front.
Dr. GM 33:35
Okay. So do they want to come up with a new scoring system based on which the allocation happens?
Ranga 33:42
I don’t know if it’s a scoring system. But yes, I think the end goal is they have talked about is that how do we match better? Because somewhere, probably they see that the MELD score is not enough. So maybe they want to take in more parameters. Now whether that will get standardized and publisheded, we don’t know – we have to just wait to see. But this is the sort of the work in progress.
So, doctor, I just wanted to ask you, I mean, we talked about so many different points from the physicians point of view and a matching point of view. Now, let’s talk a little bit from the patient point of view also. If I am a recipient and I am waiting for this organ, what is the most or what are the most important questions that I would have or what I want to know from from transplant communication or being on a waitlist?
Dr. GM 34:28
So as a patient, one – you would want to know approximate, not absolute but approximate wait time because waiting is painful. Some of our patients wait for two years, three years. So you would want to know the approximate wait time which is more probably standardized in western world, where you say okay, you average wait time is 5 – so you have to wait four or five years. But it’s not the case here. Here, it depends on so many decisions patient has to make to figure out what wait time they’re looking for.
Another question they will have is how long the organ will work? That’s the most common question. If they’re transplanting their liver, they are already 60. And they’re like, Okay, how long will it work? If it is not too long, then I do not want to go through the entire process because it is expensive, getting liver transplantation done is quite expensive, even kidney transplantation for that matter, but it’s not as expensive as long term dialysis. So the question is how long this organ will work. So those are the questions which stumps us because we do not know. There are data but predicting how long the organ works is again, very, very difficult question to answer.
Ranga 36:08
Yeah, again, maybe again, another opportunity for technology like AI, because AI and machine learning in particular is about predicting anything, whether it’s the stock price tomorrow, whether it’s the weather temperature tomorrow or how much is my car going to cost in three years from now? So again, I mean, these are probably simple examples, but maybe in the case of a life changing decision, like if a patient asks, how long is this going to last – based on transplant outcomes that have been done, based on tracking the data of the recipient, like what stage of life they’re in, how old are they, what are their other complications? So if this data was available, of transplants done in the last, let’s say, 5 years, 10 years, and it was also monitored how long the recipients lived after the transplant, then possibly with AI and machine learning, there’s a good opportunity because with this large amount of data, they may be able to give you an approximate answer to say, okay, you know, sort of you fall into this bucket of this kind of a patient this kind of history. If this kind of donor organ is there, you might approximately work for this much. Again, an opportunity is what I see.
Dr. GM 37:25
Yes and also, some people refuse organs on very limited data. Sometimes, if you refuse organ, the chances of you dying on the waiting list is higher and if you had taken that organ, you would have probably survived. So, that risk assessment again, is very complicated and I do not personally know how to answer those questions. But if somebody can give that kind of answers, then it will be easier. To say, Okay, let me take this risk to accept this organ because if I don’t take it, the chances of me dying on waiting list is very high.
Ranga 38:08
Yeah, then that’s a life changing decision, it’s a life and death condition.
Do I take this organ, given the complications and given what I know how long this will work? Or should I risk waiting in the hope that I will get a better organ? But then I don’t know how long I’m going to wait for. It’s a tough trade off, I think. And certainly, if we can make this data driven, and with AI, we can give some sort of a better answer than what we can give now, I think that’ll be good.
Dr. GM 38:39
That’ll be great actually if somebody can come up with that kind of predictive analysis.
Ranga 38:45
So we’ve had a great conversation Dr. Goutham Mehta. At the end, I just wanted to sort of ask you, how do you envision the AI assisted future of transplantation? What are the key things that you sort of see, as you move forward 5 years, 10 years when AI is going to play probably a more better role or better assistive role for physicians. How do you see that vision?
Dr. GM 39:11
So, what I feel in transplant, the field of transplantation, which is probably most data driven among all the fields right now. So the kind of data you have it in USA about all the transplants, the registry, it is one of the most data driven fields in medicine. I feel AI will definitely help in decision making. If we have better tools in decision making, both for us and for the patient. Decision making is the key – not just accepting the organ, or matching the organ, it’s also the post operative management of the patient, long term management of the patient. All these things I think decisions will be taken better with AI one, two – some of the things which are very rarely available, like a pathologist who’s trained in transplant pathology is far and few between in entire India. So if I want to see a liver biopsy and the pathologist who’s trained in transplant is so scarce, I am always second guessing his diagnosis. So that is something which can be like histopathology of transplant. There’s too many biopsies happening – kidney transplants, we do so many biopsies, liver, we do so many biopsies. But we do not have trained people to take care. So there probably is another aspect in which data and AI driven pathology would come into the picture. Right now, those are the things and another thing is logistics of doing transplantation.
Ranga 40:12
I think you mentioned that – anything that can better the logistics and help you arrive at a decision faster, bring consistency and bring everybody together results in a better outcome. And, of course, the thing that you mentioned about bias in countries like India. If we can be more data driven, some of these biases that maybe only some of us know, or know it internally but we can’t do anything about it, it can raise the awareness of that bias to a level so that people become aware and conscious of.
Dr. GM 41:00
So, let me give you an example of how transplant surgeon in USA is changing policies using this kind of data. Well, there’s a guy named Dory Segave at Johns Hopkins. What he does is he first comes up with the data. comes up with the paper and says these results work. And now we need a policy change. So with the new research he goes to the papers and say this is the research and then he goes to the policy makers and say these are the data, so you change the policy. So he could bring in radical changes like now HIV positive donors can donate organs in USA – HIV to HIV transplant has been started. These are huge in decision making. So policy changes, data driven policy changes are again one more thing which we have to pursue.
Ranga 42:02
Absolutely. Sounds very important. And also, what I think is when the data is in front of you, it’s very hard to refute it. But if it’s just a feeling that I’m coming and telling you that hey, this should be done. You know, you will say – hey you think this way, somebody else thinks another way. So somewhere, data is a leveler – it helps the policymakers see what is the fact. Yes, and then it really can kind of move them to make the changes faster than slower.
So thank you doctor I think we had a fantastic conversation. Thank you for your time. We touched upon. very interesting aspects of how AI could play a role and also appreciate your vision of how you think the transplant space can be transformed using technologies like AI and machine learning.
Dr. GM 43:37
Thank you. Thank you very much. Thanks for having me.
Ranga 43:50
Thank you folks for listening in. Do give this podcast a five star rating in your favorite podcast app. Do connect with me Rangaprasad Sampath on LinkedIn and follow my online handle @youplusai on Twitter, SoundCloud, Medium and YouTube. I’ll see you soon with another episode. Enjoy!