Greetings all, this is Jnana Bharati. I am pleased to meet you all with this hurriedly put together filler talk about data monetization or should I say creating value from data for in the research field we like to pretend that we don't care about money. But before I get into this let me acknowledge the country we acknowledge and celebrate the first Australians on whose traditional lands we meet and we pay respects to the elders past, present and emerging. Anyway, I will be speaking about data monetization in the context of data quality as well as other data management key principles upon which data monetization or value creation is inextricably linked. So what data monetization and how it applies to research sector is about how to derive value from data asset. So first of all to think about data as an asset and does it have the characteristics of an asset. So that is one question that often comes to mind and it has been argued by a number of economists that data and information possess characteristics of an asset. So as typically economists do and they have been attempting to manage this or our described models to manage this. So they talk about factors affecting data quality, the measurable ones on the left talk about accuracy, integrity, consistency, completeness, accessibility, precision, timeliness, etc. And the one on the right are little more intangible. They are relevance, usability, believability, clarity, objectivity and scarcity and so on. And so in valuing these assets like any other assets, they talk about the quality which describes completeness and accuracy, etc., etc. And the relevance bearing on its alignment with the objectives and processes, etc. And the third one about timeliness, how soon or at what time it is the information is available. So it's a timeliness. And then there are a number of cost in at various stages in terms of acquiring, processing and applying this in different contexts and the benefits, of course, can be gained by this. They argue in the performance and the gain or in decision making and so on. So in particularly, they propose six models of information valuation or data valuation. The one, the first one is the intrinsic value of information. And we will look at what each one is. And then the next one is a business value of information and also the performance value of information. All three are based on the characteristics of the data and the domain. But they also propose three other valuation models that are based on economics and finance. One is a cost value of information and the market value of information and the economic value of information. What each one particularly talks about in this case of intrinsic value, how good and easy to use this data versus how likely others might have. So what is an intrinsic value of this? So of course, for the data to have value, it should have some level of validity. So percentage of records that seem deemed correct needs to be complete. And so fewer missing values and so on. And I mean, fewer errors and scarcity and how rare the data is to find for it to have the monetary value as economics would put it. And then the life cycle, which is how long this data would actually be available. So I think this is probably the most important concept for especially to translate into research. But the formula proposed is validated times completeness times one minus scarcity times I mean, life cycle is it seems to at least I felt it seems to give a mixed results. I mean, or mixed interpretation, I would say the scarcity would increase a value. So I did slightly modify took the liberty to modify this as accuracy, completeness, accessibility, how easy the data to access the scarcity and the and the life cycle length or of these actually contribute in some way not necessarily as a product, but proportionally contribute to the intrinsic value. Then the business value on the other hand is all about how quickly something can be applied to the particular process. And there, of course, talking is still talking about the business relevance for business application. So it talks about relevance, validity, completeness, and timeliness. So at any given time, how soon you can get it to a point and be able to use it. And talk about performance value of information, which is how much does the data having a unit of information intrinsically contribute to moving closer towards target. So it's an incremental increase in moving towards a target. So it's a delta. And so in over a lifespan and how much it contributes. So it's a different improvement divided by or the fractional improvement against a control group of having a data available is what is what they actually propose as a performance value. Well, there are other values, financial measures, such as what cost value of information, which is based on what it would actually cost to replace the data, which includes capturing processing and etc. That can be attributable to this data. But as well as if this data were to be not available, what would be the opportunity cost or the loss? And then market value is how much someone is willing to pay for it as in in this context. And then to the finally, the economic value. So this is essentially what they're saying is it is like a performance value of information minus of course, all the cost involved with this. That is a cost of acquiring and processing and applying this data. So this I mean, that gives an overview of what the literature or what widely talked about in terms of value. And of course, these are pseudo equations, or you might want to not really I mean, take you want to take it with a pinch of salt. But but how do you actually apply? There are three ways you can apply this that or derive value from data. One, of course, the lowest most the option three, you can just simply sell the data. And option two is actually you can use this data to enrich the products and services by wrapping information around it. So by making it more relevant or making it more personalized and so on, as well as providing additional information. And then the first one is how could it actually be used directly in decision making, or in improving existing processes, etc. So three ways you could use it. But in all three case, it's what we are trying to do, especially this is what is relevant for research in the in the sense is we are raising this data to be more and more organized form to answer certain questions. So organized data becomes information. And the linked pieces of information becomes knowledge. And the knowledge when it is distilled knowledge, is becomes wisdom. So says, Rasa Lakoff, in his argument about the data hierarchy. And this is this is this particularly valuable to us, because as data professional, this is where we want to get to. How do we actually raise this data to to this wisdom? And there are different processes that we employ. I tried to summarize both artificial intelligence based processes as as well as extracting simple insights such as business intelligence processes into one nutshell, which is of course, the data, the tools and infrastructure, and the methods, and the frameworks come together. And through us. I mean, through a scientific process, and to be able to integrate and interpret.  So, so if we were to describe it in the pseudo equation, the data usage depends on quality, integrity, of course, as we recognize fairness, and the security and privacy, etc. And the process of raising data to the wisdom process or towards it. In the case of AI is all about minimizing the error, using data algorithms and compute. In the case of insight generation directly from the data, it's all about applying rules and compute on the data to come up with insights. This is I see as primary difference between the AI and the BI. And so in but the perform that is for just the model. And the performance depends not just on this first part. I mean, the equation one, but also an engineering compute and the processes that I involved. And then if you want to have impact, it performance alone is not sufficient. It has to be aligned to the actual problem. And there must be risk as well. A risk that might be because that should be controlled as well. The sustained performance should it should have impact, but also should have an organization around it, including culture, governance, etc. But to achieve this, we use a lifecycle process from so starting from problem formulation, to actually acquiring and processing the data, developing model. And that model at one stage becomes your proof of concept. After few iterations, it would become your product that you actually take it to productization. So this process often drives the lifecycle of the data. But we know that that the data quality itself has been a serious impediments, as both in the industry as well as in the academia, for for actually realizing the value. And billions have been estimated to contribute to that. But particularly the data quality, the opportunity to address data quality is is in the early stages in in the problem formulation, in the data processing part is where most of the data quality management occurs. But even but it is also, it also spans the entire lifecycle process. But what is even more important is what are the downstream implications of this. The value of of this data. And in this case, I want to just step away from just the monetary value, it can be any value of the data increases as you go through more and more processing. And but the ability to influence the data and ability to influence a value as well as the cost or the outcome decreases very rapidly over time. And so it is in the very early stages of the problem formulation and initial data acquisition and pre processing. Most of the opportunity for working with the data is lost. Or it is expanded. And beyond that, it is actually developing the model, whatever algorithmic is, and then mainly translation and adoption. Of course, there is some dependency of these in the in the data quality, but being able to influence would actually decrease over time. And but it is also it is important to know not just it decreases, but there is a serial dependency as well. That it is it is a fragile path to the value or the impact. There is a so the whole analytic life cycle can be thought of as number of a chain of linked life cycles, one, of course, information and data management, modeling and data science and deployment. So if any of this from end to end, including the problem formulation and and deriving value, if any of this is broken, the ability to extract value out of data is actually broke. And so you need to look at the whole life cycle. If while your ability to influence is higher in the earliest stages, the ability to but at the same time, the risk continue to remain the same. Meaning if any of this is broken, it will continue to it will actually destroy any value realization. And but the traditional view of research, as we see today, is is is primarily ending in the reports and papers and publication, primarily publications. I mean, of course, you define various measurements and constructs and gather data just to satisfy this. And, and they can you may address missing data and and so on. But areas such as why as much as the validity of the construct is is explored, the validity of the and the provenance of the data and the data quality is is hardly addressed. In fact, anything beyond being able to achieve the papers and the present achieve these publications, etc. are are seriously jeopardized. And so you can actually so you can fix a data quality issue at at the end by at at by adding some rules or doing some end processing. But actually, the value in it, but it can also be those can be very symptomatic. And the majority of the processing that is done at, let us say in pre processing in AI or machine learning, it's it's quite symptomatic. So we use statistics to manage what is missing data, or any if I try to identify errors, and and so on, or we cleanse the data, impute the data. So the data quality is handled at a very symptomatic level. But on the other hand, the root cause resolution, going back and collecting a better data, it's of course, incentives are often against us. And so it is seldom done. And but in reality, the fixes should occur throughout. And that that includes definitions, metadata, traceability, or data integration. So or inconsistent or erroneous lifecycle processes and management, and poor data migration, and lack of active maintenance, which is another factor, even if you had good data, and it's one of, and it is actually destroyed. In other words, the weight both in the industry, as well as in the academia, are towards the production side of it, then in the monitoring control and stepping back. But the data quality is one area, at least it along with governance and risk management does look at these. And so in the case of I mean, industry, it is all about delivery of and that in that in that case of academia, it's all about publication, these outweigh other, that is urgent, often outweigh the importance that having governance, having risk management processes, and the deep innovation in the industry can be quite lacking. And especially in Australian context, and similarly, translation in research is is again, is a serious issue. So both are struggling with different problems. And the data lifecycle itself is aiming and or the data quality that is conventionally measured, is looking at completeness, integrity, timeliness, validity, these are all that one considers in the case of data monetization or value creation. And along with also data curation, or improving the fairness, it is it's applied. And so both care and the fair principles that are coming into place will play a role or could play a supportive role. And in the case of the value creation, this is one definition if I mean, this is that actually looks at the whole lifecycle process. And it looks at the end to end treatment of the of the quality. So I wonder if Leslie is here, or I mean, I mean, there are authors of this paper are here as well. So if they're happy to comment, I'm most interested to listen. But this Peng Zetal's paper does actually capture the importance of data quality and encapsulate this in this in this bigger picture. So that's something I thought it's quite quite relevant.  Which is, for example, if you look at, even after you build a model, a good model, it's only about 20% of actually it's about 10%, 10 to 12% of the models that actually see the light of the day, they act, they end up getting deployed. And that is what the statistic says. And these these involve big figures. And it is a case even even with the industry. And there are many reasons. But the low success rate of deployment, of course, it tied to data quality, but it's also tied to number of other factors, including even if you were to deploy a model, the model shifts, or the data quality shifts. And over time, so the retraining and the maintenance, etc. are quite necessary. So the value realization would involve, of course, understanding the drift in the data, and identifying it in time, and then say retraining. So the model performance is retained. And so this means a continued monitoring is necessary. And so so in terms of beyond the algorithm, one also needs to look at issues such as the data related issues, model related issues, and the number of non technical issues, if you really want to see value realization. And if you also want to see the model getting used, they need to be trust in the model and the data. So the model must be able to should not be black box, it should be able to explain its finding. And so similarly, the data provenance, scope, quality, etc. comes into picture, but also a number of non quantitative factors, non technological socio technical factors also comes into being. So that is why it is so AI ML code, for example, in in a machine learning situation is only one small piece of this. And in reality, you have a number of other boxes to worry about even just from the technological perspective. So putting it all together. So we are looking at there are systems level challenges to the value creation, starting from the life cycle stages of linked life cycles. And oftentimes, lack of stakeholder participation and serial dependencies in the path and number of interactions, dependencies and points of failures in this. So the path to value creation or impact is is is a fragile path. And but at the same time, if you really look at majority of the price, if you ask the practitioners, which issues are important. And what I had found is it's kind of diverging usually the younger professionals, HDR students, or even early career researchers focus quite a bit on the technical aspects, such as say, the model development, the green for the greenbies. And if you ask more senior professionals, and some of them will concede to the one says get familiar with it, they will concede to data pre processing stages. But but it takes much more years of training and actual practice and failures to understand how much more value also lies in other areas, including the problem formulation, as well as production icing as well. So so this. So so there are a number of issues such as domain inefficiencies, peculiarities, missed opportunities, and so on that are already plaguing research sector, as well as number of, I mean, as well as the industry sector is not immune to that as well. They have been legal battles, and, and so on. But at the same time, and also it results in the poor decision making propagation of biases and organizational politics and broken window syndrome often happens in research labs. Okay, it's been this way. And let's keep going. And same in the industry. And could often result in mistrust and distrust of data. So I was going to give you some case studies, but in the interest of time, I'm just cutting it short. And, but it is important to look at solving the right problem. And it is important to have a hyper participation of the stakeholders, and not just both the technical stakeholders across a value chain, but also the customers and other stakeholders as well. And to understand the path to value is long and fragile. And also allocate appropriate resources without cannibalizing and understand the biases risk and ethical issues both in the data, as well as the processing pipeline, including machine learning or other modeling processes, and tech compatibility is also quite, quite important. So one needs to take a more systems view and the framework governance and managing management are important processes and are important meta data and meaning are quite important. And human factors need to be addressed. So so do risk ethics and fair and care ideas. So I was thinking what happens in slightly less data intensive area in the case of translation. So I was looking at some of the systematic reviews in this area. And surprisingly, the number of issues that have been highlighted in the case of translating research evidence is is quite interesting, but also worth learning about partly because there are very few studies in the startup profession itself about the translation and and so on. So they talk about time constraints, lack of workforce, etc. So and they talk about identifying the right stakeholders, budgeting, the right level of activities, motivation of professionals, and so on. And so things that are barriers versus that are facilitating have been nicely laid out. And similarly, what is micro level versus me so or systems level have also been highlighted. So I thought it is worth looking at some of the other domains and see how they have actually done some of these translation and and value creation. And there are a number of theories on models and theories models and frameworks in certain sectors, particularly in health care or clinical translation, that is worth learning about. So with that, I would actually stop here.


Hello, welcome to another edition of the Socialist.net video blog and joined today by Holger Martin who's on the steering committee of the Solidarity with the Anti-Fascist Resistance in Ukraine campaign. Holger, can you explain some of the latest developments in Ukraine after months of fighting between the Kiev government and the rebels in the east? We've now seen a ceasefire. Can you discuss these latest developments? Yes Well, 10 days ago there was a ceasefire was signed between the Donetsk and Luhansk republics and the Kiev authorities also with the participation of Russia and this deal, the terms of which understood in different ways by different people involved meant that fragile ceasefire is now in place. The immediate push for this ceasefire was the fact that the Kiev forces were being heavily defeated they had to retreat on all fronts and there was a threat even of the rebel forces taking over the key important port of Mariupol in the in the south coast. So in reality Kiev was forced to sign this deal otherwise they would have lost even more ground but there's a whole number of different reasons for this agreement and there's also a whole number of forces people who are not happy with this deal or who see it just a temporary stop-gap. First of all we have to say that after a number of months in which the so-called anti-terrorist operation of Kiev against the Donbas seemed to be advancing and the rebels seemed to have been surrounded in Donetsk and in Luhansk and they were about to be cut off from the Russian border the situation of the actual war or the the front the battle lines changed rapidly most of the western media and the western governments and the Ukrainian government they said that this was because of strong intervention on the part of direct intervention of Russian forces they were talking about thousand Russian soldiers having crossed the border then some other people talked about ten thousand Russian troops crossing the border then there was talk of a Russian tank colon being involved but for most of this there was very little proof I mean this way the same people were saying these things We're the same people who told us about ten years ago that there were weapons from us destruction in In Iraq Which then we found out it wasn't the case and there was a lot of hype at the time of the NATO summit in Wales To try to build up this idea that there was a Russian invasion of Ukraine and that something had to be done and but in reality behind the scenes NATO was clear that they couldn't do anything about it and there was no real appetite or Possibilities on the part of NATO to actually intervene directly and help Ukraine in such a way that they could finally smash the rebels because that will have meant Direct conflict with Russia, which they don't want they're not prepared for for it at the time when NATO is involved in So many different fronts with US imperialism, which is the driving force behind NATO Obviously has all these problems in Iraq Which are of their own creation, but they don't want to be fighting in too many different from the unable really of fighting many different fronts Having said that it is it is obviously clear that there was Russian involvement in Ukraine in in in the fighting and in my opinion this has been mainly taken the form of Allowing supplies across the border allowing Volunteers across the border supplying the rebels with intelligence and so on Any talk of an actual invasion is not It's completely out of the of the question and and they haven't actually provided any real proof for this I mean we live in a world where US military satellites monitor every movement that takes place in conflict zones and they haven't been able to produce any actual evidence for such an invasion In any case, I will say that the very powerful factor in the defeat Of Kiev forces on the front was also the fact that For the local population in the whole region of the Donbas. These are occupying forces these armed forces which have been waging a war against civilian population there's more and more evidence coming out of Shelling indiscriminate shelling of civilian areas supermarkets markets hospitals schools kindergarten's have been bombed by Kiev's forces without any Respectful for human life or non-combatants and so on about a Million people have been displaced million civilians have been displaced from the region most of them what 800,000 have been forced to fled to flee to Russia two or three thousand That is the official Ukrainian Ministry of Health figure for people having killed being killed in this conflict, but it's probably much higher So this is very difficult to win a war when you are basically considered as an occupying force rightly rightly so There's another factor and that is that the combat fighting morale of the Ukrainian forces is very very low throughout this period of the anti-terrorist so-called anti-terrorist operation. We have seen constant protest by wives and relatives of mothers of soldiers because these are after all Drafted troops people were going about the everyday business working here or there and they were Suddenly drafted into a war which they don't have any any real Desire to fight in in most cases not only this but these soldiers the Ukrainian army Not properly trained so they basically sent a scannon fodder to fight in the in the front They are not properly fed not properly given body armor or weaponry or anything Hundreds literally hundreds of these troops were at one point or another surrounded by rebel troops or Cornet against the Russian border some of them defected to Russia abandoned the front There's even a case of a military unit from live-off in the in the West. They basically said look we have no And we haven't any more Weapons who can't continue fighting we have no supplies And this is basically you got on a bus and went back home across the whole Country in a situation like this really there was a danger of the complete collapse of the Ukrainian army And the only ones doing the fighting where the volunteer forces The battalions which are composed of Nazis neo-fash neo-Nazis fascist paramilitary thugs being legalized and incorporated into state apparatus and Yes, they might have Fighting spirit idea. This is a battle. They want to fight against the foreign enemy or whatever. They want to see it But they are also not very good at doing the fighting and they were heavily defeated in in several Battles with with rebels so the situation was not looking very good for Kiev regardless of any Russian Direct involvement of Russian troops And they were being defeated so this is the reason why they finally reached this Deal, but there are as I said before a number of forces that are against this deal on the one hand The fascist elements on the Ukrainian side of this fight on the on the Kiev side of this fight They are not happy with this and even today they were protesting outside Parliament they tried to storm Parliament. There was a joint demonstration of Svoboda the far-right party, which is part of the woman The right sector and all the elements and they said they wanted to overthrow this woman They're not happy with the signing of the deal with the European Union They're not happy with the signing of a truce with the rebels and So-called amnesty for some of the rebel fighters. They're not happy with this because they actually Take to heart all this far-right nationalist propaganda patriotic propaganda and they want to put an end to this To this people they don't they don't consider them as Ukraine They consider them as foreign Asians mercenaries and so on so is In reality Kiev has unlist forces which are a bit dangerous even for themselves, you know, they are completely out of their control Also on the rebel side there have been Indications of a number of units and even military commanders who are not happy with this deal who's saying look I mean we fighting against fascism Fascism is not being defeated and our aim is not just to liberate the Donbas But basically to get rid of fascism all together in the whole of Ukraine and we're now advancing Why should we reach a deal? But obviously it is clear that and there's also elements of this anti-oligarchic mood which exists in Donbas, and they're not happy. They're saying well, I mean the oligarchs are still in government We should get rid of all of them and the people should have power. There's been many instances of different commanders of Rebel fighters making these sorts of comments, but obviously the Kremlin is not in favor of any Anti-oligarchic revolution in Ukraine or even if it's just in the Donbas because this will be very very dangerous Also for the forces that are in power right now in Russia putting is a Representative for the Russian oligarchy. They are not interested in an anti-oligarchic revolution in the in the Donbas They're supporting the rebels up to a certain Point they're using them as a bargaining chip to have a say in the future of Ukraine as a whole and to resist NATO Aggression, but they're not interested in going the full way. So this is more or less the situation that's been created As a result of this is fired today the Ukrainian parliament passed a law giving the Donbas special status in Within Ukraine, but it's not very clear what that exactly means There's also a limited amnesty for the rebel fighters, but also the terms of this and not very Thanks, you've discussed a bit there about the fascist elements involved on the Kiev side in terms of the army and the government as well Can you explain some of the evidence behind? The existence of these fascist elements. Yes, I mean this has been clear right from the beginning and in the In the solidarity with the anti fascist resistance in Ukraine website We have been publishing material about this right right from the very beginning of the of the campaign. It was clear Kiev the Kiev authorities that came to power after the overthrow of Yanukovych. They They didn't hide the fact they called for volunteers to join the newly created National Guard Battalions and the Ministry of the Interior Battalions And these volunteers mainly came from the paramilitary fascist organizations the right sector Zvovoda Patriots of Ukraine the Social Nationalist Assembly and all the far-right and Nazi Organizations if I go to the first volunteer battalions that was created under the authority of the Ministry of Internal Affairs I estate body was the as of battalion and the as of battalion was created and It's made up mainly of members of the Social National Assembly its paramilitary Organization Patriots of Ukraine these people are Nazis. They are openly Nazis and they don't hide the fact themselves And the commanders of this Unit are Nazis and living members of these fascist organizations But all of this was not very convenient for the Western media to show because this is a woman that's been supported by the Western powers by imperialist powers in In the US in Britain in Europe and so on so there was not there were not many reports in the Mainstream media about these things, but now in the last few weeks more and more Evidence has come out about this even in the mainstream media, but one of the latest ones is a video that was shown in German TV news in the German TV channel ZDF and It showed members of the as of battalion which are playing key role in the defense of Mariupol and they had Drone in the helmets Nazi symbols including as Vastika the SS symbol and This created a bit of a stare in Germany and in other countries obviously because this was clear a clear indication that Soldiers who were fighting under the authority of the Kiev government in this case the as of battalion of the Ministry of Interior of internal affairs Openly Nazis and when asked about this one of them said no, I'm not a Nazi I'm a national my ideology is national nationalism Obviously doesn't really make any difference or the others say no they're not national is they are They are patriots Yes, well the Nazis were patriots of one particular Kind and other people say no they they symbol Which is a Nazi symbol is not really a Nazi symbol It represents the end and the eye idea of the idea of the nation But if you then go and look at what the their idea of the nation is the view of the nation is a white supremacist racist Nazi Ideology and they do not hide the fact I mean the main commander of the assault battalion Andrej Bilecki He is a Nazi has been a Nazi for a long time. He spent time in in jail and you read his writings Even writings is produced now in August and he openly says what his idea of the nation is and Not only this but now the most Perhaps shocking thing But it's not it's not a new phenomena. It's just a development of what's been happening in the last few months, but Yatsenyuk, who's the prime minister and Turchinoff who's the president the speaker of the parliament have created a new party because there's elections coming up on 26th of October this new parties called the popular front and he was launched in Kiev few days ago, I think it was on the 12th of September and this party has created a military council because people in the army are not allowed to be members of the party So they created this kind of military council to allow people who are involved in the in the fighting to be part of the party and This military council is composed of the following people Arsen Avakov who's the minister of interior Andrej Parubi who's the former secretary of the National Defense and Security Council Who is a former founder of the Social Nationalist Party of of Ukraine Nazi organization? And then all the commanders of the different volunteer battalions who are all of them Nazis of one way of one description or another including Andrej Biletski, who's the commander of the main commander of the of the as of battalion. So this guy is now Heavily rumored that he's gonna be in the election lists for the popular front Which is the party of the current prime minister and current president of the of the parliament So this is a fusion between the Nazi elements and the reactionary bourgeois Elements who are now in in power. So I mean the Kiev authorities You can say it might have certain legitimacy there was a presidential election after after all, but I mean in reality this is is a very unhealthy mix of right-wing reactionaries neo-liberal bourgeois politicians extreme Nationalists and and some who are openly fascist and neo-nazi elements, I think that The idea behind this is since these people have been doing quite a lot of fighting and they have certain prestige out of that They will use them in their electoral list in order to get a better better election results And these are the people being supported by the by the West so And today as a reward for all this The European Union and the Ukrainian parliament have ratified and signed the European Union Ukraine Association treaty was the original reason why this whole movement against Yanukovych Started so it is quite a dangerous position. It is not just a situation where Nazi groups or Nazi gangs roaming the free the streets of Ukraine, but they also been incorporated into a state apparatus giving heavy heavy weaponry and incorporated into a whole political Consensus of bourgeois right-wing bourgeois politics at the same time Anyone who is in opposition is been persecuted On Friday night three Activists still I've been activists were arrested in Odessa One is a member of Borodba another members of the Communist Party and another member of the Communist youth and one of them This member of Borodba. He is one of the survivors of the Odessa massacre where he was beaten up by the right-wing thugs and They three of them have been arrested under the allegation that they are a cell of the so-called Ukrainian Red Army, and they are preparing a terrorist terrorist actions Now this Ukrainian Red Army has not carried out any actions and it's quite likely just a fabrication but in any case this Member of Borodba has been beaten up and tortured in in detention and his relatives are very worried about his safety and They they saying that explosive were planted in his flat when When the raid took place by the security agents and also that fascist elements were also part of this rate on his flat Not only this but this person Who's been arrested member of Borodba is also the editor of the May 2 Website which is a campaign that is trying to bring justice to the victims of the Odessa massacre and to guarantee that There's a full investigation of what happened at that time in it is in this context of Attacks on democratic rights the other day they raided The offices the editorial offices of this Newspaper Vesti, which has been in opposition to the war and they raided they took Journalist personal notes hard drives they seized the web server Under the accusation that they had published some articles which will constitute Incitation to the breakup of the territorial integrity of Ukraine So anyone who's really against the government or against the anti-terrorist operation is immediately branded the terrorist Foreign agent and action is taken against them. There's also as we know legal action being taken to ban the Communist Party who's The parliamentary faction of the Communist Party has already been disbanded and so on so this is the context in which these elections are taking place on the 26th of October Thank you You discussed a bit there about the solidarity with the anti-fascist resistance In Ukraine campaign Can you explain a bit more about some of the actions and Activities of the campaigns to doing here in Britain and internationally to support The anti-fascist resistance in Ukraine. Yeah, the campaign was set up in June and since then we've organized a number of pickets of the Ukrainian embassy and number of solidarity activities and Recently last week the British TUC the trade trade Union Congress, which organizes over six million workers in different in different unions past an emergency resolution moved by the RMT the transport Union which condemns NATO's NATO policy in Ukraine Which incidentally right now there are there are NATO military exercises taking place in Ukraine Which is hardly a contribution to reaching a peaceful deal to the to the conflict in Ukraine But this resolution, which is very important. I think it's very significant Condemns NATO policy in Ukraine opposes any involvement of British forces in the conflict and Also offers solidarity of the TUC for those fighting for trade union rights and against fascism in Ukraine this is very significant I think this is it's obviously the first time that a major trade union body in the west offers its Solidarity with those fighting fascism in in Ukraine and opposes the policy of imperialism of intervening in In Ukraine, I think this is very significant and we need to use this To maximum effect to build a campaign and to take the campaign down to the ranks of the British trade union movement Thank you. And finally, can you just discuss briefly what you think the perspectives are now for the Ukraine? It's very difficult to say because even the ceasefire is very fragile. They're still fighting going on Just a few days ago Ukrainian Army shelling killed about 25 people in Donetsk, even though there is a allegedly a ceasefire but in any case what I think is that this Ukrainian Gorman is not only carrying out this anti-terrorist operation, but it's also a woman that is committed to harsh austerity measures Which been signed in a deal in the best so-called bailout deal with the IMF The the Ukrainian economy is now in a much worse state than it was when this deal was signed is forecasted to Collapse by 7% perhaps this year GDP and this will mean that whatever government comes in Into power after the 20 October 26 elections will be committed to these policies massive austerity policies cuts in Massive layoffs of civil servants cuts in pensions and wages increases in the prices of the utilities heating gas fuel electricity and so on and this Can cause social explosion at one point or another do this Nationalist hysteria the fork of patriotism will dissipate and the real issues of bread and housing and heating will come to the fore and Hopefully the Ukrainian people will wake up to the to realize who the real enemies are the oligarchs who Where in power and the Yanukovych are in power now and they're using cynically this war using ordinary working people as canon father as well on both sides of this conflict to advance the interests and Continue the enrichment at the cost of the interest of ordinary working people in in Ukraine We can only hope that sooner than later There will be a genuine anti-oligarchic movement that will do away with this Handful of wealthy oligarchs and manipulating ordinary people in their interests. Thank you for joining us today. We'll be a good look at the campaign Thank you


Hi everyone, I'm Joe for jaspyscasebrakes.com. Happy Saturday coming at you with 2011 Top's five star football, three box random team break number one. One spot gets you two teams in this blast from the past.  Congrats again to the winners of spots right here.  Like Sir Mixalot, there are all 32 teams in right there. Let's roll it, let's randomize it. Six and a four, 10 times for names and teams. One, two, three, four, five, six, nine, and 10th and final time. Six and a four, 10 times. We've got Don down to Mike. Six and a four, 10 times for the teams. One, two, three, four, eight, nine, and 10th and final time. After 10, we've got the Pats down to the New York football giants. All right, Don with the Pats. Gordon with the Cardinals. Don with the Jags. Daniel, you have the Raiders. You'll get Oakland Raiders stuff too, obviously. Nicholas with the Titans. Tommy with the Dolphins. Daniel with the Bengals. Don with the Panthers. Blaine with the Los Angeles Rams, St. Louis Rams too. Nicholas with the Fly Eagles Fly. Don with the Seattle Seahawks. Christopher with the Colts. And the Los Angeles Charters, obviously. San Diego Charters, I'll go to you. Justin with the Bills. Brian with the Falcons. Mike with the Packers. Don with the Bears. Gordon with the Lions. Justin with the Washington football team. Slash Redskins. Tommy with the Ravens. Don with the Jets. Blaine with the Vikings. Don with the Saints. Dallas, Don with the Saints. Daniel with the Cowboys and Broncos. Nicholas with the Niners. Don with the Texans. Jason with the Steelers and Chiefs. Nicholas with the Cleveland Browns. Brian with the Bucks. And Mike with the New York Football Giants. All right, and let's alphabetize by team name. And we're gonna pause the video. When we come back, we're gonna see if there's any trades. Then the break. Stick around, BRB. All right, welcome back, folks. No deals were done. So that list right here remains the same. Thanks for hanging with us on a Saturday night. Appreciate it. Let's see what we got in here. 2011, a blast from the past. We were looking at the, we were looking at the 2011 draft. Cam Newton, Vaughn Miller, A.J. Green, Julio Jones, J.J. Watt. All right, those are some of the guys. Mark Ingram. It's all in the first round. So, among others. So let's see what happens here. It says 20 leads, this must be it. You can see, for those of you not familiar with the old Five Star and Tops doing football products, this is gonna be a treat. So this, I don't even remember this era either, where the Five Star were intense. Isn't that crazy? They have been, Jeremy, but we're 15 minutes away from the show ending. So yeah, I don't think that's not gonna happen tonight, unfortunately, but it definitely will tomorrow. Remember, check the schedule to tell you whether there's a late night or not. It's pretty helpful. All right, let's flip these over here. We got a book in the mix as well. Maybe let me get a couple of slider boxes here. Any chance tops, I've asked tops executives that before, they want to, but I don't know. It's just a matter of if the NFLPA wants to go a different direction, wants more people involved or that exclusivity goes away or if they just outpay Panini just for all that. So I don't know, there's a lot of different factors involved. But for now, I don't think so. But here's Jason Whitton to 129. There may be a possibility, it's for the Cowboys, that maybe with tops going public and working in a digital market, the NFTs and stuff like that, that they might be a way to kind of back in, backdoor into doing some football, which could be interesting. There's Mike Tolber, 13 out of 129. Dude was like a bowling ball. That goes to the Chargers. That'll be for Chris Hoyt. A baseball five star used to have a bunch of cards in one slider box until they changed the configuration. Out of 129, Defan Bess, 99 out of 199. That's for the Dolphins, Tommy Kendrick. And our autographs. We got Kendall Hunter. Rookie Otto for the Niners, 27 out of 190, 190. Nicholas with San Francisco. Patch Otto Randall Cobb, nice. Do we give it two colors? A little bit of dark green peeking there. 56 out of 99. Rookie Patch Otto for Green Bay. That'll be for Mike Krasinski, who won that spot. So yeah, so we'll see if tops kind of backs into the football market like that. That's some of the speculation. We'll see. It'll be nice. There's Champ Bailey. He's pretty good. Three color patch and autograph. That looks like 26 out of 50 for my rivals, Denver. Dan-O with that, Dan-O. Let's see, what's behind the book? Big patch. Let's see the patch first. Jake Locker. Big jersey right there. 68 out of 88. And that is for Nicholas and the Titans. And the book is gonna be Future's Autograph Book, Three Piece Memorabilia for the New York football giants, Jarell Jennings, Jernigan that is, Jarell Jernigan out of 35 for the New York football giants, Mike Krasinski. It's with another spot that he won. Next one. Right, so you think if tops gets a license, Panini's stuff will drop? Like value, you mean? And tops will be worth a lot less? I think there might be different enough where, I think, I don't know, maybe before, maybe six, seven years ago, I could see that argument. With the hobby being the way it is, I think people would love to have top stuff. I think it would be a good thing for everybody. All right, Chris Hoyt and you'd probably be, probably have to compete with the competition alone, I think would be better. There's Richard Dent to 129. That's for the Bears. The Reggie Wayne went to, goes to Chris Hoyt and the Dorell Rivas to 129. That's for Don. There's Jamie Harper right here. Well, there wouldn't be any more. Well, how it works is, oh, that was a crazy shatter bet. Like there's like 30 somewhat football product released a year. If it ever went back to the sharing thing again, it would be like Panini would have 15 releases and tops would have like 15 releases or something like that. It wouldn't mean like tops wouldn't have 30 releases and Panini wouldn't have 30 releases. There's Jamie Harper, rookie autograph for the Titans, Nicholas, so previous years, they would have that sort of cap on total. There's Leonard Hankerson, six out of 99, three color passion autograph. Is that a catch? It might be a catch right there. Five out of 15, Bart Starr, old school guy Mike Rosinski with the Packers and a dual autograph. Ooh, different teams. Beall Powell and Jarrell Jernigan, 11 out of 20. Maybe back in 2011, this would have been a monster. It's a randomizer between the Jets, Don and the Giants, Mike. Good luck guys. And the relic on the bottom is DeMarco Murray, rookie relic, 23 out of 40. He was pretty great too for a good chunk of time. Dallas, Daniel. At least if Tops got a lightsaber, hopefully they would not agree to all that before they put a cap on it. There's only so many cards that the players themselves want to sign too. I don't think they want to sign cards for 60 different products every year. So if Tops ever gets to share this license again, I don't know, it sounds like Panini is going to hold onto this exclusive just as long as possible. It's pretty lucrative for them. All right, last box, folks. Remember Peyton Hillis to 129, there's Peyton Hillis. This is for you. We got Brandon Jacob, 96 out of 129. I remember this guy too. Giants, that'll be for Mike. We got Jimmy Graham, 102 to 129. This familiar Saints gear. We got Austin Pettis. I feel like there's an Austin Pettis story. Was he the one that... Let me look this up. No, never mind. I was thinking of someone else. Austin Pettis, though, is related to, some of you may remember, old baseball player, Gary Pettis, nephew of former Major League or Gary Pettis. Here it is. Three color patch and autograph. Cam Newton, rookie patch auto Cam Newton. 95 out of 199. That's pretty awesome. That's Don McCrae with the Panthers. He won the Panthers in that filler break. As we were running out of cards, I didn't think we're gonna see any of the bigger names. Could be a revival on his RPA stuff if he has a great season this year, which is possible. We got Kellen Winslow, 87 out of 150, Buccaneers. Brian Ferguson with Tampa Bay. And we got a book and a relic. Let's see, let's get the relic only first. Nice Ray Lewis, 23 out of 40. Two color patch for the Ravens. What time is it game time? Tommy. And we got a signature book. Three piece relic right here. There you go. Heinz Ward, nice. The relics aren't super exciting, but it's a nice on book autograph right there. Is there a number here? Oh, there it is. 10 out of 35. And that goes to Jason Siebert and the Steelers. And there you have it, ladies and gentlemen. Some five star right there. Let's do the randomizer between the Giants and the Jets. All right, so let's go back to some new dice. Good, NYG. NYJ. Let's roll it, let's randomize it. Three and a six, nine times. Team on top after nine, we'll get the dual auto. One, two. Three, four, five, six, seven, eight. And ninth and final time, flips to the Jets, goes to Don. There you go, Don. You get the dual autograph, Jarell Jernigan, and Belal Powell, 11 out of 20. And there you go, ladies and gentlemen. Really solid rate that Cam Newton was really nice. Thanks for getting into the action. Thanks for breaking with us, ladies and gentlemen. I'm Joe. I'll see you next time for the next break. Bye-bye.


It's a pleasure to be here and participate in this really fascinating discussion. Our panel title, as you see, is Making Biomedical Science Nimble. It's The Patient's Stupid. Preparing for this panel put me in mind of some of the experiences that I had as a graduate student in the biomedical sciences in something called the Medical Scientist Training Program, or MSTP. Just curious how many people know what that is. It's basically a federally funded program to support the training of physician scientists, so people with both a medical degree and a PhD. And I envisioned a career in medical investigations and research that involved people, which is why this program attracted me. But the orthodoxy at the time was expressed by the person who was then the director of the MSTP program at my university. He insisted that all the students in the program cut their teeth on basic biomedical sciences, basically going into the lab where we could control most of the variables and conduct clean and elegant basic investigations. Well, that, he said, was the foundation for any type of research that we could hope to do in our careers, even on much more complex systems like whole people or heaven forbid, health care institutions or systems. This latter type of research was looked down upon and considered soft and error-ridden. So off I marched into the lab prepared to exercise the practice of clean and skeptical scientific inquiry. But what I saw in some, and certainly not all labs, was that the pressure to attract competitive research grants, establish a name, advance an academia, led to practices that weren't very scientific. It was like repeating experiments until the results the researcher knew were right, were found. Not publishing negative or null hypothesis results, the things that basically we tried and didn't work as we had expected. And as Dan pointed out in his earlier introduction to the conference, there has been more and more in recent years a recognition that these types of biases or sloppy or inadequately robust results from basic bioscience have not been appreciated enough by the folks who are trying to translate these promising discoveries or what seem like promising discoveries in the lab into treatments or methods to fight disease in actual people or in populations. And because that hasn't been appreciated enough, there have been recent articles such as one in the prestigious journal Nature by C. Glenn Begley and Lee Alice who raised this issue in the context of cancer research. And basically they painted a picture of dozens of clinical studies and patients who were needlessly volunteering to participate in research in studies that really had no chance of working because the basic science on which it was based was not replicable, could never be repeated, and in fact was spurious, wasn't of value. And so they've called for raising the quality of preclinical research. Well, our panelists today have recognized that the problems and the opportunities to fix them go even deeper than that. They challenged the orthodoxy that was expressed by my old MSTP director and the value of our medical knowledge enterprise, since we're using that phrase today, places the value that that enterprise has traditionally placed on different types of investigations. And their work it seems follows from the belief that some of the most intractable problems in medicine can't be addressed solely by research on lab rats, cells or molecules on a petri dish. And in fact, often we do understand the basis of the disease or we may even have the drugs or the methods that should be effective at treating it. But we have as yet failed to translate that into effectiveness for individuals in a population. So these are complex problems where the biochemical mechanism of the problem is but one variable. And what we will discuss today are the possibilities that it's not just different tools that we need, but different skills and approaches that don't necessarily flow from fluency in how to do the basic science. So the work of people like our panelists is really part of a movement. And I'm happy to say I recently went back to speak at my MSTP program, annual retreat, and in fact now a much wider range of student investigation is being encouraged, including PhD research on health economics and a lot of translational and collaborative research. So it may be that these types of research are in fact gaining currency and respect even in the top institutions. So I'd like to begin with Ron DePres, who's the director of the Center for Community and Public Health at the University of New England in Maine. And also the director of UNE's Center for Health Policy Planning and Research. His work focuses in particular on how to deliver healthcare to people with chronic conditions, the ones people live with for a long time like diabetes and hypertension, heart disease, asthma, COPD, and prevention strategies. And Professor DePres, in January, a group of smart people from a variety of clinical and non-clinical backgrounds, including New America's Shannon Brownlee, released a report under the auspices of the Entrepreneurship Foundation, the Kauffman Foundation, on improving productivity and quality in healthcare. And I want to just quote from them. They said, a recurring theme among scientists in the discussion was that medical biology is the hardest of sciences. Quote, if only it were as easy as rocket science. One task force member bemoaned. Biology is hard because life is complex and adaptive. Laboratory biomedicine is harder still because of the incalculable, incalculable variety of pathologies and treatments and the even larger numbers of ways they can interact with the body. Clinical medicine is hardest of all because one must deal with human beings, the most complex and unpredictable of creatures, end quote. So do you agree? And if so, how do we begin to deal with this complexity in research? And not only in clinical medicine, but also in what you focus on, which adds even additional layers of complexity, implementation research. That's a good question, Sherry. And I would say that you're dealing with applying applied research when you're dealing with clinical medicine, you're not going to invent some. But I want to go back and talk about one comment on clinical research. This is the placebo effect. You've all been exposed, I think, recently to a lot of studies that are looking at the placebo effect. And basically what it tells us is that that has a real effect on people. People get better, even though they haven't been taking the medication. I've been taking this, so it's really kind of a backdrop to understanding science that we have this thing called regression towards the mean, which has governed my life in the sense that every time we see something, we study it more, it comes back to a normal differentiation. Not to say that we haven't had great strides in clinical research, because we have. But the problem, as you mentioned before, is how do we take these great strides and apply them to populations? And populations that aren't anywhere near alike in many instances, whether they live in rural communities, whether they live in urban communities, whether they live in suburbia, they're very different populations. And it's hard to get these breakthroughs in science, whether it be taking care of diabetes, taking care of hypertension, how to take care of COPD, lung disease, how to get them to be effective in rural and in populations. Because now you're dealing not only with the application, but you're dealing with the economics, whether they have access to care, whether they have transportation to services, whether they have the insurance to pay for medications, whether they will take the medications because it gives them a side effect, which they don't like. 60% of the population of not only the US, but other industrial societies, they don't adhere. Use the term of adherence to the medications or to the prescriptions of their providers for a host of reasons. So we have failed in that regard, it seems to me. And the second way to look at it, we're talking now about clinical transformation. That's the buzzword in Washington at CMS. It's a really good word. How do we take things and transform this health care system to be more popular, people-oriented, to be more effective? And we have a lot of good research on comparative research going on, etc., etc., about how we can do better treatments. But how do we take that and transform the system? And that is what we don't know how to do, I would say. We don't know how to do it very well. Health delivery systems think about how do we make more money out of the system? Or how do we make this profitable so that we can continue going? But that's not the answer. The answer is how do we get populations to change their behavior? To change their behavior and adhere to whether or not they're taking medications or changing their lifestyles or changing whatever they did to become healthier because the facts of the matter is we aren't growing at all more healthier at the pace at which we're developing new treatment method, treatment remedies, at the pace at which we're developing new surgical procedures. We aren't growing the improvement. We've improved in this country and other countries, mostly because of very simple things. Nutrition, lifespan is expanded because of better care to moms. It hasn't grown because we've done a better job, necessarily, of taking care of people with hypertension, diabetes, COPD, other kinds of current conditions. And how do you translate that? So that's basically, I love that comment about the complexity. It's not about rocket science because I think what this calls for is a sense of rocket science. And how do we transform what we know now into actual practice among people as well as providers? Now I want to go back to one thing. Back in the 80s, I was part of an NHLBI 10-year study, I'm sorry, five-year study on hypertension. And we knew then how to take care of hypertension. And the question was, how do we get it to people to do it? And the study came out of a set aside by an Ohio congressman who was head of one of the subcommittees. And people poo-pooed it. But actually, the state of Maine, among other states, Connecticut, there were 10 states, Michigan, California, really took this money and was able to change the way for at least a period of time people practiced. And people actually lowered their diastolic and systolic blood pressure as a result of interventions in health care delivery system. I'll stop there. Just real quick, actually, there's a great quote by an online activist, patient-e-patient Dave. He always likes to talk about, you're 50% at here. And patients are 50% at here. And actually, doctors follow standards of care, known standards of care, only about 50% of the time, too. So in reality, we're actually practicing medicine about 25% right now. So we've got a long way to go in terms of actually getting to even what we know is right. And we can talk about the unknown, which is probably what we're going to get into a little bit today. Thank you, yes. So we're going to talk next about a disease I know you know a lot about, amyotropic lateral sclerosis, or ALS, or as it's commonly known, Lou Gehrig's disease. It's a devastating illness. I'm sure most of you are familiar with it. Patients lose the ability to move their hands, arms, legs, eventually the muscles that govern breathing, while their minds remained perfectly intact. And for a couple of decades, mice with multiple copies of a faulty gene, superoxide dismutase 1, served as a model for the disease. And scientists relied on them to test promising treatments. But since then, close to a dozen drugs that were reported to increase the lifespan of these mice have failed to benefit ALS patients, leading researchers to question the very premise that these mice were an appropriate model of the disease. Similar problems have arisen with models of other so-called neurodegenerative diseases like Alzheimer's. Our other panelist who you just heard from, Ben Haywood, is the president and director of Patients Like Me, a company that provides a platform for people to share their health experiences with other patients, with researchers, or other organizations and companies that focus on these conditions. And Haywood co-founded this company in 2004, inspired by his brother Steven's battle with the disease, ALS. So Ben, your company contributed to ALS research a few years ago in the study of the drug lithium. Could you please describe that study for us and the larger concept of how our current information and communications landscape could contribute to the biomedical knowledge enterprise? Yeah, no, I mean, I'm going to go back even a little further to tie in this sort of pre-clinical area as well, just in terms of my family's journey. So my brother was diagnosed back in 1999, and actually our family did two things. One is start patients like me, but my other brother started a nonprofit biotech doing hardcore in vivo drug discovery. And all of us are actually mechanical engineers, but manufacturing engineers by training. And I think Jamie actually spent $70 million doing hardcore in vivo drug discovery, running about 30,000 SOD1 mice through his lab, and repeated every known study ever published in the 20 years of that disease. And there's a lot of questioning of the mouse model. And what he found is that almost every known study was in the noise floor of the mouse model, so that scientists were not taking into consideration the noise of the individual, of the mice population. And I think what that really challenged for him is that we really don't have a baseline understanding of disease and illness, whether it's in the mouse model or in the human model to use the same language. And so we started patients like me really to begin to actually understand and measure and value, and measure health in particular chronic illness in a much more patient-focused way so that the end user, the patient of what we're trying to solve here in healthcare is actually determining the value of both the products and services. So what we built is a platform for patients to come and actually share their experiences, but it's not just an anecdotal site. It's taking patient-reported outcomes, validated instruments, and having patients share them in an open environment. And that has allowed us to do many things. And you mentioned the lithium discussion. So that was basically a real-time comparative effect in this observational study that occurred on our platform. It wasn't designed a priori. It was a result of being on the platform. Basically what happened was there was a published study in PNAS, a good publication in Europe that showed that lithium dramatically slowed ALS in a small phase one clinical trial. So about 16 patients on drugs. Obviously in a universally fatal disease, that was a big deal. So 10% of the patients on our platform went on that drug, obviously with their doctors because it's a prescribed medication. We now actually have about 20% of the ALS patients just to give you a sense of scale of the size of the site for that disease and about 5% of MS patients. So about 10% went on drug. And about nine months later, we were able to show that in the real world using our 3,000 controls of ALS patients plus the 300 or 400 plus patients who went on drug, that there was no effect of lithium. And so while when we first initially published our findings via a poster, three studies subsequently started NIH funded studies in the $20 plus million range. And they all stopped within six months of futility basically validating the results we showed basically in near real time. That our work was actually eventually published in Nature Biotech going back to this in the medical industrial complex. One of the things about a new methodology like ours is that we have to actually really go back and utilize publishing in other ways to validate it's a new methodology. But it just demonstrates the power of what you can do when you bring the aggregate patients and you give them the tools and the instruments to measure their own health and measure value both on an individual level and then on a population level as well. And we're across 1,000 plus diseases now but we have significant depth and a handful of neurology for diseases. Going back to Professor DePraes. Most of our $30 billion in health research is biomedical research. And so the questions that you raised as fundamental questions that we need to be focusing more on is this also a question of how our American funding priorities are allocated? Do we need, does that need to change? Or is it an attitude question? Or are we lacking in tools and imagination? Do we need a national research program on the science of health delivery as the Kaufman Task Force called for? What are your thoughts on those questions? My thoughts are I think we've, I'm not saying we should detract from the basic biomedical research and which means applied research and basic research in some kind of term that we use the term transformational research which usually means getting the experiment to the drug that works. But not, I think we need another form of research that helps people understand how they can bring that to apply it to the health delivery system and apply it so it's effective and so we can measure it. And it is a science. It's not a science of a lab science. It's science of how do you take information about what we know works and get it to work in a population? One of the things that we're doing in Maine and my background is I had an opportunity to work all of the country from Alaska to Texas to study health delivery systems. And that gives you, every system is different. But what we're trying to do now is trying to look at how can we bring diabetes care, for example, into the patient's home so they can actually learn to take care of themselves and keep their A1Cs down and keep their diet down and keep their exercise levels up and do their foot exams on their own. They don't need a doctor to do that. They can do it themselves. It's that kind of, but we don't really know how to do that very well and there's not much funding for that. AHRQ, which has been around for many years, it was first started as a national health services research group in Washington, part of the federal government. That's been traditionally underfunded because most of the funds go to NIH. But AHRQ is the health services research arm of the federal government. Now most basic scientists cast that up but without that kind of research, we don't know how to take these experiments and take these knowledge and apply it. So I'd say we need more emphasis on how we apply it through that. And in the methodology, we don't really know how to transform medical care system. How do you know? What has to do with organizational research, has to do with medical research, has to do with people and what incentivized people has to do with healthcare delivery systems, how they're paid for. So it's very difficult stuff. It needs a new type of paradigm. And what I think of when I deal with my pharmacology friends is that how do you study a mixture? Because most of the pharmacology studies around forever were about studying the effect of formaldehyde on cancer, or studying the effect of any chemical on this or that. But we don't count. We're not chemicals, single chemicals. We're people. We're populations. We have issues. So you do need some kind of a way of looking at that which takes what we know from other disciplines and applies them to the delivery system. Thanks. Well, you got to the question of incentives. And it seems to me that a lot of the reason why we may not have more of the type of research that would be transformative has to do with financial incentives. And I'm curious, Mr. Haywood has a business background. Your company is for profit and in journalism we learn follow the money. So what can change if economic incentives don't change? And how can we get the economic incentives in place to do the types of post-clinical research that you believe are needed? Yeah, no, actually. So we are a for-profit company and that was a very active decision we made early on. Actually, as I mentioned, my brother had run a nonprofit and we really thought about starting this as a nonprofit as well. But we felt fundamentally if you couldn't figure out a way of aligning the patient's individual interest with financial incentives in the healthcare system that we couldn't scale and actually make the impact that we wanted to. And so we felt that we had to figure out a way of aligning a patient's interest with the healthcare system. So a couple of the challenges we had though is as we began to help, particularly the chronic life-changing illness space that we're in, those patients begin to measure and understand their own health is who in healthcare actually pays for measuring outcomes? And the truth is in all of our $2.4 trillion that we spend on healthcare, the only people who really spend money to measure fundamental health are pharmaceutical companies because they're regulated and need to measure health. So in terms of the most effective and sort of mature and measuring health effectively, it's the pharma industry. And so that's where we started. I don't think that's where we want to end up. We want to have an ecosystem where patient value drives market value of products and services. But there's a long way to go for that realm. So again, we started in pharma for a very specific reason because that's where the money was for us. But we don't feel like that ultimately where it needs to end up. And as ACOs spring up and payers are beginning to look at patient value and you look at what's happening in the UK with NICE and even the EMA, which is the European FDA, they're beginning to look at patient end value in terms of approval of drug indications on a much more narrow scale and then opening it up as they prove indications in the real world, that the system is gonna have to get much more sophisticated measuring health. And today, there are some illnesses actually particularly ones you focus on. We're pretty good at actually, I mean, if you look at diabetes and chronic illness but actually some of the illnesses that we focus on epilepsy, MS, fibromyalgia, there's really no true standards in the measurement of those diseases. And you talk about, and we ultimately, I mean, our philosophy is that if you engage patients in that problem, the system will be better for it. And just a small example that's in sort of the space you talked about the AAN last year or maybe it was 2010 now, published guidelines for epilepsy patients, for epileptologists and neurologists about the best standards of care. It had nine simple things, like asking about pregnancy, asking about driving, asking about side effect of drugs, very standard, simple things. And what we did in concert with the author of the guidelines is we actually surveyed and engaged patients in whether or not they were receiving that care. So instead of waiting down the road in parallel, we knew with a decent sample site, 300 plus epilepsy patients, how they were meeting the standards of care. And we were able to already measure that the epileptologists were doing it better than neurologists that were doing it better than GP, which you might expect, but actually people were wondering, why would you publish such simple guidelines as those AAN guidelines? And the reality is because the standard of care wasn't being met broadly in the wild. And just following up, one of the ideas, I think it was in the Kaufman report that most intrigued me was this idea that you would have like a sort of a preliminary drug approval process and then a couple of years after that where the drug would still be studied actually out in the public. And do you see, is there a financial incentive for that or would that regulatory change that would need? No, I mean, I think that we're moving that way. I think the both Pharma and the FDA are doing more and more what's called phase four post-market surveillance. I think the reality or the challenge actually for the system is that the rigor of phase one, two and three trials are being applied to that phase four level. And that is ratcheting up the costs of having a drug on market significantly and squeezing sort of phase one and pre-phase one clinical dollars because it's coming out of the same pot. So I think what the system needs to do is begin to look at more and more models for sort of broad monitoring. I mean, our platform is one of them but there are other ways of doing that. And I think it's very important. And the FDA has a long-term initiative called the Sentinel Initiative in that space. But I think it's very important that I think we, random glass control trials are very nice for tidy, clean research. You even mentioned cocktails. They're almost financially impossible in doing cocktails. And so we need every day in doctor's office with patients there are millions and millions of experiments going on and we capture very few of them. There's a hope that EMRs will fix that problem but the reality is for a lot of illnesses we don't actually capture health information in EMRs. So we need to again build out new systems of truth and that's gonna take a long time. Electronic medical records. And I guess along those lines, the last thing I'll ask you is, this type of research and the research that could be built on your platform, the critique of it would be that it lacks blinding. So the idea that the patient doesn't know, get it getting back again to the placebo effect you don't know which drug you're taking. And that's an important part of testing drugs in the earlier phases. So how do you respond to that? So the reality is all research has biases. RCTs are very narrow focused populations with very scrubbed down criteria that don't represent what's gonna actually happen when you put them in patients and they're 50% adherent or they're dealing with working nights and they're- RCTs are randomized controlled trials, sorry. And observational studies have been done, I imagine you've been involved in a number of them. I mean, that's been done for a long time and there are great ways of doing the Framingham Heart Study which is one of the most famous ones where most of our knowledge about heart disease have come, came from. And so what we're looking at is a version of that where you're going direct to the patient and it has its own biases and it has biases that we're still figuring out. But as evidence, as a methodology of medical evidence, randomized controlled trials are 70 years old and they're really good at what they measure. And as our methodology or even observational studies are have less maturity, less dollars put into them. And I think they will become as important evidence as RCTs going forward. But it's going to take a while and it's certainly going to take a while before the regulators value them as greatly as evidence. So the point being we don't need to throw away our old methods, we need to add on new methods. And there are new funding levels to do some of the things that I'm talking about.  Well, it used to be they would just dole out money for due services. Now they're under this current administration, they're requiring evaluation and evaluation research can tell you whether or not things work or not. CMS with this whole Center for Health Innovations is they have to spend $10 billion a year to try to transform this healthcare system. That's part of the legislation. And they are funding large-scale efforts now to try to see how we can transform the system. That the only criticism I have of that is that it's often so predicated on changing the healthcare provider system and not dealing enough with patients and patient self-management and those types of questions because they're harder. People don't like to deal with hard questions like how do you change people's behavior? Thank you. I'd like to open it up to the audience. Please wait for the microphone on its way. So while I was recuperating from a stroke last summer, it was blogging it of course. And I learned a lot about telemedicine and applications to stroke specifically which is a no-brainer, literally, sorry to use the expression. In other words, getting the right set of eyes, right neurologist, he's sitting around at home, he can Skype on steroids, he gets to a patient in some emergency room and gets the right medication. So the technology is there. It's a no-brainer, it's in like 600 hospitals but it's not in 6,000 hospitals because the innovation that has to happen is administrative. And I would like to get some input from you on how often you see where the big bang for the buck thing isn't actually the new molecule but looking at the administrative bottlenecks and finding ways to use R&D to look at systems and see, in this case, the bottom line is Medicare doesn't reimburse unless the doctor's at your bedside physically. And that's a problem with telemedicine except in like three areas of the country that are officially rural. So what are the missing opportunities there? Missing opportunity, what you're asking about is the opportunities to get what we know out there into the general provider population. Is that the question? At the administrative level because you have to have financial incentive to do these things. And you find that most systems cannot or say they will not do it unless they have the financial backing to do the right thing or do what's required. I'll just give you an example of CLPD. We know how to diagnose, take care of CLPD really well now. It's the use of spirometry. But the problem is we haven't trained nurses how to do spirometry. We haven't got spirometry out there in the general communities coming but we've known it for a long time. And so we don't, spirometry is used both to diagnose CLPD and to formulate the treatment protocols. It is essential. It's like A1C was back with diabetes back in the early, in the mid-80s. A1C we were experimenting with. We were part of a study with Dartmouth and we were basically knowing how to do it knowing what it was. But we found how to do it and now we do it. But to get it out there being used is a tremendous problem because of not, it's not about a hospital administrator talking to you but it's about the whole apparatus of change in a complex organization that requires financial backing. It requires people who want to make the change. It requires people who want to educate the health provider workforce to do it. Because it's not just about docs. We don't deliver care anymore just with docs. We deliver care with lots of different health professions. I mean, I just, briefly, I mean, I think, I agree that there's probably a huge opportunity there but I will say that I think the system is going to be challenged in ways that we don't even comprehend at this point in time. I mean, I think, if you look at biology and new technologies and biology and I know you were saying other areas if you look at new technology, proteomics, microbiomics, all of the omic technologies, there's basically Moore's law is happening four times as fast in biology as it is in computers. And you think, okay, the first PC was 32 years ago now. So in eight years we're gonna have the revolution of evolution, whatever you want to call it, of computers did to society in potential biology. And yes, the system is not ready for it and cannot handle it. And so I don't know whether it's going to break it and what's going to force change. We believe that enabling patients in that dialogue and that journey will help the system, well, will force the system to adapt more quickly. But as often the case, engaging patients is challenging in the context of they are not populations, they are individuals and they have problems that we don't typically call health that are really related to their health. Healing is not about just A1C, it's about all of the factors that go into managing it, which often are not medical. And we need to capture all of that in a system today that is designed not to. And there are some good successes around this. I mean, look at Vermont. Vermont's doing what's called, by the way, there's no mystery why Vermont is the so-called healthiest state in the nation. For me, I mean, maybe folks think it's because up here in the northern part of the country and it has all these rural people who eat all organic food, no. It's partly that maybe, but partly the issue is that they're doing things like the Vermont Blueprint for Health, which works with people and they embed providers and social work and other kinds of people in the primary care office to work with people and their problems of taking what you've prescribed for them to actually turn it into reality for their everyday lives. That's a system change, that's a sea change. It's happening in North Carolina and it's happening at Geisinger in Pennsylvania. And those kind of how we do it, we need to follow up with doing that elsewhere in this country, because it can work. It just takes time. Well, I'm sorry, back there and then up here. I wanted to carry on with the amount of information that's now being produced. Mark Segoff, I'm sorry, at George Mason University, the cost of sequencing genes and so forth for producing all this information, storing it, relating it, just following. However, our laws and rules and regulations regarding privacy, consent, the confidentiality of information is working out of the way so that I wonder with the, there's an advanced notice of proposed rulemaking now that HHS has put out, I told in the water, but it would impose all of the very toughest scrutiny on patient data because of what they call into informational risk on anything a patient is asked. Especially by a non-medical professional, including other patients. I wonder if you can comment on how patients like me negotiates around these very difficult issues of confidentiality, privacy, consent, and so on. Well, there's a straightforward answer of how we do it today. I mean, we went directly to the patient and we're not a regulated body in the context of HIPAA or other things. And even in the spirit of HIPAA, we actually do go directly to the patient and ask for permission to do what we do with the data. But actually that goes to, I think, what you're talking about. I think for us to be successful thinking about integrating three things, which is taking the technology, so clinical research, integrating it into the clinic, and then actually having patient value be the primary driver of that interaction. The system's gonna really have to change and we have these paired buzzwords that we talk about, but I mean, I think as you mentioned, it's gotta go from much more close to open.  So it's an open environment. So private to shared, which is, we gotta get more comfortable with sharing this information. Patients, we gotta start talking about them as subjects and talk about them as partners. And when you partner with patients, then they're much more willing and able to engage in research and actually free up that data liquidity that you talked about. We talk about Secure and think about Health Vault, which is Microsoft's product for integrating personal health records. It's a great concept, but they branded it around a vault, which is starting from the wrong way of getting data liquidity, right? They branded it as a place that you put money and it never goes away. So that's a terrible, that's even just a branding failure, you let alone creating the environment they wanna do. We often talk about validated in the context of healthcare. So validated patient outcome scales are other things and in my mind, the term validation in healthcare is an excuse to stop learning because once it's validated, we never go back and see, hey, can it get better and can we integrate it? So we need to go into an integrated continuous learning environment and that actually can get down to the clinical decision level. And then lastly, I think, aggregated data and population health is important, but the only way I think you can affect that is to begin to personalize it and make it meaningful to the individual patient. And you can talk about that for us on the micro level around an outcome measure. So measuring how a patient is doing, what, yeah, population outcome, most population, I'm sorry, most patient reported outcome scales are designed for population management. And to be honest, to get a patient to engage in that problem, you gotta measure it for an individual and have it relevant to the population. And by the way, what's a population gonna be when we break down disease into the molecular level in the next five to 10 years? There's one area just we have to just mention that we haven't covered and haven't got time to cover it, but that's to do with population prevention and how do we do prevention research? Because it's really a very challenging, difficult area to get people not to start smoking, to get people not to eat right. Those are areas that we don't know how to do very well where we have very little money put to that, to prevention. We talk about it all the time, but it was very little effort, very little money and that has to do with really then population. Health policy, how we change health policy, not just being health policy, I mean transportation policies, food policies, those are areas that we don't do a good job of understanding how to affect, because it's political. But a challenge we have, and this is important, I think for this group, I think it's important is that, is exactly to the point you're making it, which is we actually don't talk, when we say healthcare, everybody means different things. And so prevention and population management on a large scale is different than chronic serious illness and is different than acute care. And we need to really start to dissect and optimize to the ones that make sense. And I don't think we do. I think people, both policy makers and then individuals, and when little Jimmy gets sick and gets leukemia, those are all different problems and we all have different emotional attachment to them in different ways. And we really have to dissect that in a good way so we can actually solve the right problem at the right time. So we have one minute left, 30 seconds for Deb Blum's question, 1515 for your answers and your wrap up please. So in my other life, I'm a chemistry, toxic substances blogger at Wired. So I spent a lot of time looking at, thinking about what I'm gonna call the educated patient, which is what I hear running as a theme through this that we're developing a medical system that relies to some extent on the educated patient, the patient that knows about telemedicine, the patient that can tap into patients like me. And again, the patient who is informed say about herd immunity enough to make an intelligent decision about whether or not to buy into a vaccine or not for their children. But are we investing in that? Are we in a system that's increasingly relying on patients to do their own homework and be educated patients? Where is the policy that is giving us those educated patients? I think that's what we're talking about. We need to understand how to do that better. And we need to experiment on how to do that with additional types of research that will, it's the educated patient, but it's the patient, I think, as partner. Because they have their own, mostly patients have their own best interests at heart. They wanna get better, or they don't wanna be overweight. I'll stop. Yeah, I mean, just briefly, look, I'm not a policy guy, but I will say that when you look at companies like 23andMe, which is the direct consumer genetics platform, what I think is most powerful about them is that they're learning how to do that well. And they're starting with the people who are techno-radi and probably a little bit more literate and able to engage in that, sort of scientifically literate, engage in that problem, but they're learning how to communicate and simplify the science in a way that we can actually move that down the spectrum and actually begin to educate a larger population. And similarly, what we do is the same thing. And you gotta start somewhere. And if we throw darts at innovation that's sort of talking at the top, then you're never gonna figure out how to move down the pipe. But they're different problems, I think. And, you know. So I think we've had a fascinating discussion today. We've painted a vision of the future where we ask different questions where we don't accept that the drug is the answer, but it's implementation in the population where we change policies and incentives, dare to ask more complex questions, and use today's information and communications knowledge infrastructure to promote engagement on a patient level. Thank you, everybody. This was great. Thank you.


Hello and welcome back here to the channel. I'm evil rabbit. We are staring at the brand new m4 Competition edition here on a set of course up. We're gonna be taking a look at it today It just got dropped on the a set of land So being working for BMW you get a chance to drive this car in real life So we're gonna take it on the North Slavin today in a set of course and see how it does Make sure you guys find me on social media all which found in the description box below I said we get to it and Rip North Slavin in this brand new m4 so we are in the cockpit of the brand new m4 and I think we can actually look around we should be able to know yes We can so the interior is definitely a very very good model these carbon Fixed-backs reclining seats are definitely amazing We do have one that has these seats in it and they are super comfortable seats at this one is of course paddle shifters The one we have is a manual trans with those seats. So it's definitely an amazing car So we're gonna rip this on North Slavin and see what we can do with it Car does sound pretty good Get that out of the way So we are we are on the North Slavin tourist section We are on the factory street tires and things of that.  I do like how it has the HUD on the front windshield How's the real car and everything does is like I said a very very nice model. Let's make sure we are in the correct Steering trim for this. There we go And now we're just gonna go full send we do not need to use clutch because we are on a DSG setup Understand why that Got extremely loud the mSport transmissions with the paddle shifters are definitely a very nice Transmission getting a chance to drive them like I said on the daily at my daily job and for first impressions of how this car is This thing is actually Quite grippy for not being for being on street tires. This thing is gripping quite well. Love the nav Right in the center On the iDrive display kind of gives me an idea of what's coming up But I do already know the track so I figured the best place to bring this car out for a first initial test would be here on North Slavin Nuremberg ring and I'm thoroughly impressed with this car Does have the dummy downshifting where only downshift unless the car needs to downshift Which the real cars in the paddle shifter form do have as well You can't downshift unless the car is in capable range without belowing the transmission So I said first impressions of this car. This is oh I didn't think we were gonna save that this feels very good on the wheel definitely a lot of grip and for being on The factory tires and not on any racing tires or everything just off the rip this car is insane I love the gauge cluster with the g-force meter and everything Like I said, I definitely like the iDrive and the heads up display in the middle of the windshield Very true to the real car I can just power straight through I'm thoroughly impressed with how this car is handling right now With no tune straight off the rip this Bit of exhaust crackling there and you know, it's not stepping out on me Oh, as I say that we missed that turn It's not stepping out on me Which I anticipate a lot of cars in the set of course that to do so whoever made this model on the physics on this Say very solid driving car I'll leave the link for the car down in the description box below if you guys want to try this car out yourself So then I bring the question do you guys want to see a Drift build on this new M4 swapping it to rear-wheel-drive full and Putting a manual trans and everything in it Or would you like to see the build on the Volkswagen Polo rally car? so let me know down in the comments which car you would rather see built because I'm having a lot of fun with this. I want to love to keep this car in grip form, but also possibly do a Drift form of this car as well Probably didn't need downshift there cars a handful of feedback on this car is incredible feeling There's one of those downshift for dummies Driving these cars in grip form is definitely something I don't do enough of and I feel like I need to bring more of these Nuremberg challenges With some grip cars, so we just want to see Unsure why the exhaust note just got way loud This is like a weird echo going on with the exhaust note of this car on some persons of the track So like I say if you guys want to see more maybe Nuremberg challenges on some grip cars Let me know down in the comments if you guys like this style of video something a little bit different than drifting But I do enjoy grip driving and things like that getting a little bit back into iRacing here recently So if you guys want to see that stuff on the content, let me know my you know trials and tribulations of iRacing did hop into a race with the new M4 GT3 race car without any practice and Didn't go so well. It wasn't too terrible But it didn't go the greatest and I'm actually having a lot of fun with this M4 right now this car is truly truly good Like I said, I'll leave the link for the card down in the description box below So you guys can give it a shot yourself and let me know what you guys think of this car All the different views for you guys here today as well pedal cam wheel cam third person action going on as well as cockpit view with the apex look function of content manager This is a blast to drive you guys need to take this car out here yourself And so let me know what you guys think of it because this is actually oh Sorry BMW as we say that oh They let me get access to a brand new M4 and unfortunately We kind of just went hard and all my steering was not starting anymore. Oh We went hard into the wall Nuremberg fail not even in YouTube corner. Oh Ah I wonder if we will finish the run. Oh, yeah, there's definitely some Some wheel play going on in this car right now There's a lot of wheel chatter a lot of wheel movement as you can see going on right now and oh It's not happy It's hoping to do a whole lap without crashing, but unfortunately that was not the case So like I said if you just want to see more of these Nuremberg attack videos with some new cars on a set of Corsa Let me know it on the comment section as well as let me know what cars you think I should bring out here because I Don't mind driving Don't mind driving this track. I should be paying attention to where I'm driving so I don't wreck these cars more Yeah, you can see my steering wheel is not straight at all.  Maybe a street car. Please don't make it like a you know a zeta or anything like that That's gonna take me, you know 20 million hours to complete this track because that would just be that would be torture You know what being able to drive the whole track flat foot without ever lifting would probably be fun But yeah, that would be very much torture So overall impressions of this M4 in a set of Corsa This is a very fun driving car very grippy very nice And I do love how the car handles when you're not hitting a wall and causing damage to the car Because that did put a damper on this last half of this track I love the interior how it looks very tried and true to the real car And they did a great job modeling this like I said I've had the chance to drive one of these new M4s at my work and these things are a blast and a very nice car to drive So we managed to complete the course in the tourist section of North Slavin, so Successfully not destroyed the car. So we're going to come in to finish this section off And end this episode off make sure you guys follow me on all social media all of which are found in the description box below Yeah, you can see my wheel is not straight Oh, yeah, there's another hit so That'll do it for this episode here on a set of Corsa as always I think you guys come back and watching People rabbit. I'll see you guys on the track