Just to see where this is heading. So, there is lots of work and lots of interesting things
happening in the space right. So, if you take the visualization or visual data to the next
step, now people now we are looking at how 3D printing can be used in medicine. One of
the most exciting areas is surgery planning where while in the earlier screen when we
saw looks things like MRI that can be visualized on the screen, it is still a virtual image
and it is still on a flat screen; a doctor would get a much better perception of the
problem they could hold the problem in their hand before they did surgery right.
So, the approach the three d printing is taking us can we take the same MRI type data and
actually print out a physical model, which the doctors can now play around with to say
what does the condition really look like, how should I approach surgery what is the
best direction of access, what are the other organs that might or might not get affected
and so, on. So, this is now real it s not any more rough fiction there are hospitals
in India that are using this quite regularly, it is not very difficult technology there
is some technology to be done. But where it is become very helpful is in treating I would
say somewhat abnormal conditions especially with young children, where every case is almost
different the reason they come to the hospital is there is some particular abnormality that
needs to be looked at, and it would not fit any standard textbook that you have.
So, rather the doctor starting the surgery and then trying to figure out what is really
going on, they can do a lot of pre work and hopefully reduce surgery time improve the
kind reduce the kind of cuts there to make improve chances of survival success and so
on. So, this is one end of where we are going; so but know again healthcare data has
been used a long time to understand you know health conditions better apart from individual
data. So, the other thing that is very important
is to look at macro data aggregate data, and say how can we get useful information out
of it. And I thought you know you lose one use one of these as the, this is probably
one is a classic example. So, there is anyone familiar with this London Cholera outbreak
can heard of. Sir John Snow.
Yes exactly. So, the story I mean this happened 170 years back right. So, Cholera at that
time was the causes of Cholera were not known. In fact, it was strongly held it was because
of bad air or something like that, and there is lots of debates about it. In the 1850s
London had a serious outbreak of cholera, thousands of people died IDo not unfortunately
remember the exact number, and you know there the public health system was struggling with
this, and basically people who are just dying and there was no idea of what was causing
it and therefore, how this could be stopped. So, along came this person john snow who I
think was an architect and a physician type of person, who said let us analyze this data
in a more systematic way, and what he did is basically took a map of London and on that
marked all the locations of all the people Who had died from Cholera.
Luckily they had public record here to just go to every hospital, pick the thing get their
address and start marking it down. And then he had this intuition that, this were more
than probably intuition that this was not because of just bad air it was because of
contaminated water. So, at that time the source of water was hand pumps public pumps. So,
what he did is he also mapped where the pumps were there are the little circles you can
see here and there and he started seeing clusters of data and so, for example, that particular
location was where there was the highest death and if you look at it.
He said that here is the pump and here are the people, and because it is a hand pump
that is why you had to go for water you would go to the pump closest to you logically right.
So, all he had to do was do kind of find clusters of people around pumps, and a found was a
very good correlation he was able to show that the number of deaths were very high around
certain pumps. So, then he went and studied those pumps, tested the water in the pumps
and found that it had been contaminated by sewage essentially, because at that time they
had only soak pits as there was no sewage system in London at that time.
So, he was able to establish that contaminated water contaminated by human waste, was the
cause of Cholera. And actually took a long 20 30 years for this to get accepted because
partly probably you know the corporation did not want to accept that this was a problem
and. In fact, that pump is still standing there as a testament to that, and incidentally
the first action of the municipal corporation was to remove the handles of all the pumps
that were contaminated right there is something wrong in the water let us not use it.
But he persisted with that was able to convince people about this and it led to the formation
of the sewage system in London itself and you know it is completely changed urban life.
So, this came from a methodical study of public data this is and so, John Snow is known as
the father of epidemiology, he started this whole field of how do we look at aggregate
data and study epidemics and growth and spread of epidemics. So, this is a great example
of how data can be used and can help, and you know now it is done very standardly whether
you look at Zika virus or Ebola or any other newer things it is all about tracking down
patients and any but.
And any you know what could be the possible transfer points we also; obviously, now 150
years later, we have standards pressure analysis tools to do that. So, the same thing could
be done by ou know this clearly tells us where the concentrations are this shows us
where the, you know increased concentrations and so on and so, now, it is fairly standard
for in epidemic analysis epidemiology to take aggregate data and try to find patterns out
of it visually. So, that is the. So, these are all been in a different ways in which
data has been used in healthcare is being used in healthcare I should say, at a from
a macro level to an individual patients level down to very fine data about from MRI s or
b p monitor. So, now you have ambulatory devices. So, you
can get minute by minute data about your blood sugar or blood pressure or anything over days,
and you can do very effective analysis and treatment, it s no longer just a random check
or a symptomatic check. So, this given so much data, it is clearly a good candidate
for looking at algorithms that can help us manage all this data. Data is there in many
forms it is becoming very difficult for an individual or small group to actually analyze
all this data, because of the variety and volume of data and it seems to be amenable
to machine learning type of algorithms sorry first of all we started the image and signal
processing for sure, how do I take ECG data and from that figure out the possible infractions
that are happening. Now,. So, these kinds of algorithms are coming
into play heavily in radiology pathology. So, how can. So, looking at a slide under
the microscope is really, an image crossing problem at some level I mean a brain does
it very well builds an intuition and experience and all that, but at the end of the day it
is finding patterns in a particular slide. So, pathology, radiology type of things which
are innately image processing oriented seem to be good candidates for image processing
algorithms and there is been tremendous progress in that.
So, you familiar with IBM Watson as an example, which digests all the information that is
fed to it about cancer research cancer treatment effectiveness of treatments and so on and
for a given patient is able to give you suggestions with probability essentially, saying this
approach is the highest probable this has 80 percent chance success this has 60 percent
chance success and so on. Presumably give you a very precise recommendations,
at this point they are still recommendations because even though even we do not have enough
data to know if that is the right way of doing it Google yeah.
question in IBM Watson does this famous u sketch that all could if you have to take
you likely to get up a 100 tack with an extra with or. So, whatever nature is go to the
nearest hospital then. So, there was one case then the other case, where if the person had
repeated something cough fever some other symptoms then IBM do not want to put in that
the actually got a heart attack. So, how do you.
Yeah these are very much incomplete that way. may you can like kind of suspended the whole.
Yeah exactly. so, I mean there is no they are not yet at the level of maturity where
they can be they can replace the doctors know where that right, they are still recommendation
systems and our decision support systems and for a near future we should expect that doctors
will still take the call. So, but at the same time the benefits of this seem to be very
good because they can see patterns in data which humans may not have the time to do.
So, one many there is you know many doctors are suggesting that this should be the way
medicine is done, that you have access to a software algorithm whatever that is going
to give you there is going to help your decision making and you take the call.
So, if you think about it increasingly doctors especially specialists are going to spend
less an less time with you because of the crush on you know the patient load that they
have. They come out of a surgery spend two minutes talk with a next patient go back to
the next surgery and so on. It is unrealistic to expect that they will make a completely
objective deterministic this one, while a machine could point out errors could point
out. So, as a support system it is very valuable
and that is likely the way it will continue for some time. One of the big challenges and
is that it s a black box right. So, Google now has this they are applying their deep
mind alphago type technology to, pathology sample this one and they claim that they can
come up with the diagnosis better than most pathologists ok.
But pathologists are not convinced, because they want to understand what led to that determination.
You talk to many doctors who are still very skeptical when they like the idea that it
can help, but they want to know why what because especially the confirmation of diagnosis and
the treatment plan depend a lot on what are the chain of reasoning that went there and
till our AI and ML systems can evolve to that we are still in it is going to be a support
system, but it s moving at a rapid pace. So, we should expect realistically then in five
years or less I mean Google's latest alpha chess thing shows that it does not take very
long time to figure out these things, if you can find the right problem to attack.
So, let us say roughly in a 5 to 10 year time frame, we should expect that algorithm based
decision support systems are going to be very important in the healthcare thing. For that
you need to have reliable data that is curated, tested and you know accessible for these algorithms
to work. And now we move from diagnosis to predictor systems more on the lines of what
you are saying if certain pattern of events have been detected can that predict say the
fact that this person will need to be taken to an ICU or that this person can be discharged
and will not need a readmission, if they follow the protocol right. So, we can you can use
predictive systems to identify possible problems, but still you know in a very still in a very
early stage. But this is again one of the promises that
we are looking at, and it will come with uncertainty for some time.
One of the big benefits that we can think about is while we are talking about a lot
of these in hospitals in high end clinics and so on, it has potential big impact in
public health and I will just uses in uses now and then come back to it later. So, one
of the big challenges we have in public health is, the sheer skewed ratio of doctors to patients;
the number of doctor there is roughly one hospital per hundred-thousand people in India
roughly and if they all have to come to the major and these includes all kinds of hospitals.
So, if you all the patients have to come to the major hospitals just a processing of this
is a huge load on the system, there are very few specialists the pyramid is very sharp
and can we use technologies like this to improve the screening process and there are lots of
company startups especially working in this space; for instance can be do eyes screening
in the field apply intelligent algorithms to it, and have only the critical or the important
cases actually be referred up and most can be treated in the field.
So, there are people doing this for eye for inner ear and various other various other
kinds of diseases, it relies on the fact that we can capture data remotely using the smartphones
or some low cost devices; do some computation on the data typically images or other data
come up with a and come up with a diagnosis, they come up with a categorization and decide
that only this category of people actually need to see a specialist and others can be
treated in the field. If that is successfully done, it could reduce
it could have two big impacts one has reduced the load on the specialist. So, they can treat
the serious cases better and more importantly reduce a need for patients to make trips to
the main hospitals, which is one of the biggest burdens on the system right now.
So, this has a big impact potentially in the public health system. So, maybe that is a
good way to say. So, what is public health or what are the issues in public health.
Inspection resource. Yeah come to the problems and lots of problems
yeah. So, basically we are talking about the health services that the government runs to
cater to and it is open to anybody to. So, in India we have a very particular flavor
of it, you can say the UK systems and other kind of public health because the universal
health coverage there and so on. So, we will focus on the India version of
public health, which is primarily how do we have hospital hierarchy of a medical facility
starting with sub centers, primary health centers community health centers, taluka and
district hospitals, urban hospitals, tertiary care hospitals, super specialties and so on
all under the government health umbrella, the government funds these, managers programs
in these and you know tries to ensure the overall health of the population, this will
improve health indicators of the population. The a little bit of status check is probably
important. If you look at the UN sustainable goals health
indicators are very important, and if you look at a couple of them. So, the maternal
mortality and the under five motor the infant mortality, India is not doing I just blow
up those. So, we have a maternal mortality of 174
plus per hundred thousand live births, and infant mortality about 47 birth of these numbers
about two years old is improved a little bit from there, it is far from where recommended
standards are or what you know the more advanced countries and some of the not so, quote developed
countries are in. So, we are lagging behind by health indicators,
we are we have large government programs running it, but the impact is still not there, there
are studies that indicate that some improvement has happened in some areas, but long way from
the numbers we need to get; we probably need to look at how mortality rate of close to
50 which I think it is about in some places it is less, how can that come to 3 and 4 and
things that is the goal. So, there is still a long way to go in terms
of bring the bringing these numbers to a more acceptable level, we should not be happy with
these numbers or should not accept that thing. There is a goals that have been set up for
20, 20, 20, 25 where these are supposed to be brought down significantly and you know
the ministry of health is tracking these and doing something.
So, if you take this as a macro problem that in some sense these are just two indicator
there many more such indicators. So, we have there is an expectation that IT
can help and what the government and lots of other players are betting on it is spending
a lot of time and money on this, and it is expected that with the proper use of IT, we
can improve on many of these things which will all contribute to the health indicators.
Most fundamentally the problem that we struggle with this access to healthcare availability
affordability of healthcare and quality healthcare; this has to do with numbers the availability
is very low, because the patient doctor population is very poor, they are not necessarily easily
to access either for physical other reasons and you can always come to a city, but the
cost is very high. So, that is not a good answer, and even if you do get access can
we be sure about the quality of health care. So, you can and; obviously, we want to reduce
health indicators that is probably the most important, but you also want to improve the
quality of life for people at least in their interaction of healthcare, can we reduce waiting
times, can we you know right now if they have to find access to medicine medical care in
a high end hospital whole family, typically travels to that place spends a few days there
and you know it is a very painful and expensive process.
So, can we push more of this out into the places where the patients are, can technology
help them we have to manage multiple reason. So, there is it is a reality that there is
a severe shortage of physicians, even if the government cranks up and in suddenly expands
the number of super specialties, there is still a long way from. Right now we produce
single digit numbers of specialists every year 20 would be large, and we are talking
about a population of 1.2 billions. So, the scale is just huge. So, we have to.
So, one of the promises of IT which many health professionals say is that, improve help improve
the reach of physicians can the same physician now address a much larger population through
the use of appropriate take and this is where telemedicine is playing the potentially big
role. And then how can we improve the efficiency
of a lot of money and effort is being spent is it doing the right thing, is it producing
the results. So, how do we track it how do we monitor it and so on. So, this is the other
place that IT is expected to help, and you know in all the challenges of India of large
geographic distances the different entities that are providing health service and so on.
So, if you take stock of where we are, IT has been used in public health, but it is
impact has been minimal at this point. It is not really you know if you if you should
look at public health in terms of large numbers are out there, there is very little IT actually
being used a lot of the earlier discussions we said are happening in private or in urban
hospitals maybe some of the high end the government hospitals, but definitely not in the large.
So, it is actually a problem it is been going on for a while people have been trying, but
many other have not got past the stage of pilots or local implementation. So, typically
there are initiative that will try to do some IT based solution for one particular problem
or issue or whatever and that runs in one state for a while and does not sustain after
that. So, the government has been putting greater
emphasis on this, and the national health mission which has been around 10 years or
something has put a lot of focus, at least on gathering health data and putting programs
in place for specific conditions. So, there is an HMIS system that is used across many
health care government healthcare programs and so, the track and it is the first phase
on many of these is to how do I monitor what is going on, how do I make sure things are
happening are enough people being are enough visits happening is, enough vaccinations happening,
are pregnant women given the right you know sequence of a booster shots I mean medications
and so on. And the first phase of most of these has been
just helping this and traditionally the first traditionally these were done by just paper
or excel based reporting, and now that has slowly gone online. So, now, you have online
health reporting systems and to that extent they have been successful in the sense there
is a structured way in which data is available, that is available data for policy and planning
and other macro. If I look at these problems you can abstract it away from healthcare and
say these are known problems because IT is there to I mean enough IT solutions do that
right it does not work. So, while for many of these the solutions
seem to be obvious, the initial attempts are the obvious solutions have not worked, and
their work and not work because of some of that issues that we will discuss right so,
but at the same time we have to live with the fact that there is a shortage of doctors,
I mean that is a separate problem to improve capacity, but if you do an also the numbers
you have to get 100 x the time. So, doctors that you have that is not feasible
in a near timeframe. So, what we can look at is how can these systems make those doctors
more effective at many different ways, can they get better data. So, they can do their
decisions better, can they track what they are doing better can they get assistive systems
that will help them how can a MBBS doctor in the field get access to enough better decision
making system so, that they can treat the patient better.
So, we can improve one part of it is how do we improve the effectiveness of the doctors
or there. So, the solution to this might be to look at public health programs as an example
and to target an intervention of IT, which is what is largely happening. How do you take
some part of the public health program and improve it is functioning, and that will have
some impact some improvement it is probably at this point it is still limited, and maybe
we will look at some of the challenges in a in a minute right.
So, just; so, on the what is being done there is now also an attempt to start capped. So,
the reporting systems are purely reporting they are more like monitoring the program,
but there is increasing effort to put patient data also in various centralized or decentralized
databases. So, NIC has a e hospital system, which is now mandated at major central government
hospitals and they are starting, to use it still using in a very limited way, but in
5 to 10 years maybe that will be better right. There are many issues with this which touch
on some other issues for instance how do you identify a patient.
So, patient can go to any hospital in any state any level of hospital, how do you identify
a patient.
no person walks in and says I am so and so, I want to now connect that to any other data
about that patient that might already be in the database somewhere. So, there is a china
Unique ID. Unique ID. So, what kind of unique IDs
Like Adhar. Like Adhar right. So, we will open up will
get back that is what is happening. So, so the first concept that came in is at least
at some level we will have unique IDs, and you will use some mechanism to come up with
a unique ID right. Now the next attempt that is being done right now is Adhar will be that
unique ID. So, hospital systems especially in the government
framework are using that to identify the patient, that while that works that; obviously, opens
up other issues especially when we talk about privacy of medical data today the ID is unique
to a hospital. So, the way hospitals used to do it historically was every visit was
a new ID effectively, your duration of stay or encounter was an ID.
Now, at least it is you for your repeat visit you will get back the same ID. So, at least
continuity of information within the hospital system is there correct. So, that is the expectation
and that is a goal that has been set up, we have to see how long it takes for that to
come there are some practical issues with that going back to privacy and confidentiality
and security and things like that. So, how do you share data in a way that will provide
this, because right now because doctors are not sure how about how the data will be save
used they are restricting the data they are put there the.
Yes in India we have not evolved those both policies practices rules yet, there is a lot
of discomfort among doctors to shared data this is comfort among hospitals. So, its
but it is been it is been achieved in other uses of
Ah not in the even there, there are restrictions on what kind of data can be accessed yes.
So, that is where it is going. So, the idea of this unique ID is to get there, it will
probably take a couple of iterations to get there because, but clearly we need to enable
continuity of care and for that there is no way around it, there has to be a way to get
data from anywhere and make it all integrated. There the government is there are different
challenges, there you have different hospitals. So, some are run by the central government
or some are run by the state government stay health as a administration of health is a
state subject. So, each state has it is own way of doing
things there is no each program is a vertical silo that cuts across things. So, each of
them has their own way of doing it. So, there is not been enough attempt to integrate this,
I am happy if my program can do better I will worry about integration later has been the
common thing. So, now, that is beginning to change, but we have to look at it. So, the
use cases in a public health are even more challenging than what we might have thought
in terms of continuity of care; and classic example that is used to test out any solution
that comes up is migrant workers as an example. By definition they are mobile they are moving
around they can move anywhere. So, while the government has a very good program
for maternity maternal care the mother and child the programs and all that, it still
breaks down somewhat across state and other boundaries. So, this unique ID is attempt
to move fix that, but it is still not fully there. So, the classic example that we you
should think about as use cases solve these are, imagine a migrant construction worker
the woman who is pregnant in she is a construction worker and she is pregnant.
So, she goes to the local PHC and go starts her treatment. And the government has and
the program has very decent and her checks diagnosis and recommendations that go on.
Almost certainly she will go back to her hometown for the actual delivery and that could be
in a different state, and at that point if that particular piece of paper that she is
carrying does not get properly identified as I said effectively that history is lost.
So, in some sense that doctor starts all over again the history is the oral history that
this patient may be able to translate may be able to articulate, and then that gets
captured; treatment happens and then let us say the child is born and the child is reasonably
healthy and so on, at some point she will move back to some other place probably right.
So, she is constantly moving and its so, critical that and we are talking about infant mortality
for up to five years. So, at least for five to six years this information has to be tracked,
and it she is definitely going to have many different touch points in that thing. And
right now there is no system that consistently will be able to handle all this information,
while the architectural framework and theory are there in practice it is not yet evolved.
So, these are practical problems that are still need to be addressed. So, why is not
happening. Since we know all the solutions and we technologists believe that these I
mean many people we talk to are including myself these are easy things to do it. So,
it is done why are we struggling. So, what might be some of the reasons why it is not
got any better than this? regulate regulatory in school
There is no regulation is school right to do what you want.
Too many stakeholders. Too many stakeholders yes.
data. Reluctance of few yeah .
paid for all these. Government what is the problem money is not
issue. Lack of resource.
Lack of . Resources.
What kind of resources? technology.
Technology in India they are supposed to be an IT superpower right.
when compare to No that I think lack of I mean yeah you are
right at I mean lack of the appropriate set of resources, where it is actually a interdisciplinary
thing, you have to have doctors technologies all sit together and frame from you and that
is not happening enough. resource for example, when two administrated
for all the citizen, that is all there is not a problem, but then government cannot
do because lack of resources. Its lack of priority or not lack of resource
the flu was identified as a priority program, it would have got the attention it is not
right. Now they are much more serious disease burdens to deal with.