The Munk Debates Podcast - Be it resolved: The future of mental health is big data

Episode Date: August 24, 2021

A Facebook algorithm that tracks posts for suicidal thoughts; an app that monitors the speed of keyboard strokes for signs of depression; a computer program that analyzes our facial expressions and tone of voice when we FaceTime. These are a few of the thousands of algorithms tracking our mental health that some experts say could revolutionize how we diagnose and treat mental illness. They say that our 24/7 use of digital devices is generating a goldmine of information about our mental state that must be accessible to mental health practitioners if psychiatric medicine is to operate like a scientific discipline in the 21st century. Instagram posts, text logs, Google searches, and GPS data, and not psychiatrists' observations and intuitions based on conversation, offer the detail and time stamped precision we need to generate tailored and effective treatments to the millions of individuals who desperately need help in the post pandemic world. Critics say the problems with this big data approach go far beyond the obvious privacy issues that come with outsourcing mental health monitoring to digital monopolies like Google and Apple. The push for mental health algorithms reflects a reductive view of human emotions that undermines the strengths of the traditionally human centred field of psychiatric medicine. Diagnoses based on dialogue between two individuals and grounded in intuition and empathy will always be better than machine intelligence at drawing out the personal histories that explain trauma and generate helpful treatment. Engaging machines to address the mental health crisis is nothing but a quick fix solution that only helps the deeply underresourced health systems of our world today. Arguing for the motion is Daniel Barron, Medical Director of the Interventional Pain Psychiatry Program at Brigham and Women's Hospital. He is on the faculty at Harvard Medical School and the author of the recently published book Reading Our Minds: The Rise of Big-Data Psychiatry. Arguing against the motion is Gerhard Gründer, Professor of Psychiatry and Head of the Department for Molecular Neuroimaging at the Central Institute of Mental Health in Mannheim, Germany. He is the author of How Do We Want to Live? Sources: CBSDFW, CBS Boston, The Doctors Company The host of the Munk Debates is Rudyard Griffiths - @rudyardg.   Tweet your comments about this episode to @munkdebate or comment on our Facebook page https://www.facebook.com/munkdebates/ To sign up for a weekly email reminder for this podcast, send an email to [e-mail].   To support civil and substantive debate on the big questions of the day, consider becoming a Munk Member at https://munkdebates.com/membership Members receive access to our 10+ year library of great debates in HD video, a free Munk Debates book, newsletter and ticketing privileges at our live events. This podcast is a project of the Munk Debates, a Canadian charitable organization dedicated to fostering civil and substantive public dialogue - https://munkdebates.com/ The Munk Debates podcast is produced by Antica, Canada's largest private audio production company - https://www.anticaproductions.com/   Executive Producer: Stuart Coxe, CEO Antica Productions Senior Producer: Christina Campbell Editor: Kieran Lynch Producer: Nicole Edwards Associate Producer: Abhi RahejaBecome a Munk Donor ($50 annually) to get 72-hour advanced access to the full length editions of Friday Focus and Munk Dialogues. Go to www.munkdebates.com to sign up. Hosted on Acast. See acast.com/privacy for more information.

There are options, and that's why we need to take this opportunity seriously. How many you can prevent global warming unless China is part of the solution? This is not normal male behavior. This is predatory behavior. We don't know how bad this bug is. We don't know what this bug does. All of that was thrown away in those eight minutes and 46 seconds, and that's the moment that I became an abolitionist. Extraordinary claims require extraordinary evidence. Welcome to the Monk Debates. On every episode, we provide you with a. civil and substantive debate on the big issue of the day to arm you, the listener, with

enough information to make up your own mind. Today's debate, be it resolved. The future of mental health is big data. Facebook announced it will start using artificial intelligence to help identify when someone is expressing thoughts about suicide on Facebook. That's where Bewee comes in. It's a new smartphone app and collects millions of data points about a patient's behavior. tracking their movements, phone calls, texts. What we have are exciting fronts of AI. There's so many metrics that, objectively, can help at a real-time basis, tap into a person's state of mind.

Hello, I'm your moderator, Rudyard-Griffith. Well, those are just a few of the growing number of algorithms tracking our mental health that some experts say could revolutionize the field of psychiatry and bring quicker and more effective diagnoses to millions. of people in need of help. Instagram posts, text logs, Google searches, activity trackers will all supposedly usher in a new era for psychology where clinicians will be freed from patients' faulty memories and maybe even more importantly from their own inevitable human limitations and biases.

Critics say the problems with this big data approach go far beyond the obvious privacy issues that could come with outsourcing mental health monitoring to digital oligopolis like Google and Apple. The push for mental health algorithms reflects a reductive view of human emotions, and it has the potential to fatally undermine the traditional human-centered field of psychiatric medicine. Diagnoses based on dialogue between two individuals, grounded in intuition and empathy, will always be better than machine intelligences in terms of, of drawing out personal histories, explaining trauma, and generating helpful treatments. On this installment of the Monk Debates, we challenge the essence of these arguments by debating

the motion, be it resolved, the future of mental health is big data. Arguing for the motion is Daniel Barron, medical director of the interventional pain psychiatry program at Brigham Women's Hospital. He's also on the faculty of Harvard's medical school and is the author of the recent bestseller, Reading Our Minds, The Rise of Big Data Psychiatry. Arguing against the motion is Gerhard Grunder, Professor of Psychiatry and Head of the Department of Molecular Neuroimaging at the Central Institute of Mental Health in Mannheim, Germany. He's the author of his own acclaimed book, How Do We Want to Live?

Daniel Gerhard, welcome to the Monk Debates. Thank you for having us. It's great to be here. Well, thank you both. This is a really interesting topic that I think challenges us to think through two different key features that have shaped much of our experience collectively and individually of this pandemic. And that is technology and mental health. So the opportunity to kind of explore the intersection of these two kind of important social trends and things. and features in our day-to-day lives is really a privilege and an opportunity indeed.

Our resolution is simple to the point today. It's be it resolved. The future of mental health is big data. Daniel, you're arguing in favor of the motion. I'm going to put a couple minutes on the proverbial show clock and turn the program over to you. Thanks, Fred Yard. I'm pleased to affirm the resolution because I believe that not only the future, but also the past and present of psychiatry is big data.

So to give us some perspective, it's worth noting that for millennia, clinicians like myself and Gerhard, have sat with patients, ask them questions, and by carefully observing how and what they say, we've gathered data about what's wrong. In fact, the very first recorded medical texts

back in ancient Egypt, the medical papyrite, they described procedures for eliciting and organizing clinical data and then deciding how to act to benefit the patient. This is technically an exercise in big data. The difference between our assessments today and say what King Tut's family physician Ben to would have done in 1300 BC is simply technology. Physicians today have better instruments and give or take 3,500 years of medical knowledge

at our disposal. And especially in the last 100 years, technology and big data approaches in health care have led to this blossoming in the medical sciences and it's allowed clinicians to detect things that are otherwise invisible. For example, we use electrocardiograms to measure and trace the flow of electricity through the heart's muscle. We use molecular sensors to detect oncogenes that can, you know, tell us which treatments may be benefited in psychiatry. We don't spend nearly as much time measuring as other disciplines. And I make no mistake, the problem isn't that I, you speak with and listen to my patients. Every doctor of every specialty does that. Rather, the only

instrument that I use to gather data that I think is clinically relevant is my brain. I don't measure the behaviors I think are important to my treatment of psychiatric disease. And yet, it's likely you have one of the most sophisticated behavioral measurement tools ever designed in your pocket or maybe even in your hand. Technology like the smartphone or wrist-worn smart watches would allow us to, instead of simply asking a patient how they're sleeping, to measure the duration and quality of that sleep, or instead of simply listening to someone's description of their mood, I could analyze a patient's facial expression, their voice, the way their ideas flow onto another, things that my brain simply can't detect, types of signals that are too subtle

for even a trained observer to attend to and to make use of. And so the tools that we're going to discuss today are still in the research stage, and without question, there are valid and very reasonable concerns about data security and privacy. Concerns which I should add apply to any clinical tool that is used today. And yet every other field of medicine is successful. added bigger and better data to improve clinical care. And so really the question is big data is the present and future of medicine. So why shouldn't it also be the future of psychiatry? Thank you, Daniel. Terrific opening statement sending out the key arguments in favor of our motion today, be it resolved. The future of mental health is big data. Gerhard, you're arguing

against the motion. Let's have your opening statement. Yeah, thank you. Big data is the latest big promise of salvation in psychiatry. Here we are promised to be able to diagnose mental illnesses earlier and better and then also to treat them more successfully. If only we have collected enough data about the individual, the human being reduced to his brain is understood here as a biomachine that can be simulated by an artificial intelligence and whose behavior can then consequently also be predicted exactly. According to this understanding, the brain is a computer, although a less powerful one. This is a radically reductionist view of man, which assumes that all feelings, thoughts and emotions in man are just epiphenomenal of brain function. Accordingly,

we only need to intervene in the brain to get rid of psychiatric illness. This is a dystopia similar to Aldous Huxley's brave new world. The risk for mental illness increases with poverty and socioeconomic inequality. If we are to improve global mental health, we must improve human living conditions. We achieve this by seeing ourselves as active designers of our world and not as passive biomachines with input and output. So, we have to make a decision. Do we want to find predictors of suicidality

to decrease suicide rates by collecting and interpreting large amounts of data? Or do we want to improve the living conditions on this planet to prevent mental states that lead to suicidality? The beneficiaries of the first approach are a few startups who program apps and get really rich with it. The second approach benefits all people on this planet and thus all of humanity. Thank you. Thank you, Gerhard. Now an opportunity for rebuttals.

This is a chance for both of you to reflect on what you've just heard in terms of a contrary argument. So, Daniel, you're up first. let's get your rebuttal to Gerhardt's opening statement. Thank you. I appreciate the arguments set forth by Gerhard. However, one of the things that I struggle with as a clinician is defining what actually are those mental states, what actually are the conditions that we're treating. And so in the absence of information, in the absence of the ability to model and define clearly

what it is that we're talking about, meaning can we define what exactly depression might be, or what exactly schizophrenia might be? Can we design tools that will allow us to better approach them with rigor? I'm not sure that we're going to make a lot of progress. The whole goal of medical science is to take something that is rather confusing and rather poorly defined and to constrain the problem in such a way that we can begin to understand it and test it. The whole medical science enterprise is based on the concept of reducing complexity by gathering, data and thereby understanding what we can do as clinicians, as scientists, as a society, to produce an outcome which everyone agrees is best for everyone involved.

And so the reason why big data has been so successful in the medical sciences is clinicians individually, unassisted by instruments, are not very good at defining clinical problems. For example, instead of saying a patient has a rapid pulse and chest pain, we now use an array of very sophisticated technologies to really dig in to measure that flow of electricity through the heart to measure the biomarkers which are present in the blood to measure the flow of blood even in the heart itself to better define the clinical problem and thereby be able to intervene in a clinically meaningful way thank you let's now Gerhardt have your rebuttal to Daniel's opening statement or what you've just

heard now your view is based on the assumption that psychiatric disorders are just brain disorders, that we are just talking about disorders of individual brains. For me, psychiatry is much, much more than just dysfunction of individual brains. Psychiatry is about social interaction, about community, how the social environment and the living conditions influence. the individual, it's about causation, causation in living systems. Your view assumes that we are in principle determined by the function of our human brains. My view is more based on, or it adds to this view, to this reductionist view, the assumption

that there is downward causation. That means we as a society or as a community or a gathering of individuals form a new system of complexity which can exert effects on the individual. And these factors are completely ignored in big data psychiatry. And in addition, as I said, how we. practice science is really an important factor in how we create worldviews. And our worldview is determining how we deal with problems. And if we invest all the money, all our research money, in brain research, we lose a lot of opportunities to improve.

improve global mental health by just improving the condition, the living condition of society. Thank you, Gerhard. Let me join the debate now and bring some questions that our listeners might have, listening to these terrific opening statements, really a debate that has both practical considerations, but also as Gerhard has just raised some possibly bigger moral and philosophical issues for us to reflect on. And maybe Daniel, to come to you first, for many people that have experienced psychiatry, there's probably a sense that the process is as important as whatever empirical insights, if in fact any really can be generated from the relationship between a therapist and a patient. So I'd like to hear a bit more from you about how big data is,

more than just bringing a kind of impuricism to the therapeutic dialogue or conversation and how you think it could actually help and rich provide a greater meaning to that very difficult aspect of therapy to qualify and quantify, which is the conversation, the dialogue in and around the self that occurs between a therapist and, and their patient. Yeah, thank you for the opportunity to kind of dig in a little deeper here. And the way I would address this is by saying the conversation between a patient and a clinician, I don't imagine that will change very much. So if we look at how other fields of medicine have adopted data into their clinical practice, those clinicians are still very compassionate. They

still have what we call a therapeutic alliance with their patients. However, what data does is it anchored our assessments in something that we can measure and trace. So for example, when I see a patient, I'm very much interested in their social interactions, in the community they live in, and their living conditions. And I'm interested also in providing services to help improve those facets of their life. What I'm suggesting is that we begin to measure those social interactions, be able to create data that can then guide our decisions better, that we begin to use tools that exist now to understand communities with more precision, to be able to detect problems that may not otherwise

be apparent. Living conditions can also be measured digitally even. I mean, smartwatches record ambient noise, like how much noise pollution is there in a person's life, things that have been associated with, you know, sleep conditions, with chronic illness. You know, by having data, by having the ability to approach a human's experience with clinical rigor, the clinician can then gain more knowledge and be able to implement treatment decisions in a more precise and effective way. And to Gerhard's point, my background is in brain imaging. I have a lot of experience processing functional MRI images to trying to understand the structure and function of the brain. And the reason why I'm interested and excited in these digital tools, it allows us to

approach behavior with the same sort of precision that I would use in a brain imaging scan, the same sort of precision that Gerhard uses when he processes pet images of brain function. And we can begin to be more precise in our communications and to be able to understand things with a greater level of precision than we would otherwise. Gerhard, let's have you come back on that because I sense, you know, in your contributions, a debate that you have a kind of concern. You characterize it as a kind of a reductionism that could be experienced by psychiatry writ large if in your view, it became overly reliant on big data. So I want to hear a little bit more of your critique in that regard. Of course, I completely agree. We have to get a better understanding of

what causes psychiatric diseases, what causes mental suffering. I completely agree. And that That is obvious, but let me give you an example. Two years ago, I attended at the AC&P Congress in Florida. I attended a presidential symposium on the opioid crisis in the US. The opioid crisis, of course, is very well aware to everybody in the US. it's basically the fact that the number of deaths following an opioid overdose increased substantially over the last 20 years. So that opioid crisis was addressed by six world-class scientists. And they all spoke about receptors, brain function, new drugs, how we can address pain,

Nobody in this world-class panel of six scientists talked about social causes for opiate crisis. Nobody talked about the role of the pharmaceutical industry. Nobody talked about the healthcare system in the United States. And that's a complete ignorance of important social factors. So I'm not saying that we don't need more data and we don't need a better understanding. of mental diseases, but we have to balance the things that we are doing. And especially in many Western countries, and I'm not just talking about the US, it's the same year in Germany, probably a bit less problematic, but the problem, the worldview is basically

the same. It's this reductionist view that just sees mental disorders as a result of dysfunction. in our brains and that's completely wrong. We have major determinants in our social environment. And secondly, talking about more data and better understanding of mental disorders, I oversee 30 years of psychiatric research and I can say that there was over these 30 years, we got a huge amount of data about brain function. And a huge amount of data for in so-called biological psychiatry. But we didn't reduce suicide rates.

We didn't reduce admittance rates to hospital. We didn't reduce poverty. We didn't reduce the living conditions for people with severe mental illnesses. And that all was admitted by Tom Insel when he left the role of director of the NIMH in 2015. That was admitted. He spent a lot of money, 20 billion US dollars on investigating brain function, getting data, big data, but he didn't improve the practice of day-to-day psychiatric care. Hi, Rudyard Griffiths here, your host and moderator. I have a favor to ask you, please consider becoming a monk member. Membership is free and you get access to a series of great

benefits, including a 10-plus-year library of some of our best debates, dialogues, and podcasts. You also get a free monthly newsletter featuring the debates that we're watching around the world, and you get a specially curated Friday weekly Monk Members Only podcast that focuses on the big international events and trends shaping our world. All of that, again, free at www. I hope you'll consider joining and becoming part of our community. Now, back to our program. You know, come back on this idea that, you know,

you can have all of this rich data, you know, spilling off our iPhones or wristwatches, but how that data would be used, who it would be accessible to, that there are some real issues of kind of equity and fairness here. Isn't there that, how do we deploy? deploy the insights from this data in ways that simply don't privilege the privileged. And in Gerhard's view, it's an expansive one, but don't kind of delay us or act as a panacea from actually addressing the underlying structural social factors that are driving mental illness in society. I think those are excellent questions. And things like social interactions, meaning how someone expresses

themselves online, activity, like movement based on GPS location, based on where someone's going, how active they are, things like living conditions or even the community, those are more of the social aspects of big data. So those are the types of data that can be gathered to describe more of the psychosocial part within the larger biocyco-social model of medicine. And so the question is, would this data be useful? And I think that Gerhard and I completely agree that those data about the social side and the psychological side of a patient's well-being are very much relevant. However, until we develop tools to measure and then trace what it is we think is relevant,

they won't be precisely addressed because there isn't a way to precisely address that. It wouldn't be a tractable problem unless you can begin to measure things. And in terms of equity and who has access to these data, I'm really comforted that almost everyone has a smartphone nowadays. The types of data that Gerhard and I are agreeing need to be incorporated into the biocycho social model,

we have access to gather those sorts of data already. And in fact, these data are already being gathered by many large tech companies who have access to see how someone communicates within their social group or larger community to understand what someone's living conditions are. And whereas right now, these data are being used to market us products. What I'm suggesting is that if these things may be relevant to a clinician, that's a testable question. Would it be relevant and would it improve outcomes? And for whom? And so the question of equity is, can we define models of care? Can we define ways of approaching these data in a rigorous and clinically useful way that can

benefit all patients and help us develop more carefully crafted treatments, which can then be deployed in a more effective and more equitable manner? Thank you, Daniel. So come back on that, Gerhard. I mean, what Daniel's saying here is that, you know, let's be practical. Let's understand the power of these tools, the fact that they are, at least in advanced societies like Germany, Canada, the United States, relatively ubiquitous and widely held. And that they can actually be an effective means to dig into these larger kind of sociopathologies that are the result of the issues that you care about, whether that's poverty, environmental degradation, these larger structural features that, you know, I think Daniel readily concedes are critical to both understanding and addressing mental illness in society today.

An app that traces my behavior would be helpful to, for example, detect when I have a relapse into depression or into mania. And that can be probably, I'm not sure about it, but probably in the future those apps can trace or they can prevent relapse or detect relapses early. And then one could intervene. But my approach is more, who benefits from such an app? It would be the developer of the app. I'm not sure about it. My approach is more, let's prevent that the patient gets suicidal. And so prevention is a much more powerful tool than detection of early relapse or suicidality.

So we shouldn't try to reduce suicide rates by gathering big data. We should try to reduce suicide rates by getting to the roots of suicidality. and that's in part at least the social environment of people. There is another aspect to big data. When we talk about, for example, early detection with medical devices, let's say an app or a biomarker profile, if Daniel, the patient comes to Daniel, Daniel collects a data, gets a biomarker profile.

He performs an MRI. He collects blood. He performs an EEG and all that. And then he states, well, you have an increased risk to get depressed over the next 12 months. What does that mean? The patient tells you that he's feeling fine. And so what do we do then? who interprets the data?

Is it the computer? Who makes the decision? What is what to do? Is it Daniel? Is it the computer who makes the diagnosis? And what is the diagnosis here in this system? Is there, where's the border between normal and a disease state? These are really very important question for,

ethical question for the medical system in the future. Big data psychiatry is promising. that we detect diseases before they emerge. But that means that we have to think about the consequences. Who tells the patient that he has an increased risk? At what point, at what probability? Let's say you have a probability, your computer tells you the patient has a probability

to have an 80% chance to get a relapse in depression over the next 12 month. Is that enough to tell the patient, you might have a negative influence on his further outcome over the next 12 month? At what cut off will you tell the patient? 80%, 90%. What's the probability at which you make the decision to intervene here? Is it 99%? 99% probability would mean that you are acting or that you are interacting, that you are interacting.

with a completely deterministic biomachine. That's all very important ethical questions of big data psychiatry. Dan, I want you to address the issue of how this data, because it goes part to what Gerhardt has just talked about, how this data is used and who it is used by. I think we see in society a growing concern around issues of not only the privacy of our data, But how governments can often have, in many cases, very compelling reasons to aggregate and access data in ways that governments feel address public health or other kind of issues or concerns. But on the part of an individual, that government response amplified and instigated by big data could be seen as a gross invasion of their privacy.

or a manipulation of their mental health. So I'd like to hear a little bit more from you, Daniel, about why maybe you're optimistic that these issues around privacy and the manipulations of publics by governments using big data is something that we should not fear when it comes to the application of big data to psychiatry.

In terms of whether we should fear it or not, I think that would depend on whether or not the data are useful to predict what we think it's predicting. And so having a conversation about this before these analyses are performed, before we can actually determine how useful these data are for predicting which things or even for describing which things is I think one of the reasons why we were both excited to be on this podcast. Because it is a very important conversation to have. At any point when we are collecting large amounts of data, which we believe may be helpful to predict something, whether a mental state or the outcome of a community or of a family, how those data are collected, how those data are secured and how those data are used and by whom and for how long, those are all very relevant questions, which I believe should be asked right now. And the way that we would approach that is just very practical methods that are used throughout medical science.

We can see a problem. For example, is this person's social community benefiting their health or is it not? So there's a problem. And then we could use different forms of data to define what we mean by that question. Are we defining their community based on their Facebook friends, based on the people, people they commonly call or they text and what is the content of the communications between those people like how can we see the flow of information between and then if we try to implement some sort of social therapy or community-based therapy can we detect in our data whether or not

what we believe is helpful actually is helpful and so all of these things require data and if we're going to consider problems, they have to be precisely defined so then we can evaluate them. And then if we're going to consider solutions to those problems, we also have to define the solution and then develop methods and tools to test whether what we're doing has the effect that we want. And so things like what Gerhard was suggesting, you know, what are the probabilities, when should we intervene, how much should we intervene? Those all are testable questions. Those are things that we can answer if we have access to data that we believe. may shed some light on those inquiries.

Gerhard, does that allay your concerns? No. Psychiatry is not just brain science or neuroscience or interpretation of data is not data science. It's more. My suggestion is psychiatry has to be political. And this whole issue is a very, very relevant political issue. It has to do with how we deal as a society. Let's, for example, look to China.

China has a very different, very different culture and a very different political system, as we all know. My understanding is that in China, the individual is just seen as part of a large community. and community is more important than the individual. And that's different from how we see ourselves in the Western societies. So if the Chinese government decides we have to decrease costs for the health system, they might decide, well, if Daniel can make a diagnosis very soon, with a 90% probability based on his big data, then the government might pick those individuals and put them into a hospital,

although they have no symptoms, no suffering at all. So it's really not just about a better understanding of mental health. It's about how we treat ourselves as a society and how we, in the future, how we want to deal with big data in the future. And this is also about tech companies, big tech companies. What's the role of these companies in the future? Who makes the diagnosis, the psychiatric diagnosis in the future? Will it be Daniel or me or will it be the computer at Microsoft or Alphabet or Apple?

These are all really very political questions. So we as psychiatrists, we, We have to have a very political view in the future when we are dealing with big data. Before we go to closing statements, Janet, do you want to come back on that point? Because it's an interesting one. You know, it's ultimately who decides. I mean, right now it seems we have a lot of predilection, a lot of tolerance in society towards pushing decisions over to algorithms.

And I think we're all starting to learn that many of those algorithms have inherent biases, some of which are quite disturbing based on issues of race, class, gender. How do we negotiate a future where there's this seemingly this potential to outsource the work of the psychiatrist to the anonymous, in some ways, unaccountable algorithm? I think it's helpful for the discussion to be more precise about what we're discussing. And so I am not suggesting in any way, shape, or form that we outsource mental health care to an algorithm in the cloud. I, first of all, am not convinced that that's possible. I'm also absolutely convinced that it's not possible right now.

And so what I'm suggesting is, I think, much more scaled back than this. idea that artificial intelligence or algorithms will replace psychiatrists. The only thing that I am suggesting is a very practical idea that I gather data that I think will be useful in my clinical decisions, that if I believe a specific data point, again, for example, how much someone's sleeping, how active someone is, that I not just ask about them in an offhanded manner, that I actually use instruments that are developed specifically to measure those points of data that I think will be helpful. And so what I'm suggesting is, again, that I am a psychiatrist. I will continue to see my patients, but I want to develop better instruments that can help guide my decisions, not make those decisions for me,

but instead improve my ability to detect and treat diseases in the same way any other medical specialty uses instruments to better detect disease and better treat disease. The question of neuroscience is something that Gearheart is brought up a few times. I think that this could very well involve brain imaging or neuroscience or blood tests for the simple reason that the medications I prescribe to patients act on receptors in the brain. And we know that different genes have different levels of those receptors. So if I'm going to prescribe a treatment that affects the brain and I might be able to better treat a patient if I understood the brain or the genes of the patient that I want to prescribe a treatment to, those data would be helpful for me. Of course, that is a theoretical helpful and that's

why people are researching this. But again, I would be the one making the decision. So I want to be able to inform myself to make improved decisions in the future with access to. to more precise, I guess you could say, bigger data. Thank you. This has been a fascinating debate, a lot of really interesting ethical and moral issues in addition to expanding my understanding of just how important big data could be to the future of psychiatry. We are up against the clock, though. So let's go to those closing statements.

Gerhard, you are up first. You've been arguing against our motion today, be it resolved, the future of mental health. is big data. Let's have your closing statement please. Yeah, thank you. This is, I believe, just the starting point of a very important discussion. I know I'm very glad that I could be part of this today. I think Daniel and I are not so far away from each other, but I think we also agree that there are lots of potential problems associated with big data in psychiatry. And I'm absolutely sure that as psychiatrists, we have to broaden our view beyond the individual to societies and political systems. And I think that's, that will be very important.

for our profession in the future. Thank you for having me here. Thank you. Well, thank you for your contributions, Gerhardt. Daniel, we're going to give you the last word in our debate today, be it resolved the future of mental health is big data. You've been arguing in favor of the debate proposition. Wrap up this conversation for us.

Thank you. I think overall the question that we've been addressing isn't whether big data is already being used in psychiatry, but instead which forms of data might be the most helpful. And so I have been promoting that if we believe that aspects of the biopsycho and social profile of a patient, meaning aspects of their biology, of their psychological health, and also of their social context are relevant, then we should use instruments designed to measure those things we believe that are important as rigorously as possible.

The reason why is clinical work is difficult. It's very complex. And as much as we can reduce that complexity by allowing precision, by taking precise measurements of what we believe is important, then the more likely it is that we will be able to not only understand what is going on in a patient's life in their biological, psychological, or social aspect of their life. But it's also more likely that we'll be able to intervene in a meaningful way. And here again, big data can be helpful as it has been in every other medical discipline. If we believe we have a treatment or if we believe we have an intervention that is helpful, we owe it to our patients and to society and to ourselves as clinicians to trace and test whether or not what we're doing is helpful. And that requires data.

That requires rigorous assessments of what we believe we're trying to. to do, that way we can test whether or not we're actually doing it. And to Gerhard's point, I think that there are many potential problems, which is exactly why we're so excited to be here and to be able to debate this and more discuss these potential problems. Questions of data security, questions of data privacy, questions of who owns data, who can act on data, who can, you know, share the data with whom. those are outstanding questions that can only be resolved by further discussion. And so hopefully this will be a starting point, not only to address and highlight some of these

concerns, but also to address and highlight some of the great potential that these tools have to improve the lives of our patients and also to help the interaction between clinicians for the medical field at large with the society at large. Thank you, Danielle. And thank you, Gerhardt. This debate has been been a bit of a departure for us. Normally, we focus on issues of economics, geopolitics, society, and culture. But from this pandemic, I think we all come away with a renewed understanding and importance of mental health, both individually and collectively, and the opportunity to, again, kind of synthesize these two big trends in our society, the accumulation of larger amounts of

data and the insights that that gives us about ourselves as individuals and as a society with, I think, an urgent and important discussion about the future of mental health and the origins of mental anguish in our society. It's just a critical conversation, and we've had it today thanks to your expert analysis and insights. So on behalf of the Monk Debates community, Gerhard, Daniel, thank you so much for coming on the program. It's been a pleasure. Thank you for having us. Yeah, very welcome. Thank you. Well, that wraps up today's debate. I want to thank our participants, Daniel, and Gerhard, they certainly give us a lot to think about.

If you have some feedback, reflections, any personal experiences that you want to share in the context of today's debate, please send us an email to podcast at monkdebates.com. That's n-unk, debates with an s.com. And a reminder that our members-only podcast is free for yours to listen to any time as a perk of our also free basic membership. You can grab both of them right now at www. com forward slash membership. To listen to more debates on everything from climate change to religion to geopolitics to the future of human progress, visit our website, triple w monkdebates.com.

You could also find videos of debates, our acclaimed among dialogue series, and so much more. Thank you for helping us bring back the art of public debate one conversation at a time. I'm your host and moderator, Rudyard Griffiths. The Monk Debates are produced by Antica Productions and supported by the Monk Foundation. Richard Griffiths and Christina Campbell are the producers. Api Rahaja is the associate producer. The Monk Debate podcast is mixed by Kieran Lynch. The president of Antica Productions is Stuart Cox.

Be sure to download and subscribe wherever you get your podcasts. And if you like us, feel free to give us a five-star rating. Thank you again for listening.