Biobanking Today: Facebook, Crypto, Artificial Intelligence


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Sanguine partners with patients and leverages their health data to accelerate your research for their condition. By working together with patients directly, Sanguine is able to perform home visits and to easily retrieve medical records on their behalf. 500+ completed studies. 20/40 top pharma. 30,000+ patients.

Transcript

Brian Neiman:
My name is Brian Neiman and I’m the founder and CEO of Sanguine. And a little bit about our company before I jump into the topic is that we work with patients directly to engage them and collect their data on their behalf and match them with either clinical trials or observational studies for them to help accelerate research for their condition. And I’ll give you a little bit more about our company throughout the presentation, but I chose these topics to discuss today because engaging with individuals directly is what our company does. And we’re seeing specific trends in the fields of blockchain, cryptocurrency, artificial intelligence, and social media that are impacting how we’re all doing our daily work each and every day.

Brian Neiman:
How social media and sharing your health data impacts the people that are a part of the all of us program that are participating in biobank studies in different organizations, both private and public. So these are the trends that we’re seeing and I’d love to share it with you and I hope that it makes an impact in how you’re making decisions or at least it gives you a few ideas on how to improve what you’re doing.

Brian Neiman:
My name is Brian Neiman, as I mentioned, founder and CEO of Sanguine. We founded the company in 2010. We’re working with 20 of the top 40 pharmaceutical companies. A few of our esteemed clients are here in the audience today and I thank you for coming to the presentation. We’re working with 30,000 plus patients diagnosed with various conditions, anywhere from autoimmune conditions like Lupus and Rheumatoid Arthritis, all the way to rare conditions such as Duchenne’s Muscular Dystrophy, all the way to different oncologies. We were recently admitted to be a part of the startup health transformer program about a year ago joining about 100 other companies that are on the cutting edge of medical research and healthcare.

Brian Neiman:
And in the time I have left, I’m an adjunct instructor of digital health at the University of Southern California in the School of Public Policy. So what are we going to talk about today? We’re going to talk about how Facebook, Instagram, and different social media outlets change the way patients are engaged day to day. I’m going to talk about artificial intelligence and how the structuring of data and the availability of data is changing that in the space. And then talking about cryptocurrencies and initial coin offerings. So there are specific companies out there that are positioning themselves as exchanges saying, “If you give us your DNA data, we will give you a coin or we’ll give you some sort of cryptocurrency that in exchange for that you can trade your DNA or health data to other researchers at other companies and you’ll be compensated in the form of increase in value of the coin or crypto.”

Brian Neiman:
So there are different mechanisms out there. This is not something that, as I mentioned, our company is engaged with, but it’s something that we’re seeing more and more and we’re interested in the legality, the ethics, and truly the transparency of how a compensation reimbursement happens and translational studies and biobanking in general.

Brian Neiman:
So we want to learn what do these have to do with each other and why are we spending time listening to this? Well, the reason why is because we’re all facing the same issue, which is Pharma R&D expenses are growing 10% per year each year, no matter how we do it. In 2017 the FDA approved 56 novel medicines. First gene therapies were approved. First medicine for primary progressive MS and Sickle Cell and 74% are potentially first-in-class and over 1100 in development. So lots of development happening, more drugs than ever before, more are being approved, more are being created and there’s more investment. The question is, what’s driving a lot of this investment and why are we seeing that uptick? And we’ve seen that a big part of it is the demand for patient data.

Brian Neiman:
We’re seeing that pharmaceutical companies are paying 10 million plus for databases. I’m sure that many of you are familiar with the $300 million transaction between GSK and 23andMe as well as 23andMe’s transactions with Pfizer and and a Genentech over the past 10 years. So we’re seeing a lot of investment and we’re also seeing some output, some positive output. So identification is 17 snips over 15 regions and essentially 23andMe mentioned that the number of applications for access to its database has doubled essentially every year. So it’s grown comprehensively.

Brian Neiman:
We’ve also seen success with companies such as Flatiron Health, which is collecting data from oncology clinics and centers and is structuring that information for better decision analysis and support. So that transaction… So Roche acquired Flatiron health last year for a total transaction size of 2.1 billion largely because of the data set they had, but even more so because their medical record system was speaking to many oncologists in the space and other companies are seeing that, Roche in particular saw that as a way to deliver decisions and data analysis to physicians.

Brian Neiman:
So patients, they’re the data holders. So in 2019, who are they? They’re connected. They have multiple ways of connecting with others. As I mentioned, through social media, different chat apps and so on. They’re informed. One of my idols in this space is Dr. Eric Topol. One of his famous books is titled, The Patient Will See You Now. And that’s provocative of the fact that patients are now more educated and enlightened about their condition and different treatment options than ever before because of obviously the internet and also a medical information being so widespread.

Brian Neiman:
Patients are finding other sources other than medical doctors and physicians to diagnose their condition. So, if you have uBiome, which as we all know now, is a defunct by the 23andMe, Helix and ancestry.com. So this generation of individuals, of people that are afflicted with conditions or have family members afflicted with different conditions, they are more likely than ever before to take control of their own dataset. By show of hands, how many people here have either done uBiome, 23andMe, Helix our Ancestry?

Brian Neiman:
Okay. So starting out and maybe we’re not doing it as often because we know the implications and we might not want to know too much, but people on the regular starting to participate in many of these different aspects, 23andMe and Ancestry and using many of these other ones and coming to their physician and saying, “I have this snip or I have this mutation, I have this gene, what’s the implication for me?” And so physicians now more than ever before are faced with having to look at different datasets that aren’t being collected in the actual clinic.

Brian Neiman:
And on top of that, the last piece is with this most recent transaction with the GSK and 23andMe, we found that people are more sensitive than ever before about where their data is going and how it’s being used. And we’re going to get to that with Facebook and Cambridge Analytica.

Brian Neiman:
How patients are using social media today. So on the right hand side you’re going to see an ad. This is an ad that we work on with essentially, a Twitter ad that we put out that attracts patients diagnosed with lupus. As you can see, we have plenty of activity here. So, every day we’re promoting different translational studies or bio-banking opportunities. And the way that people are using social media today is conversations and stream of consciousness, lots of outcomes and evidence. So patient reported outcomes and different medication adherence conversations. Better surveys and understanding. So there’s an opportunity to engage with these individuals and ask questions about their healthcare. Feedback on commercial products as well as brain development opportunities.

Brian Neiman:
A piece of that is patient discussion boards and influencers. So what we’ve seen is that within specific conditions, there’s been an increase in following of specific influencers that talk about their condition and give tips and tricks on how to treat symptoms or lifestyle habits and choices. So within Crohn’s, we’ve seen members that chair candidly about their experiences. Talking about marriage and dietary habits and things like that. And then talking about how to deal with family and different groups. So basically there are different companies out there that are organizing discussion boards within specific communities, that organized conversation that are becoming valuable for a research purposes, whether it’s for brain development, for drug development, for patient reported outcomes. So we’ve seen patients like me and different forums continue to grow.

Brian Neiman:
So the point is that the data is out there, but how do we get it and how do we use it? So artificial intelligence companies are using data to predict the outcomes and use data to predict outcomes and decision support. So Flatiron acquired Roche, as I mentioned, by over $2 billion. The way they grew the company was that initially they built a decision support tool. So, very strong analytics. And then on top of that, they needed a strong data set in order to work on. So they acquired Alto Solutions, which is a medical record company and that included, I believe, almost a half a million EMRs already to start with. And so they layered their decision support on top of the medical record data there and improve and fine tune their information that way. So that’s just an example of one way is through acquisition or partnering with another company. iCarbonX, this company was founded by the former founder of the Beijing Genomics Institute and that company is responsible for essentially using artificial intelligence to predict certain outcomes or the progression of disease.

Brian Neiman:
And they acquired 50% of Patients Like Me, which is another discussion group that we discussed and acquired different patient communities and image processing companies. So what you’re seeing now is the analytics companies acquiring the data acquirers. And then Amino partnering with CMS, Center for Medicare and Medicaid Services, to access over 3 billion claims. And so for those of you that aren’t familiar with Amino, it’s a very cool company. What you do is you go to the website, you type in let’s say medical treatment or something that you want to have done and you type in your zip code and that will indicate to you who in your area is best fit to perform that procedure or can be the best doctor for you. That’s based on the CMS data claims and potentially insurance claims and how many cases, like the one that you have, they’ve seen. And so it’s essentially matching you with the best doctors. That’s the analytics portion. And the data came from both insurance and from CMS.

Brian Neiman:
So you acquired data aggregation companies, you partner with them. But how do the data aggregation companies get it? So medical record companies, that’s one thing, but how do Patients Like Me and other companies? So to date, what we’ve seen is an indirect line to patients in this space. So we see in order for pharmaceutical researchers to get patient data to date, it’s been through biobanks, CROs, data resellers. We all know like Walgreens in the relationship that they have with Acurian, PPD selling prescription data. And so health systems, pharmacy benefit managers, insurers, they’re getting information as well from the raw data source of the patients, which is DNA, EMR, different types of surveys. So essentially right now most of the data is coming from indirect parties.

Brian Neiman:
It hasn’t been coming directly from patients only in certain cases like Patients Like Me. And the reason for that, why patients aren’t the direct source for the most part of this information is because of the lack of transparency. It says zero transparency questions such as where’s my data? And by the way, these are questions that we face every day at Sanguine. So we’re seeing it front line. It’s where’s my data, who’s using it, how do I make a difference and is this useful for my time? And the opportunity we see is complete transparency of the data use and who’s using it, why, what and how. And then on top of that is, I think this is a duplication, but this should be lack of convenience for the patient. So having to travel to the doctor’s office or a lack of a digital form to actually collect that information.

Brian Neiman:
So excessive travel administratively burdensome. So how many individuals are really going to call their physician and retrieve their own medical record. It’s going to take you half an hour to an hour on the phone each time. So the process of engaging in research and collecting your own information is highly burdensome. And the ways that we’re combating that at Sanguine is we’re retrieving medical records on behalf of patients for them and also doing home visits to make it easier for them to participate in blood collection studies. But we figure that personalized engagement and having dedicated support, essentially being a patient advocate is going to increase participation. So those are the problems that are faced. So the opportunity is convenience and it’s data transparency. So that’s where the patient data blockchain companies come in. So the promise of the blockchain has been an increase in transparency. I know who’s using my data, it’s not being misused.

Brian Neiman:
And I’m the one who gets to choose how it’s being used and how it’s making an impact. So that was kind of the impetus for all of these blockchain companies to start, which I think now that they’re actually 50, 60 of them that are funded. So the companies using blockchain, cryptocurrency, the goal is to create a secure platform for peer to peer transactions, for sharing healthy genomic data between patients and researchers. And the idea, as I mentioned, is that people have control of this information. So here’s a list of the different companies that are out there and the different funding. Yeah, just to give you some feedback, so Luna DNA, this was founded and actually now funded by Illumina and some Illumina executives have founded the company. And we also have Nebula Genomics, which was I think a co-founder, one of the largest co-founders is George Church out of Harvard. So we have some prominent names and entities that are getting involved with the space. And these aren’t updated. I mean a few of these are in the 30s and 40s of millions of dollars.

Brian Neiman:
So how does this work? So the way that, for example, Nebula Genomics, every time a company does an ICO are called like an initial coin offering, which is kind of like an initial public offering, which you would see in the stock market, basically they create tokens or cryptocurrency and then they share it with people and those individuals come in and buy those tokens thinking that there’s going to be an appreciation of that value. Still have you? And so from there kind of like stocks, they sell those stocks to the data buyers, which is Pfizer, Roche, Merck, whoever, and this by the way is from their perspectives. So when they went and did their initial coin offering, this information was listed in there like a presentation documentation. So this is directly from their materials which is right here. The source is Nebula Genomics.

Brian Neiman:
So, in exchange for money, they give the tokens to these groups. These groups then exchange tokens with the data owners, which they’re the patients. And then in exchange the patients give their data out. And then the data owners, in this case, they purchase tokens, meaning they spend money in order to get sequenced and be a part of the project. As a utility token, essentially currency of exchange specific for that community. So in this case, the utility token is what’s being exchanged throughout the process. So utility token can either be exchanged for money or data in this scenario. It provides access to a company’s product or service, not designed for securities or investments. But we all know now that the SEC is looking very deeply into cryptocurrency now.

Brian Neiman:
If it looks like a dog, smells like a dog. So token can be exchanged by values tied to the demand of the product. And token pool has a finite supply just like stocks in a company or shares in a company. So the data owners, patients, data types, it can be anywhere from EMR, genomic surveys, wearables, med devices, and these are the different companies that are involved. Here’s another example of the general ecosystem. So a company, let’s say Nebula Genomics or Luna DNA, they sell the token to the data buyer such as Pharma in exchange for money. And then the data buyer pays an individual, a data owner with a token for their data. So if I’m a patient, the reason why I’m doing this is I give my data out in exchange for a token and I’m thinking that these tokens are going to be much more valuable, so I’m more likely to share.

Brian Neiman:
So it’s really incentivizing, maybe a little too much without too much information, what the process is. And so data owner tokens and the service or product from there. And so that goes into the crypto exchange. So the growing network of patients plus data increases the value of the network and increases demand from the buyers, which increases the value of the token. And then again increases the supply of patients in their data.

Brian Neiman:
So let’s talk about patient incentives, the utility token. So these are the different companies that I mentioned. These are the different token distribution mechanisms. I’ll let you have a minute to look it over. So you can redeem it for genetic sequencing, you have voting rights on data policies. Essentially what you’re seeing here is that this may not be the final destination in terms of patient incentives, but certainly moving in the right direction, which is democratizing and giving a voice to the individual on how their data is shared and how it’s being done. So there are different aspects to it. So you need to divorce the aspects of the financial incentive portion from the utility portion, from the transparency portion of how these individuals can control it. So the value here is security, privacy, and the ability to share what you want. The downside risk is obviously the value of the tokens can decrease significantly or people don’t truly understand what they’re getting themselves into.

Brian Neiman:
But at least this is a step forward in the fact that people can actually make a decision and know who’s using their information. Encryptgen and all these other companies, they’re all listed here. I give some information about Nebula Genomics, co-founder, George Church. So a lot of these different groups that have a different kind of a mechanism and data source on how you do things such as sequencing for one, EHR for another, patient reported outcomes for another, your Fitbit or wearable data. So they’re all different flavors of ice cream so to speak.

Brian Neiman:
So how do these companies make money? They make money by essentially licensing. So this is straight from the white paper that I’ve listed here. Essentially a client pays an annual subscription to this blockchain to get access to the system. So if I’m a pharmaceutical or biotechnology company and I want access to Nebula’s group of patients, then I will pay a certain fee to access a certain number of individuals. Some general findings. I know this is a lot. But what we discuss today, this is a lot of information. So the general thoughts and findings is all companies in the space are relatively new, founded 2015 or 16 most of the companies are focused on EMR, EHR or genomic information.

Brian Neiman:
Some have mentioned recruiting patients into trials and some have discussed utility tokens versus security tokens. Security tokens, obviously financial so that they can… Kind of like a stock, versus a utility token is more, can be redeemed for something else, like a gift card or can just be used as a security. So you understand like who’s been using your sample or your data. All companies have run, or plan to run initial coin offerings. Companies with patients and data will hold the most leverage. Patient centers will be key to recruitment and retention. Some folks are more interested in patient incentives and actually giving you information and like really focused on security and privacy as opposed to just the financial aspect of selling your information.

Brian Neiman:
Technology also helps to decrease the administrative burden. So the uptake of this, the number of individuals that have signed up to Nebula is well into the 1000’s so recruiting is very quick. Technology offers the ability for more secure information transfer. I already shared that with you. And then the potential to deliver strong patient network effects. So that essentially means when people participate and then they add benefits, they will create other benefits to individuals to invite others into the platform such as giving out other tokens for more people that are invited in.

Brian Neiman:
So we spent about a couple hours at one of our last board meetings discussing all the implications for our business, the biobanking and engaging patients day to day. And this is what we came up with is, will big pharma companies and research institutions really purchase tokens and join the marketplace network to purchase data? You know, what does that look like? What does the master service agreement for something like that look like? What are the hurdles and regulations regarding patient rewards and monetary incentives? This is kind of like a stock which is completely different than Visa gift cards that we’re all used to. What types of incentives, tokens, revenue share will really drive patient engagement.

Brian Neiman:
So at the separate conferences over the past few years, including those at patient advocacy organizations, there’ve been topics about revenue sharing. And so is this a way of getting into that sharing revenue for… Let’s say a participant participates in a trial, and kind of moving away from biobanking for a moment, for an individual that participates in a trial that helps gets a drug launch. Is this a good way of incentivizing those people because they actually put their bodies and health on the line to participate and actually put a drug into the marketplace?

Brian Neiman:
Is blockchain cryptocurrency technology really needed? Is it just a buzzword? Are there other software mechanisms where we can actually achieve security and privacy and utility without having to turn to blockchain and crypto which the world isn’t really familiar with just yet. And is healthcare pharma industry currently open to blockchain and crypto? Are patients? Is it too early? The questions I like to ask myself as my father is diagnosed with a rare condition and he’s actually participated in several Sanguine studies. And the question is, is he equipped to go on his phone and trade cryptos? Is this really a good fit for him? So you really have to think about the audience that you’re working with here. Some things I maybe invoke today for audiences that are really interested in what’s exciting or du jour, and what that’s going to look like for other populations afflicted with different conditions.

Brian Neiman:
So I’d like to open it up to questions. Thanks for taking the time to learn more about this aspect and give me your time during lunch in the Miami humidity.

Speaker 4:
So that’s fascinating. I learned a lot. So in general, are screens or filters put in place to to ensure accuracy of the data? So, for example, since they’re coming directly from patients, you use lupus as an example. So lupus, it’s misdiagnosed at an enormously high frequency both over and underdiagnosed. Many patients whose medical records say lupus in fact don’t have lupus. And those who are diagnosed with another disease have lupus. So just in the auto immune world, there’s so much misdiagnosis. So patient surveys, or depending upon the source of the medical record, whether it’s a PCP or a lupus expert is going to be highly variable, whether they’re really legitimate data that you can base discovery on. So what’s the space around that?

Brian Neiman:
The question was regarding the verification of diagnosis, given the variability of diagnostic practices by different physicians-

Speaker 4:
Just one example. [inaudible 00:26:59] just data in general coming from a patient and I’ve read patients like, there’s a lot of garbage in there too. So, how do you sort out-

Brian Neiman:
The signal from the noise. Yeah. I think that’s an excellent question. That’s one that we’re seeing every single day. I’ll tell you how we’re doing it. So at Sanguine we engage many patients diagnosed with various conditions across the social media patient advocacy groups, what we do is we collect their medical record on their behalf from their physician and we ask specific questions over the phone. So we have specialists that have done many lupus studies on their own to ask specific questions about the condition. And then many cases we ask the patient for proof of diagnosis in the form of let’s say taking a picture of their pillbox or their medication or prescription history showing that they’re actually taking that drug because there is that fact as well.

Brian Neiman:
The other companies, I’m not sure. I think that one of the risks in engaging individuals over the web exclusively, not over the phone or other forms of verification. I do think that that’s going to be an issue. What we’re doing, what we’re spending time on over the next year is coming up with a verification process at the company that’s above and beyond what we currently have to increase that level of confidence. Because you’re right, there is a lot of noise out there. We don’t know what it is and I think a lot of companies are going to have to face that next year and the coming years. Great question.

Brian Neiman:
So the question was lack of completeness of the data sources out there and if cryptocurrency can help bridge the gap in terms of the completeness of the data. So let’s break down what the ways that the cryptos are being used. It’s a utility in terms of sharing, privacy, security. So that’s one aspect. So it could make people feel more comfortable in terms of sharing information. That’s one. Two, depending on where medical records go, there’s conversation about incorporating blockchain into medical record systems to see who opened it for HIPAA privacy reasons. So that’s one aspect in which we can increase. Then depending on which DNA sequencing provider you go with or genomic testing provider, there’s an opportunity for them to use blockchain. So I think depending on the connection between those companies that blockchain can potentially bring in, we can see more completeness of data based on the increase in security and privacy presented by those new technologies in the space.

Speaker 5:
I have a question on the whole system of the token purchasing distribution. Do you think there’s a role for an insurance company and if they are what they be?

Brian Neiman:
Well, that’s a good question. So let’s think about the constraints for an insurance company. So the constraints are that obviously it needs to be anonymized. Yeah, I think it needs to be anonymized. I think there is an opportunity for them to participate, but obviously the legal implications of [inaudible 00:30:14] and other aspects like that might keep them out. I think that if there is some utility first on the pharmaceutical side from an entrepreneurial standpoint, if there can be returns generated from pharmaceutical research or by technology research, then insurance is going to be the next path forward. I think that the insurance companies are more likely to be involved as it relates to their own population. So I think that there’s going to have to be a solution out there that’s more tailored for their existing population management. Great question though. Lots to think about.

Speaker 4:
So I don’t know the answer to that, but I think there are federal regulations that prohibit insurance companies from selling your data.

Brian Neiman:
Yeah, so I think that… Of course that’s a GINA. So not even selling, but genetic information nondiscrimination act is, and we all know is that the insurance companies won’t use that in terms of their insurance decision. But in terms of data aggregation in general, there most likely will be an opportunity if that information is anonymized appropriately in the future there will be opportunities for insurance companies most likely to participate. I mean, we see that with Optum Health, correct? So big data and health insurance already exists. Blockchain is just another way.

Brian Neiman:
So let’s think of it this way. The best, the most profitable health insurance companies in the world today are the ones with the largest data sets and the ability to crunch that data the best. So let’s think Optum Health for example, right? Starting off with all that Mayo Clinic data, that’s the first example. Blockchain just presents another way to more securely and essentially more quickly aggregate patient data. So if you can aggregate that information as fast as possible, then yes, the insurance companies can be a buyer if the information is anonymized or what they could do is pay the blockchain companies or the patient engagement companies a fee for conducting the analysis and insights on their behalf. So they might not even get access to the data, but they may pay for the analysis to be done.

Speaker 6:
Question. Is the population that tends to opt into some of the service like this, is representative of the general population, or is it skewed one way to another?

Brian Neiman:
That’s a great question. What we’ve seen is what we call the quantified self community. So individuals that are measuring everything about them, biometrics, wearables, things like that. That’s been the largest group that’s been interested in this.

Speaker 6:
And does that tend to be the healthy group?

Brian Neiman:
We think so.

Speaker 6:
Yeah.

Brian Neiman:
We think so. Yeah. So, to the point of how valuable is this data for specific conditions and is it just a data set with largely of healthy individuals that doesn’t really tell us much. Well, thank you so much for your time. Appreciate it.