Future Medicine

Future Medicine

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DANIEL DENNETT: Consciousness is an intellectual illusion. EMMA JOHNSTON: The Earth and its oceans are a giant ventilator upon which all of our lives depend. TOBY WALSH: There's nothing right about giving a machine the right to kill people. VLADO PERKOVIC: Good evening.

I'm Vlado Perkovic, Dean of the Faculty of Medicine and Health at UNSW Sydney. Welcome to this conversation on the future of medicine presented by the UNSW Centre for Ideas and UNSW Medicine & Health for the Sydney Science Festival 2021. Firstly, I'd like to begin by acknowledging the Bedegal people, who are the traditional custodians of the land that I'm speaking to you from today. I'd also like to pay my respects to Elders both past and present and extend that respect to other Aboriginal and Torres Strait Islanders who are listening today. For tonight's conversation, we wanted to explore some of the key developments and directions in medicine and in health more broadly.

In my role as Dean of this faculty at UNSW, I'm privileged to work with an incredible array of researchers and clinicians. It was hard to select just four people for this event, but all of our guests are not only leaders in their own fields, but they're also distinguished by a forward-looking approach and a rare ability to see beyond the horizon. They're people who are pushing the boundaries of their disciplines, but are also motivated by a common objective to improve the health of all the people in our communities. We asked our audience to presubmit questions and we've had a great response, so thank you. I've picked out three questions that highlight some of the key issues that we'll be talking about tonight - firstly, how important will developments in technology, especially Ai and robotics, be within medicine in the future? How do we balance the need for human skill, empathy and instinct with technology, AI and robotics? How will we deliver universal healthcare across the country when we don't have adequate infrastructure, digital skills and a software industry that's perhaps not adequately embraced innovation.

And finally, are these future medical innovations going to further widen the gap between those who can access them and those who can't? These are great examples of some of the fascinating questions that have been submitted, exploring the themes of innovation, technology, as well as equity - all key areas of interest to you as our guests I'm sure and we'll be exploring these in more detail shortly. To host our conversation tonight, we're delighted to have broadcaster and journalist Tegan Taylor. Tegan will explore these questions and many more with our guests.

I hope you enjoy the session and a reminder that we would love to have you join the digital conversation during tonight's event. Please comment on Facebook, use the live chat on YouTube or send us a tweet and don't forget to use the hashtag unswideas. Thank you. Tegan, over to you. TEGAN TAYLOR: Hello and thank you, Vlado.

The land I'm coming from now is that of the Jagera and Turrbal people. I acknowledge this land and Elders past, present and emerging and, as Vlado said, we're exploring the future of medicine tonight. We're going to hear about how integrating machine learning into healthcare settings can make for better care as long as it's done ethically, having diverse specialists in one place could streamline care for patients and applying the idea of precision medicine to public health. But first, imagine if you could understand undiagnosed cancers before they were observed, detect cancer clones that were going to be resistant to treatment ahead of time. Now we're really talking about the future of medicine and underpinning this future is cellular genomics.

Joining us now is a man who's helping translate the technology into clinical practice. Associate Professor Joseph Powell is a biomedical researcher and statistical geneticist and head of the Garvan-Weizmann Centre for Cellular Genomics. Welcome, Joseph. JOSEPH POWELL: Hi, Tegan. Thanks for having me.

TEGAN: Let's start with a definition. What is cellular genomics? It sounds very fancy. JOSEPH: Yes, cellular genomics is essentially a technology type which allows us to generate sequencing data, so information on our genomes, but at the level of individual cells and the reason why this has been so revolutionary is that genomics and generating sequencing data has been around for quite a while, it's made a huge impact already in medicine, but it's traditionally been done at the level of what we call bulk sequencing nowadays and that's really where you would take a sample from a patient, a cancer sample, and you would sequence all of the content from millions and millions of cells, which is fantastic, it can be used for some really important outcomes, but it doesn't give us any information about what's the difference between one cancer cell and another cancer cell, for example, and cellular genomics as a technology type gives us that information and then analysis of that data allows us to understand why the differences in cells in a cancer, for example, or immune cells that circulate around your body - why do those genetic differences between them impact our response to treatments or, you know, why we respond to an infection or indeed why do we even develop disease in the first place. TEGAN: Right.

So at the moment the way you're sampling things, it's really diluted in the size of the sample - is that what you're saying? JOSEPH: We think the current sort of approach is really lacking the resolution of getting down to that cellular level and that's not to say that there's anything wrong with them, they work brilliantly, but you miss a lot of information that you get with cellular genomics. TEGAN: So I've heard cellular genomics talked about as like being - the original kind of histopathology process is like a 2D process and this is like a 1000D process. Can you talk through what you're looking at, what is the resolution that you're getting, what's the extra information that you're seeing? JOSEPH: Yes, so to talk in those dimensions as you've just alluded to, I mean, that's a good starting point because you're right, histopathology, is 2D physically. You know, you take a section of a cell - sorry, a tissue sample, but you then also generate probably information on a handful of, for example, proteins, maybe 5, 6, 7, 8 maximum, but the information encoded in that tissue sample is probably only somewhere between 20 and 30,000 parameters and so cellular genomics not only gives you the 3D stack within that tissue sample, so you generate this information from all the physical coordinates, but you then generate for every single one of those cells 20 or 30,000 parameters of information and so you have, you know, an explosion in the inner dimensions in both the amount of information you generate for an individual section of a tumour sample, but also the whole aspect of that information across a tumour sample, rather than just a very, very limited histopathology slice, as it happens. TEGAN: So you've got all of this information. Then how do you even process it? Like there's the ability to do this and then you've got to actually be able to crunch that data to make something useful out of it.

JOSEPH: Yeah, this is - I would say this is probably the biggest challenge that we have. So as you sort of mentioned in the introduction, my background is statistical genetics, which I think nowadays is probably more frequently referred to as machine learning or even artificial intelligence and this is really where a huge amount of the research at UNSW and the Garvan-Weizmann Centre is focussed on is the application of algorithms, the development of algorithms and the development of statistical approaches to analyse that really, really high-dimension data to figure out right, well here is the tumour clone that is resistant to treatment, here is the immune cell that is causing that pathogenic effect when you get infected with COVID, for example, or so on and so forth, and so we develop and work really, you know, extensively on the big data analytics of this hugely high parameter sets of information. The scale of it, you know, is genuinely quite phenomenal. TEGAN: So it's such a powerful tool. You can look at cancer cells, you can look at healthy cells.

How do you know when to apply it? Like it sort of sounds like you could get a wealth of information from any sample in any part of any body, so then how do you know okay, when is this an appropriate tool to use? JOSEPH: Yes, so this is, I guess, the story arc that we work on in the research. We do a lot of what we call, you know, discovery or fundamental research which is trying to unpick these mechanisms, trying to understand what's happening, you know, this new discovery of knowledge that essentially wasn't there before and that is really foundational for us. That's incredibly important.

But in doing that foundational research, one of the most important things that we continually keep in mind is what can we learn from that that has got applications into a clinical setting? You know, discovery knowledge is great. You know, that's, to be frank, a huge motivation for me. But, you know, we want to use that information to make an impact on patients and the population and the community.

So one of the sort of settings that we do is work very closely with clinicians and clinician researchers to think and identify the practical problems that they have. You know, if they have lung cancer patients that 30% of them respond to an immune checkpoint inhibitor, what can we do to understand the molecular underpinnings of the 70% that don't respond or potentially what can we do to figure out what's really responding in the 30% and what can we learn from that to inform the treatments for the remaining 70%? So as we go through that discovery research, we focus increasingly on this translational component and picking out the examples that we can make an impact and then, in parallel with that, making sure that we're able to think about the practical aspects of putting that kind of new knowledge into practice and that's important because there's a lot of very careful considerations about the way you change treatment or you suggest a new approach or you, you know, use this sort of stuff to underpin new drug developments. We have to work within existing frameworks, regulatory frameworks, ethical frameworks, and so on.

TEGAN: How do you facilitate those conversations? Like you're in your lab doing your research - I know that it's more complicated than that - and a doctor is out there with their patients. Who's approaching who in these conversations? JOSEPH: Yeah, so when we established both the Garvan-Weizmann Centre, which I head, and an entity between the Garvan Institute and UNSW called the UNSW Cellular Genomics Futures Institute, we made the very deliberate decision to build multidisciplinary teams consisting of what we call basic scientists - not basic, but it's how we refer to ourselves - - TEGAN: I'm sure they don't like being called that. JOSEPH: And clinicians and clinician scientists, so the teams consist already, you know, of clinicians working at that interface and that part is really important because I think if you just have a bunch of academic researchers, a bunch of clinicians and a facilitated conversation, that works well.

You can often have, you know, a nice conversation, but you need people that really understand the nuances of both sides of things to work at that interface. So we embed within all of our teams clinician researchers, you know, as part of the research programs, as part of the translation programs, and then you are able to help leverage their understanding about, as I said, sort of the practical nature of the way that you put these things into clinical practice, what are the real problems that they see in their fields and really supercharge that pipeline of translation. TEGAN: How has this work been applied so far in real people? JOSEPH: So we have, I would say, roughly sort of two major programs of work. One is in immunology and one is in the cancer space and the cancer space is, you know, as I've just been describing. So we focus very much on what can we learn about patients that respond to current drugs on the market and so why - you know, there's drugs like checkpoint inhibitors, fantastic, brilliant, but they only work in a certain percentage of patients and so we've been focusing really specifically on working trying to understand what's the cellular landscape of patient samples that are not responding and using that to basically inform new approaches in clinical trials and, where possible, putting it into practice already using existing drugs on the market. In the immunology setting it's a little bit different because one of the things that we, you know, are quite aware of is that the immune system does really diverse things.

It actually, you know, does lots of very positive things, exactly what you want it to be doing - fights infections, and so on and so forth - but also does things that you don't want it to do, like causing autoimmune disease. So you want to be quite careful about the way that we understand how the genetics of people, our genetic differences that exist between us control that really fine balance between super, you know, healthy function and disease function. So the work that we've been focusing on in that space is really what I call this foundational drug discovery space where we are looking at how genetic variation between individuals, you know, what we call our differences in our DNA make-up that exist between everyone - how does that functionally manifest itself in immune cells and can we then use that to develop new drugs that target the genetic backgrounds of patients that are developing autoimmune disease, but not target people that have healthy immune systems. So that's a much longer program of work before that's translated so this is really, you know, what I call drug discovery work which has, you know, a long horizon for it to be translated and a lot of difficult hurdles to overcome, but it's very distinct in the way that we would think about the way you translate that compared to the cancer program.

TEGAN: If someone has a cancer, like a solid cancer, you can take a biopsy of that tumour and study it. How do you know what to sample in someone who has an autoimmune disease? JOSEPH: Yeah, that's a fantastic question. In some instances we don't know at all and so that's where that discovery fundamental basic research is incredibly important.

So we typically take a blood sample and from that can extract all the immune cells, a very standard approach, and we sequence the transcriptome or the genomes of all those immune cells and we do that for very, very large numbers of patients and indeed people without autoimmune disease. So we then start using our statistical genetics or our machine learning or AI or whatever your buzz word is for this day approaches to figuring out what are the differences in individual cells between autoimmune patients and the healthy individuals and once you understand that and we've done a lot of this work, then you can start really untangling what is mechanistically happening in the cells in the autoimmune diseased patients versus the other. So we have this discovery, you know, component to this where we just need to understand what are the differences in the cells across these 20,000 parameters of information and five years ago, you know, that was almost intractable, but the technology has moved at such a rate that now this is - you know, we sequenced 20 million cells last year, for example. It's phenomenal the change in the technology and how that's enabled our research to continue.

TEGAN: I know that you've described this as a field that is relevant to almost any condition that someone would want to see their clinician about. The applications in things like cancer and immunology you've explained, but what other things do you see this being applied in in the future? JOSEPH: Yeah, you're right. I think about this a lot. I'm really incredibly passionate about the translation of genomics into clinical practice, for lots of reasons, and, you know, Australia in particular, but many countries, has been fantastic at doing this for some cancers - you know, for things like non-invasive prenatal testing and increasingly for what we call rare conditions, where there's often a really important diagnostic odyssey for patients. But genomics has largely I think it's fair to say been unused for a wide range of conditions and diseases that humans suffer from and that's for a whole variety of reasons, but I would say, you know, an important generality to that is that it's because those things act at the level of individual cells and cell-to-cell differences are massive and so this is why cellular genomics, in my opinion, is able to make such a transformative impact because we now can unlock the understanding of all these genomic processes which, you know, we do know how they work, we do know how to translate them, but we can do so for all of these cell and tissue systems for almost any different disease and condition.

TEGAN: It feels like a really exciting area that's really kind of on the edge of what's possible. What do you see as the biggest challenges that are lying ahead for this field? JOSEPH: Oh, that's a good question. I don't know if I'm overly optimistic. I don't see - - (Laughs). Well, I see challenges.

I don't know if they're - they're all ones that I feel can be overcome, so I don't know if there's - yeah, I don't know how big they are. No, I think I would sort of flip that and think less about what are the challenges and more about what are the problems that we need to solve and, you know, for me, as I've sort of described, we have a route for other genomic technologies that are being put into clinical practice, we do know how they can be impactful and they can be transformative to patients and, you know, make a fantastic contribution to society, but certainly genomics has this, you know, other level of complexity because we generate 20,000 parameters of information for a single cell and we do that for 10,000 cells for a patient. So one of the things that we need to think about is the way that all of that rich data can be transferred into a clinical setting where the relevant information is transferred, but the irrelevant information, all of these - you know, as you alluded to, the potential things that might be alarming for a patient but in reality they don't need to worry about it are not transferred. And so that data transfer, you know, that gap between the generation of massive amounts of data, you know, in a diagnostic lab in a research setting and the information that gets put on a GP's form - you know, that is an important piece for us to focus on and I think, you know, it's a problem that needs to be overcome, you know, with some finesse and not without its own challenges I suppose. TEGAN: It's been such a fascinating journey to talk to you.

Joseph Powell, thanks for joining us. JOSEPH: Pleasure. Thank you very much. TEGAN: And just a reminder on how to join tonight's digital conversation.

You can comment on Facebook, you can use the live chat on YouTube, or you can send us a tweet and don't forget to use the hashtag unswideas. Well, hardly a day goes by when we don't hear about how machine learning or artificial intelligence is disrupting an industry. It's usually framed as a good thing and it often is, but there are ethical considerations, especially when the data that's being crunched is people's medical information. Healthcare settings are flush with data - in fact, there's almost too much - so how do you sift through this? How do you protect patients and how do you train the people in the system to make the most of the power of AI when technology is developing so quickly? Luckily we have brains like Louisa Jorm on the case. Professor Louisa Jorm is the Foundation Director of the Centre for Big Data Research in Health at UNSW Sydney.

Thanks for joining us, Louisa. LOUISA JORM: Thanks so much, Tegan, and good evening to everyone out there. TEGAN: Can you give us an example of how data could be harnessed in, say, an intensive care ward? LOUISA: An intensive care unit is one of the biggest producers of data within a hospital. You all know about the machines that go beep.

If any of you have been in an intensive care unit, you will have seen the amount of equipment that's there and all of those bits of technology are sort of producing data, continuous streams of data, about physiological parameters - you know, things like heart rate, things like speed of respiration, blood pressure, blood glucose. But in the past that data has mainly been used just on the spot to monitor that individual patient. What we can now do is actually bring together the data that's generated through ICUs from multiple patients, potentially thousands of patients, and actually then apply these artificial intelligence or machine learning techniques to them to try to produce much more personalised approaches to intensive care, so based on the experience of thousands of other patients like you, what is likely to be the best sort of course of treatment for you in your ICU stay. We can also look at things like predicting for an individual patient what might happen if we change, for example, their artificial ventilation rate or we administer insulin to them to try to improve their blood glucose levels. So it's all about using large amounts of data for many, many patients to then produce a greater degree of personalisation for one patient. TEGAN: Is this happening in ICUs in Australia at the moment? LOUISA: Really it's still in its infancy.

There's some great examples sort of emerging from the research sphere, including in our own work, that do relate to sort of automated blood glucose control and automated control of mechanical ventilation. But even in the ICU, as like all other parts of the hospital, there's actually quite a big implementation gap between what is possible using data and using technology and what actually works in the clinical setting of a hospital which, as you can imagine, there's huge amounts of human factors and, in particular, many of the current sort of generation of doctors and other clinicians are not necessarily particularly familiar with or comfortable with all of these technologies. Rightly, they have concerns about who's making decisions and are they good decisions, are there possible ethical and legal implications if decision making is done in an automated fashion. So I think the big challenge is how to actually make it sort of AI or machine learning assisted decision making but with the clinician still feeling in control and also still being able to involve patients and carers in some of those decisions.

It's not always the machine making the best decision. TEGAN: Right. I want to come to that. But just on the ICU as an example, they're such complex environments.

There's people - literally lives hanging in the balance. That's why they're there. The people who work in those settings are highly specialised, the machines are multiple. Can you talk about the challenges of implementing something like you're talking about which would be really useful into such a complex environment? LOUISA: There's a lot of challenges.

One is actually even extracting the data to do the analysis that I'm talking about. Many of the electronic medical record systems have been sort of set up as fairly standalone systems, as I said, for the point of care, bedside care of patients. Actually getting the data out from the back end of those machines and then integrating them across the various machines and integrating with other information that's really important about the patient, like their age, their sex, the health conditions that they have, it actually poses quite considerable technical challenges and, in particular, because proprietary software is often being used that may not be compatible with other sorts of software. So that's one of the biggest challenges we face is actually extracting data in a form that is then able to have these techniques applied to them.

So that's one big challenge. TEGAN: So there are examples in other industries or in other machine learning scenarios where the machine has learnt to be racist, for example, because of the inputs that were put into it, the biases that go into it. How do you account for that? LOUISA: Well, there are examples from medicine as well and a great example is IBM Watson oncology, which was developed to try to set up new AI-driven treatment recommendations for cancer patients, and the problem that they ran into, not so much that the algorithms weren't good, but rather the data that they got wasn't sufficient. They didn't have enough patients, they didn't have enough depth, they didn't have enough multimodal data about patients to build a robust algorithm, so what they decided then was to work with some expert clinicians to develop some sort of pseudo patient journeys and they incorporated these into the dataset that were being used to train the algorithm and found that, as a result, the algorithm actually was producing some inappropriate treatment decisions. So, yeah, basically the algorithms and their results are only as good as the data on which they're built and, as I mentioned earlier, that's one of the biggest challenges that we have is actually accessing the really large numbers of patients and the very detailed data that we need to actually use things like deep learning, which is one of the most successful sort of AI techniques that currently underpins so much of what you see on the internet at the moment - all of the facial recognition techniques, and so on.

TEGAN: When you're designing these AI machines, you're really - the risk is that you're baking in biases that you don't even realise are there. LOUISA: That's right and again I come back to this - there's a huge amount of data being generated but harvesting that data and being able to access it for these types of purposes is a big problem and in fact around 40% of all sort of published machine learning research in health actually uses one single dataset which is called MIMIC-III and comes from basically Boston, Massachusetts, one hospital there, and they have managed to create a version of that dataset that meets sort of privacy protection principles and make it quite generally available and so the machine learning community has basically pounced upon that dataset and used it. You know, it has some good features because it enables them to sort of benchmark how well their algorithms perform against one another, but it has some pretty potentially negative downsides as well because if we look at that population, it's really not a very similar population to what we might get in a Sydney suburban hospital, for example, a very, very different sort of mixture of ethnicities and backgrounds.

For example, obviously no Aboriginal Australians are present in that dataset. So you do run a risk of basically excluding certain groups and not being able to be sure that the algorithm is performing as expected for all parts of the population. TEGAN: Right. So it feels like there's a lot of possibilities, a lot of challenges, though.

Are they as big as they seem or is it just - are there small tweaks that need to be made or are there big kind of systemic - - LOUISA: No, they're pretty big. TEGAN: Okay. LOUISA: They're pretty big and they do relate to, you know, as I said, accessing data. They also relate to things like making sure that you bring the community along. People have to feel safe and secure in the way that their data are being used, that their privacy is being protected and, you know, many members of the community don't have a strong understanding of these technologies because they are developing so fast, but equally, many clinicians don't have a strong understanding of the technologies either.

So I think one of the big challenges is this really rapid upskilling of the health and medical workforce and probably sort of a different health and medical workforce that includes traditional clinicians - doctors, nurses, and so on - but also data scientists and engineers working alongside them. You can't wrap up every skill that you need in a single individual here because you need that clinical insight, that understanding of clinical workflows, understanding of patients as well as really sort of highly advanced technical skills in data analytics and data management. TEGAN: Do you see data scientists, data engineers, like you just said, being embedded in hospital teams in the future, say in the ICU example we were talking about before? LOUISA: Yes, absolutely.

It is starting to happen, but when I first started at UNSW five or more years ago this was something that people in the health system, in health departments and hospitals were coming and talking to us about is we don't have people embedded within our systems who can do these things and we can sometimes hire people who come from engineering or computer science backgrounds, but those people often take quite a long time to actually become useful within the health system because they don't actually have any background in health, understanding of biology or, you know, understanding of how one would integrate these sorts of technologies into clinical scenarios and clinical workflows and that's - in fact we set up our own Masters program in Health Data Science at that stage which is now starting to pump out the graduates and many of those - in fact, I think all of the graduates so far have actually taken up employment within various health settings or research settings and many of them as part of their training have actually done research projects basically sitting embedded in clinical settings. So only yesterday we had the final presentations from our latest round of students and it's really sort of gratifying to see that there are people who are now really quite ready, workplace ready, to go and apply data science skills in health and medical settings. TEGAN: So that's the Master of Data Health Science program that you just said at UNSW.

Can you give some examples of what those people are currently working on or what sort of projects seem to be in the works? LOUISA: They're very diverse, but an example - one that was presented yesterday was using very complex linked data from FaCSIA, so from the community services sector, to evaluate the sort of longitudinal journeys of children who have contact with community services and with child protection and to try to look at whether or not there is specific programs that are operated by FaCSIA to try to improve outcomes for these children and prevent, for example, out-of-home care placements being affected. So that project brought together data from I think around 12 different sort of data systems and then had to apply quite advanced analytic techniques because the data were observational. It wasn't like a randomised control trial. These were real children and there are many, many sort of external factors and other factors that had to try to be controlled for to look for this. TEGAN: So it's really exciting that you have this Masters program that's developing those specialists, but what need is there for embedding this sort of content into the courses that doctors are learning when they're at medical school, for example? LOUISA: I think there's a total need and this is something that I've actually been trying to advocate for since I've been at UNSW and we've over the years managed to get a little bit of content, we have a digital doctor content for the year 3 medical students. But excitingly as of next year we are actually going to be running a new medical honours course work in clinical AI and that's very much - you know, obviously the clinicians need to have an understanding of the underlying principles of methods, but most of them are probably not going to be themselves crunching data or developing algorithms, but they need to have an understanding of how they are implemented in health and how they can critically appraise their use within health, how do they know that a particular AI tool is a good AI tool, when do you trust the tool, how do you sort of work with it, how do you integrate it into the way that you work and excitingly a fairly - you know, a substantial number of the UNSW medical students excitedly put up their hands to participate in this first round of the clinical AI honours program.

TEGAN: That's so exciting. So it must be a real challenge to be teaching something that by the time someone is actually out in the workforce and perhaps being exposed to these things the landscape might have changed because it's such a developing field. What are the sort of core principles that you can teach that are going to keep someone in good stead over the next 5 or 10 years? LOUISA: Well, as I said, it is largely about how do I assess and critically appraise whether or not this is a good tool and there are - we're sort of developing some sort of checklist-type approaches for them to use, but I think really importantly it's going to be the young clinicians who know so much more about this than the traditional model, where the senior clinician imparted wisdom. In this case the young clinician is the one who's going to be educating the older ones.

And it's something that's struck me ever since I've been at UNSW is so many of our medical students have done high-level mathematics while they're at school and then they enter the medical program and they don't do any mathematics or statistics anymore. So in some ways these capabilities haven't been nurtured in the way that they could be and, as I said, there's quite a lot of excitement amongst the medical students in now being able to do this and I guess, you know, it's going to be those - the older clinicians are going to be accepting of the fact that the younger guys are the ones who are going to be teaching them. TEGAN: So if you're looking forward the next 5, 10 years - let's be optimistic - what do you see as the main challenges and what are the main things that you're excited about that you think might be on the horizon? LOUISA: Well, obviously I'm excited about the burgeoning availability of electronic medical record data in Australia and I think, you know, encouraging signs that it is going to become more readily available. I'm really excited about some of the new algorithmic techniques that are starting to be applied in health and medicine and I mentioned deep learning. It's had many applications in the area of image processing, but is now moving - you know, it's really overtaking most other forms of machine learning for a whole range of sort of predictive tasks and it's really very exciting to see how many new publications are coming out reporting those techniques. The challenge I've already mentioned is how do we actually put all this to good use to actually result in improvements in health and health care and people like me who may be a little bit boffiny and interested in the algorithms do need that interaction with people who are implementation scientists to make things work and I think we haven't got that quite right yet.

The other thing that, you know, remains a challenge is ensuring that we do maintain public trust and that privacy and confidentiality of individuals' information is maintained. Another thing that I'm quite excited about which I thought I might mention is a thing called the Join Us register, which is a new development being led by the George Institute and UNSW, but with 33 different university and Medical Research Institute partners from across Australia and the idea behind this is to increase the ability of Australians to participate in health and medical research. Basically people are asked to join, to join the Join Us register, and what they're then doing is agreeing to be contacted about medical research studies that may be of interest to them. There's a big issue at the moment that most Australians are quite interested in participating in clinical research, particularly if they do have a health condition, but only a very, very small proportion of them actually ever do get enrolled in a clinical trial.

So I'm really hopeful that Join Us will help to address that problem and sort of make clinical research much more accessible to the whole community. TEGAN: It's a fascinating space to watch. Louisa, thank you so much.

LOUISA: Thank you. Thanks, Tegan. TEGAN: It's a familiar situation for many of us - you get a diagnosis, say of a skin cancer. It's a scary situation.

But what makes it even more discombobulating is when you realise that you need to run all over town to see the dermatologist, the cancer doctor, get your blood tests, report back to your GP. Surely, you think, there must be a better way. Well, to discuss what that might look like, here's Anand Deva, Program Head of Plastic and Reconstructive Surgery at the Faculty of Health and Medical Science at Macquarie University. Welcome, Anand.

ANAND DEVA: Thank you. TEGAN: So you're an architect or involved in integrated care models. What are you talking about when we're talking about integrated care models? ANAND: Well, integrated care means many different things to many different people, but essentially what we're trying to do is to simplify the system for patients. So as you mentioned, the diagnosis of something like skin cancer could certainly be quite scary, particularly then if you add confusion, cost, waiting times, inefficiencies of going from one doctor to another, from one place to another and so in its simplest form an integrated care model around skin cancer would put all the elements that would be required to treat that patient in the one place at the one time and that's exactly what we've done.

TEGAN: It's not just about putting people in the same place, it's also just those people talking to each other. How do you facilitate a centre like this? ANAND: Well, that's a very good question. I think to get a system that's naturally fragmented and at times adversarial to work in a collaborative fashion is really, really difficult. It takes, I think, a collaborative mindset, an open mindset to start with and I think to put that into perspective, if you look at the health system in Australia, it's kind of grown almost in many different directions over the decades.

The biggest change of course came when Medicare was introduced in the 70s and I think that - I'm a firm believer in universal access to healthcare. I mean, all developed nations put health of their citizens first and so I think that allowed us then to provide healthcare at some level to all patients and all Australians. But the problem is that since that time we've had private versus public sector, we've had, you know, specialties versus GPs, we've had health funds versus doctors, we've had industry versus private hospitals, for example, and so each of these components don't actually necessarily like playing or working together.

So to start with I think you need to find people that are open to collaboration, and that's not easy, and then you need to pick a cause and ultimately if you have a patient sitting in front of you with a problem, there's nothing like that to actually make you united as a system in order to help that particular patient. TEGAN: Can you talk about the centre that you're involved in? How is it formed and what has it taken to get it to work? ANAND: Well, you want to summarise 9 years of toil in a few minutes. TEGAN: Could you? ANAND: I'll do my best. It started essentially with support from a grant from NSW Health and I think for that I'm truly grateful. The grant was kind of written almost in a feud state because having been a surgeon for many, many years I've seen firsthand the effects of that disengagement, the fragmentation because patients present to me having been through a very, very fragmented and discontinuous pathway, should I say, and the ultimate effect is actually a poor outcome for the patient. So the call came out from the ministry at that time for any clinician working within the public sector to come up with ideas of how we can better deliver care and knowing about skin cancer, I thought well why don't we put in a grant that actually brings together all elements.

So by that I mean GPs, because a lot of GPs are involved in skin cancer care and some of them do an excellent job, dermatologists, specialists in skin that also treat a lot of skin cancer and people like myself, plastic surgeons. So I wrote it all in a paper, had some wonderful diagrams and kind of sent it off and forgot about it and a few months later actually got the grant and that's when I actually had a moment of panic to think oh, my God, I've got to convert this idea from paper into reality. It was through actually engagement and connections that I've had with my colleagues over decades of practice. I think that's made the difference, having I guess that open and collaborative mindset and finding people with that like mindset. The centre was built partly through public funds, but also through partnership funds and I have to acknowledge the help of Ramsay Health Care, Sonic Healthcare, those big corporates that actually are not easy to deal with but can sometimes, with a persuasive argument, give a little bit to help support a model like this. Nine years on we have now three centres delivering this model.

We've treated over 30,000 patients and the idea with this clinic is a simple one, but very difficult to actually establish and then to maintain and to grow. But in effect a patient can walk in off the street concerned about their skin cancer or skin cancer risk, have access to a really well-trained general practitioner, we call them a GP Plus because they've done more training in this area and we vouch for their skills and we work collaboratively with them, and on that day they have access to specialists if they need them and also access to great facilities for the treatment of that particular cancer. So you ask how it's done. It's done through I think a little bit of support and funding. I think that's crucial.

It's done through making other people within various aspects of the healthcare system believe in the model and the vision and then the hard work is actually persuading people to be part of it. So, yeah, that's I guess a short - a relatively short answer - I know professors tend to be very verbose, but a relatively short answer in terms of how we established one of now many models that we're delivering. TEGAN: That's so interesting because I think from a patient - like the petty jealousies, or the - I won't say "petty". The jealousies between medical specialists seem a little bit inside baseball when you're the patient being like "I've got a melanoma", like "help me."

ANAND: Yes. TEGAN: The patient benefit seems very clear. ANAND: Well, that's the one thing - that's the one secret - well, not so secret - weapon that I have up my sleeve because I think if we are truly going to build the healthcare system of the future and if we are truly going to be true to our words that it is a patient-centred model, that patients are empowered, that they're given true information that's transparent and actually for their benefit rather than for the system's benefit or for the practice's benefit or for the specialist's benefit, well, that's the sort of mindset that we need to develop and that's not an easy shift from a system which is naturally pitted against each other where there is competition for work, where there are commercial drivers here that are pushing perhaps healthcare in directions that are perhaps not in the best interest of the patient. TEGAN: So you obviously see this as a model that could be applied across different disciplines or different diseases, I suppose. ANAND: Mmm, absolutely.

TEGAN: Skin cancer, plastic surgery, dermatology, those things cluster together quite nicely, but how do you decide who's part of one of these hubs when patients' needs can vary so much? ANAND: The real struggle with some of these other models is funding. So whilst Medicare was designed with the best of intentions, it's very much a transactional system. By that I mean if I was the doctor, for example, and I was treating you as a patient, what I get paid will be dependent on what particular intervention, what item number and what packet of funding I could unlock and that has repercussions across the health system. Why is it, for example, someone like me, a procedural specialist, is paid huge amounts of money to deal with a crisis problem when in fact it's the GP right at the beginning of this problem a decade ago that actually should be incentivised to solve this before it becomes a crisis? So it's kind of - Medicare has kind of introduced a funding-based system that favours urgent hospital crisis management rather than actually what we should be doing, which is getting people early before they become, you know, very sick and unwell. The funding for some of these chronic models falls short because Medicare doesn't cover allied health intervention and so what I've learnt over the last five years developing some of these models is that a lot of chronic disease management really requires input from not just doctors but other healthcare professionals and that is difficult because we can't access that in the same sort of level as we can, say, for medical intervention. Our chronic wound clinic, for example, has been running for some time and essentially we can't fund it because nurses - the nurse specialists that deal with a lot of these wounds, with dressings and pick up problems and help us to heal these wounds simply are not funded, so we need to find the money from somewhere.

And so part of these models have taught me that yes, it's lovely to have collaboration, it's lovely to find like-minded individuals within the healthcare system, it's lovely to find administrators and universities that actually believe in their heart that they want to do this. It's not so easy to fight some of the, you know, established thinking and also to be a disrupter to some extent. But what's even more difficult now is the challenge of how do we sustain these models to provide, you know, ongoing care, value for money and tackle these difficult problems but actually sustain the funding and keep them going. TEGAN: Right. So educating students from when they're students sort of throughout their career is obviously one of the steps along the way, but it seems to me that a much bigger problem or challenge is just the whole healthcare system as a whole. Do you see a need to completely redesign Australia's healthcare system and funding? ANAND: Oh, gosh - well, no, no, I think - look, I've worked overseas, I've worked in other healthcare systems through my career.

I think we have - we should really - we should be quite proud of the system that we have. You know, it's a good system, you know, and time and time again, you know, when I've compared say access to treatments for people in other countries, I think Australia has done remarkably well. We are essentially a wealthy country, we're an educated country and we have amazing resources.

So I don't think we need to design a new system. We just need to try to get this system to work a bit better to extend the value of, you know, the Medicare dollar to build these models to ensure that every dollar spent on health care is spent efficiently and in an integrated way that allows patients to access the right treatment at the shortest possible time route at the most affordable price. TEGAN: And just finally, can we talk a bit about another field of work that you're in, which is wound care, what you're looking at and what you're hoping to achieve there.

ANAND: Well, this is a flow-on from the integrated care work that we did. So shortly after we established the skin cancer model, we looked at an integrated wound - chronic wound model. Humans are unique in some ways. So animals, you can injure them and they generally will heal.

Humans for some reason as we get older - perhaps it's the fact that we live longer, perhaps lifestyle issues, but there is difficulty with healing wounds and if a wound doesn't heal by itself, let's say in six weeks, it becomes, by definition, a chronic wound. This is a hidden problem and, you know, the focus currently on aged care and the standards of aged care, you know, have been called into question. There is a hidden sea of these chronic wounds sitting in aged care facilities right around Australia. It may not be anything that grabs your attention, but it is a drain on the system, a drain on the patients, and so as part of my research interests, actually my clinical interest, I've always been interested in why these wounds, why is it humans develop chronic wounds and the answer is probably a mixture of getting old - our ability to repair and regenerate degrades over time - chronic disease once again like diabetes, smoking, vascular disease, so the things we do to our bodies is not great, and then infection is another really big problem because once you've got an open wound bacteria colonise on the surface and the body can't get rid of them. So there's been a lot of development once again in technologies and wound healing and I've been involved in some of the research on that, but how do you then deliver those benefits to these hidden patients? How do you find them in fact? So we set up our chronic wound care model based in the Sutherland Shire in Sydney - once again, you start small and develop and optimise the model and then hope to scale - and the model essentially sought to do two things.

It sought to get into some of these aged care facilities and we've used telehealth for this, so that's been a remarkable rapid uptake of telehealth and we've seen the value of it and it's not - I wouldn't say it's the answer to everything, but it's allowed us to sort of lift the cover, so to speak, and actually deal with some of these patients but more importantly the staff looking after them and being involved now and peering into some of these aged care facilities, the variation in staffing, their knowledge, even their level of English sometimes, you can sort of see how these chronic wounds are left to fester and kind of ignored to the point where they then end up in hospital acutely and, you know, thousands and thousands of dollars and time are spent to fix these wounds. So identifying them, getting into these homes using technology and then educating these staff to recognise signs that a patient is either at risk of developing a chronic wound or has a chronic wound was the first step. The second was to build an infrastructure so that we could diagnose what the problem was and so we built a bricks and mortar clinic. We armed it once again with our GP Plus, who I can say is an absolute find. She's so dedicated and has actually now started to spread her knowledge and educate some of the GP registrars that come through our training centre and of course she's then matched with nurses who are committed to the care of chronic wounds. So the model has involved education and empowerment, identification, building the infrastructure to deal with some of these wounds.

We've diagnosed wounds that have had foreign bodies in them for two years and then pulled this stuff out and the wound heals or we've picked up vascular disease, for example, that's been missed. Some of these chronic wounds are actually skin cancers, so can you believe that? They've been dressed for months and, oh, maybe this is a skin cancer, so simple things like that where you can literally solve the problem and get the wound healed. The wound model is now ready to scale, so that's really exciting. So we're now starting to talk actually to NSW Health and we're starting to work with other districts that might want to try to replicate this model. The challenge always is to find GPs that are interested and obviously the specialists that then help the clinic are vascular surgeons, plastic surgeons, endocrinologists to treat diabetes, aged care.

So the links are very strong in the Sutherland Shire. The challenge is can we develop these links and find these individuals in other parts of the state. TEGAN: Okay, so there's a lot of stuff that you're working on, Anand, but if you could only pick one thing to focus on in the future what would it be? ANAND: Well, I think if I was to pick one thing, I'd love to see the system move away from acute crisis management and more into prevention. You know, it doesn't make sense to me that we wait for the wheels to fall off and then we spend a lot of money. And so I think - it's challenging, but I think, you know, these integrated care models, the change in the culture - you know, ultimately influencing the policy and the decision makers and the fundees of health care, that would be what I'd like to see.

So rather than wait for the patient to present to the emergency department on a Friday night, the patient's picked up 10 years before, interventions are put in place such that the patient never has to be seen in hospital. That's what I'd like to do. TEGAN: You've still got to deal with those crises when they come up, though. How do you balance the two? ANAND: So I think it's a slow process.

So as we get more towards prevention, the hope is that the crises slowly come down. So it's like a slow balancing act. TEGAN: Again, ambitious but I like it. ANAND: Yes. TEGAN: Anand Deva, thank you so much for joining us.

ANAND: My pleasure. TEGAN: Well, if tonight's speakers have told us anything, it's that the future is bright. We're making strides in technology, integrated medicine, genomics and more. But what do we need to do to ensure the future is bright for everyone? Equity in access to health care is an issue that isn't going to go away by itself, so what measures can we put into place now that will ensure advances are going to be distributed in an equitable way? Scientia Professor Anushka Patel is Professor of Medicine at UNSW Sydney and a practising cardiologist. As the Vice Principal Director and Chief Scientist of the George Institute for Global Health, she has a keen focus on making health care both affordable and effective.

Anushka, welcome. ANUSHKA PATEL: Thank you. TEGAN: Who's most at risk of being left behind when we're sort of looking at this future of medicine? ANUSHKA: So I think if we look at Australia, for example, you know, we sit in the bottom half of the OECD rankings in terms of health equity when looked at, for example, the ratio of life expectancy among those of us who are least educated compared to those who are most educated. So there are some major disparities within the population and of course we all know about the major gaps in life expectancy between Indigenous and non-Indigenous Australians. So there's quite a few gaps.

There's many social drivers of those gaps that sit outside the healthcare system and outside of medicine and those of course need to be addressed, but really I think transformation about how we deliver health care is another approach that really needs to address these inequities. TEGAN: I mean, we have Medicare, we have social health care in Australia that other developed countries like, for example, the US doesn't have. I think a lot of Australians probably think of us as being like pretty good. Where are the gaps? ANUSHKA: So I think we are pretty good, Tegan. I think we've got a health system that we can be really proud of.

But the health system we have today has really been developed and established for problems of the past and most of the health inequities that we see today are problems of the present and of the future. So perhaps I can give you a couple of examples. So I graduated from medicine a little over 30 years ago and since that time the mortality rates, the age-adjusted mortality rates from cardiovascular diseases, which is an area I work in, have dropped dramatically. The death rates have reduced by 50% and that's in no small part due to major advances in public health measures such as reductions in smoking levels, but it's also been introductions of major innovations, new drugs, new devices, new therapeutic approaches such that a person who, for example, presents to hospital today has a much better chance of surviving from a heart attack and surviving in a healthy state than, say, 30 years ago.

But those improvements have been inequitable. They're not fairly shared across society, so there are many segments of society where smoking rates remain high and where outcomes from a hospitalisation from an acute event don't do as well. But because of the improvements in general, what we're also faced with is an ageing population. So in the year 2000 about 12% of Australians were aged 65 years or above. Now it's more than 16% and that's increasing by about a percent per year.

We've also got the major problem of chronic disease multi morbidity and that's where an individual might have more than one chronic disease. So they might have heart disease, they might also have diabetes, chronic kidney disease, mental health issues such as depression and anxiety. About 1 in 5 adult Australians have that but if you're over the age of 65 it's about 50%.

It's a major problem and again it's inequitably distributed in the population. So those who sit in the sort of bottom quintile of socioeconomic status have higher levels of multi morbidity. And our health system is really not equipped to deal with an ageing population and the growing problem of multi morbidity. It really needs transformation into the future.

TEGAN: What does that transformation look like? ANUSHKA: Well, I think it could look like a number of things but, you know, perhaps what I'd like to do is focus on three principles around changes that might not only result in better equity, but also a concept that's gaining a lot of credence around prevention that is called sometimes the three Ps, so it's around innovations or changes to medicine or health care that not only prevent disease but also at the same time help promote equity and protect the planet, which is another dimension I'd like to talk about. So the first of that is moving the health system, of transforming the health system towards patient-centred health care with a much greater focus on prevention than cure. What I mean by patient-centred care is that for each person we're delivering the right care at the right time and the right place with a really strong emphasis on shared decision taking and it's care that's fundamentally customised, so it's individualised for a person. It's collaborative between healthcare providers and patients and it's coordinated between healthcare providers, particularly in the context of multi morbidity. It's also accessible to meet the needs of the patient, not really the convenience of systems and processes that our traditional bricks and mortar health systems are used to. And these aren't new concepts, Tegan, but they are happening very incrementally when they need to be transformative.

But I think, you know, for that transformation to patient-centred care to happen, it will require really major shifts in vision, values, you know, leadership, drivers of quality improvement, which include funding models but also new workforce strategies. But I have also no doubt that maybe some of the other innovations we've talked about today around data and technology are going to be critical enablers for any transformation, particularly transformation that's going to promote equity. TEGAN: So you mentioned personalised care and I often think of something that's bespoke as being like a premium product, but you're talking about equity at the same time and targeting this to the people who are most in need of it, which, as you say, that lowest fifth of the population in terms of socioeconomic status. How do you do both of those things at the same time? ANUSHKA: So that's a good question.

I think, you know, precision medicine or personalised medicine, which is similar related concepts - the goal is to really identify the optimal care for an individual, you know, based on their unique profile rather than that of the average population and, you know, we do have tools to do that for chronic diseases, an area that I work with, but they're relatively crude and I think the power of precision medicine in data has a big role to play even in chronic diseases. Where I think you can bring in the dimension of equity is to talk about a concept of precision public health, which is a little bit controversial, but a way to describe precision public health is around delivering the right public health measure to the right public, population at the right time, so using again data, often big data, to identify those populations that potentially have the greatest burden of disease but also signal detection to identify those communities that are at risk of developing future burden of disease and targeting public health measures to those populations. It's an approach that's been used using not only health data but non-health data, so for example looking at how people purchase food, what types of food they're purchasing in their areas, what's the neighbourhood walkability like, what's the safety, what's the ability to exercise.

Those sorts of aspects can be incorporated into a precision public health approach. TEGAN: I don't think the public has ever been as aware of public health in the past as they have been this past 18 months. We've seen public health interventions in practice. Do you think that that's something that's going to help with buy-in from the public? ANUSHKA: Yes, I think it is. I think COVID-19 has given us great examples of where you can have fantastic innovations in medicine, like the development of vaccines in absolute record time that none of us expected, but then we've fallen over in some countries, perhaps you can say in most countries, including in Australia, on the public health aspects and it shows us that these two areas - you know, technological advances in medicine and improving public health - are not two sides of the coin, they're intricately linked and they need to be complementary to really deliver health outcomes equitably across the population. TEGAN: So you've talked about your work as being around designing around patients, not systems.

What do you mean by that? ANUSHKA: So maybe I could give you some examples of what I mean by that. TEGAN: We love examples. ANUSHKA: So, you know, I don't see patients like this every day, but it would be not unusual for me to see a patient, maybe a 60-year-old woman who is retired.

She's 60, so her average life expectancy having got this far in life is probably another 20 to 25 years. She's had a heart valve replacement maybe 10 years ago. She has a history of longstanding high blood pressure and diabetes. As a result of the diabetes she's got chronic kidney disease. So she sees me regularly as a cardiologist.

She also sees a nephrologist, a kidney specialist. She sees an endocrinologist to manage her diabetes. She's developed early Parkinson's disease, so she s

2021-09-04 03:05

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