YouSaid · the spoken record

Sonal Chokshi

lines on the record: 66
first: 2019-01-16
most recent: 2019-01-16
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Actually, hearing all three of you say the same thing in different ways, which is it's about empowerment. And if you put the P in power, and that should be the P in HIPAA, actually, not just portability and patience, but that's what we're talking about, is empowering the patient. Thank you guys for joining the A6NC podcast.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Because it's a solve for me. And I have the protocol that I follow. And it's a chronic condition and the clinical establishment has said you don't have a brain tumor, go home, you're fine. But I still haven't solved my migraines. So that's where peer-to-peer healthcare, which Suzanne has been promulgating for a long time. It really, that's the outer circle that we don't know how that will be shaped. That's an ecosystem play that we're very excited to help power.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“I love this idea because there's going to be two buckets. There's going to be the very acute, highly clinical bucket of information. And you want a license professional to weigh in and a quarterback. But I, as a migraine sufferer, I have shared a recipe I have with whoever will listen to me in the moment.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Can happen around health data because we don't yet have access to it. Again, we're leaving half the team on the bench by not giving patients access to their own data, much less access to each other, which I think is really going to unleash well-being”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Because I'm really passionate about looking at the expertise that patients really can have. So there are expert patients out there who really know their disease. They really know their condition and can bring that expertise. And what I want to see is that everybody operates at the top of their license. And by the way, I think patients can operate at a pretty high level. And so, yes, there's a danger of Dr. Google. Yes, there's a danger of amateur pathology. There's a lot of amateur dermatology. But there's also the possibility of everybody being educated, everybody raising their game in healthcare, including patients and caregivers. What I would like to see is an ecosystem flourish around the possibility of access to industrial strength health data so that we can see, we have no idea what's going to happen. We have no idea what kind of engagement”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“For many people, the joke is they go to Dr. Google. What does 300 cholesterol mean and something like that? And I think the opportunity here is something much grander. If you finally have the data in a portable way, you can actually just ship this off and then get information from a real doctor who now has everything all in one place. I'm going to disagree.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“I wouldn't say that patients like me is folksonomy. I would say that they are a serious taxonomy of people's own tracking. And when I think about industrial strength health data, I think about the data that is currently being held by the clinical system that most people don't have access to. And when people get access to that, Where are they going to direct it and the choices that they have?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“I agree. It makes me think about the Google doctor problem, the Googling problem where patients think they're doctors because they're informing each other and Googling things and it actually creates more problems for a lot of doctors in that space. So when you say industrial production side, it to me means taking that data and putting it in a more rigorous system than one that's just so informal folksy.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And patients like me is a great example. It's like a time machine that you can travel backward and forward in your own record and in other people's record. And what's essential is that people are creating this small data for themselves. And what is the opportunity is to create an industrial strength version of that.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, this actually reminds me of a web siren that I used to be obsessed with patients like me. I loved it because it's essentially like the long tail of the internet to find like-minded people suffering, whether it's dysfunctional uterine bleeding, which is a weird category or there's like a million things that you don't know. You station tubes collapse. There's a million specific things. I love that aspect of people being able to center and create community around.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“When we think about the patient at the center of the circle and how all the big players are sharing data around the circle, and for them, the most useful share might be to a community of fellow patients. It might be to create a longitudinal record that they share with other people who have lupus, other people who have cystic fibrosis.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So here's the dirty little secret about big data and healthcare. There isn't any. And the solve to big data in healthcare is small data. That means what Susanna said earlier. Where's the other 94%? It's in the imaging systems. Of course, images are large, so there's a lot more data. It's in the microbiome. It's in the genome, whether it's the full genome or just a portion of it. It turns out that the electronic health record systems weren't built to handle all of this other explosion of data. And even if there's a consolidation of vendors in the marketplace, which we predict there will be, and I think that's going to be a good thing, the consolidation is going to be outstripped by the runaway fragmentation in digital data. The only person who is legally, ethically, morally incented to pull it all together is the patient.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So, what I'm hearing overall is a theme here is that when you get the horizontal data that connects all the players in the healthcare system that can now communicate to each other through the patient at the center, and then there's a vertical piece, which is the history of the patient and the past moving forward, et cetera. Now, this gets us to the idea of big data because now you have a lot of data to work with. We've been talking about the canary in the cold mine. We've been talking about all this stuff that people can do on top of this data. Honestly, it's a buzzword I hear all the time, like big data and healthcare. What's a big picture here on that front?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“The other thing with chronic care is that because it's such a longitudinal time horizon, an 80-year-old person, if they had their entire health record, there's no single institution that would keep the record on file for that long. So it turns out that patient portals and APIs give you a smaller window than an 80-year-old history. Some of the information available is aged out over a couple of years. So you can't rely on a single interface to keep all your data. One, because you're getting treatments every other place. And two, because they're not responsible for keeping it. And so it becomes incumbent on the patient to manage at least the longitudinal aggregation of the, if not the interpretation of the data.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Beautiful. And actually, in a sense, there's sort of centralized versions of that with the cystic fibrosis foundation with vertex and so on. But you're talking about it more like pop-up crowdsource. Yeah, something where that you don't need the army there that you could band together.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Think about the current state of coming up with new therapeutics, new drugs, the cost of clinical trials is really high, and too many things fail, and there's different reasons for failing. One reason for failing is actually not getting the right patient cohort and not designing the trial to run such that you would have a successful outcome at the end. And that's really a data problem. And the opportunities with that is that more drugs could get through. And even certain things in principle could even be rescued. That would actually radically shape how these therapies get to market.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“The other thing that people can use their longitudinal health history for is to participate in clinical trials. Most clinical trials upwards of 90% or worse don't get filled. And it's because there's no frictionless way for a clinical trials inclusion exclusion criteria to detect a candidate patient. But if you have on the one side a digital health summary in cyberspace, on the other side, you have a inclusion exclusion. Let's say matchmade in heaven, and we think we can move, really move the needle for both pharma who wants to detect patients and patients who want to be matched up with targeted clinical trials.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, it's actually a lot like new moms, and the pediatricians always telling them don't worry about the normed curve, track the kids curve because you just care about them growing and gaining weight. But otherwise, a new mom's lose sleep when their kid is in the 25th percentile and so it's a 75th percentile. It goes to that same.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“There's been really some classic examples recently where the beauty of having time series data is that you're comparing you against your previous self. Unless you have that time series data, all you can do is compare you against the population. And people are just so different and such high variances and overlapping distribution. The well-known example recently is, and Stiller had prostate cancer. But his PSA level actually never went high from a population standpoint. It just went high from his own baseline.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Longitudinal Dale is going to become ever more important as compared to episodic data. And that's because we're moving from acute care to chronic care. If we had a pillar or procedure, we could do something in a hospital. So that's an acute episode. The fact that it's chronic, the condition is chronic means we don't have a pillar procedure. It's going to take place over a long period of time. And people are increasingly mobile. So their health data portability problem is exacerbated as we go forward unless we address it now.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So all of these diseases we've been talking about so far, cancer, lupus, autoimmune disorders. I have a chronic condition, nothing to worry about, not to scare my listeners, but in the context of I have to see a doctor regularly, et cetera. These are all cases where you have multiple touch points in the system and often longitudinal data helps. How would the longitudinal data and having a patient at the center now, what does it do up to them?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, there's different issues with the opiated crisis. One is that often the first intervention is opioids while you wait to see the back surgeon or musculoskeletal surgeon. That's an accessibility issue. And then once that starts, then the problem is that the opioids are more accessible than the other solutions. And so now you have people sort of doing doctor or opioid arbitrage. Between places. And so, hopefully, we can understand first why this is happening and then why it starts to sprout and then what's facilitating it. And having the records in one place would do it. I think this is a little challenging because if it's driven by the patient, the patient's going to want to have to.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Reimbursed or not reimbursed for pain management all across the world, pain is managed using therapies other than drugs. So there's all kinds of ways here in the United States we encourage Development of drugs and through a complicated history, which I won't get into, it became more the norm in the fashion to reimburse for drugs, to prescribe drugs for pain. Sort of alternative therapies like TENS and various other things. Exactly. And so how does data enter into this? Data can tell us when a crisis is happening. It can show you also where we're actually gaining ground. We're seeing that we're gaining ground in Ohio against the opioid crisis. Data is telling that story. So that tells me how it informs public health and people thinking about this, but how can something like what we're talking about where this dark data unveiling actually solve? I'm not again trying to advocate for solutionistic view. It's a larger, bigger problem beyond technology, but how can it help?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“We started seeing signs of the opioid crisis in death certificates. And it was actually public health researchers who started looking at data and seeing, wow, we're really seeing an uptick in this sort of death among young people. Addiction to opioids? Well, the problem was that it was unstructured data. So people started looking at the death certificates and started understanding what was happening. What I'm passionate about is learning lessons from the past so that in the future we can take the temperature of the country more accurately and more quickly to solve those problems. How might we create a dashboard for the country so that we see something like the opioid crisis happening? Everybody played a role in the opioid crisis. The pharmaceutical companies played a role. Public health agencies played a role. Payers played a role. Why did payers play a role really quick? Oh, because of the way people were being”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“The opioid crisis is one place where data is the canary in the coal mine. It is an early warning system where if you digitize the data, you could literally have an Excel function, and I'm trivializing it, that is scanning for populations, and where it sees a concentration.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So, speaking of something very American in a sad way, because I do agree with you that permissionless innovation is incredibly American, is why I'm a capitalist. But a sad reality of American life today, especially something that we talk about a lot in healthcare, is the opioid crisis. And I, for one, would never, ever say something that can be solved through technology alone, because it's a socio-cultural problem. But in this context, how would something like this play a role in a public health crisis like the opioid crisis?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“A country of rugged individualists for good or for ills, right? So a lot of healthcare depends on whether you have the wherewithal or whether somebody in your family has the wherewithal. And we as a country win when we make it easier for everyone to participate.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, the analogy that comes to mind for me when I hear that is I'm a big historian of the history of computing tech, internet, and one of my favorite themes is the idea of permissionless innovation. And what I love about this is what you're describing, because if you think about what happened with the internet and permissionless innovation allowed people to build on top of the platform that is the internet, if you think of data as a platform and what people can build, you cannot predict the use case, they're second order and third order effects that nobody, the designer of a system can never predict up front. So what I love about this is this is permissionless innovation in a permissioned way where the permission is actually coming from the patient because essentially the patient is saying you have my permission to move this data around this portability. And then to your point, who knows what that can unleash? And that is a really exciting thing.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“And yet, they have so much insight to share. And we need to make sure that we are pushing the power out to the edges of the network. That's where expertise lives that we don't even know about. We don't yet know what will really happen when we free the data and allow people to create the dashboards that they really need. We don't yet know how different patient groups are going to create something really useful, how an entrepreneur is going to look at this opportunity and say, I could create something that really helps people. And by the way, it could be a small group, but have a significant impact.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Data is fuel for an ecosystem. And so when we talk about the structure of the healthcare system as it stands now, we talk about pharma, we talk about the payers, we talk about hospitals. But a big part of this are the patients who don't appear on anybody's organizational chart.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, definitely. And this is useful for pharma, but frankly, it's also useful for pears. In a world where things have gone past clinical trials, but really now the new barrier is not getting past the FDA. The new barrier is reimbursement. The payroll want to know with real-world evidence, like, is this really helping? And this is something that you obviously can't answer without data, and you can't answer without the right type of data structure in the right way.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“There's so many different ways that data can inform us. So just one off the top of my head is that if you're a pharmaceutical company, you want to be able to understand how things are going with patients, maybe in a clinical trial, maybe in a baseline, and you need to be able to get a large number of patients that are the right ones. So this is not necessarily 100 million people. This is maybe thousands of the right ones that can give you the day you need. And what I think we'll start to see is, especially as new statistical methods come online, that real world evidence will be very useful in some ways will be more useful than what you can do in a clinical trial, both due to power and due to the fact of real life is different than a clinical trial.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So tick me up in level then beyond the individual patient experience and talk about this at a structural healthcare systemic level. What does this mean for insurers, for hospitals, for researchers, for clinics, drug testing? We have to blow the ocean here, but how does this play into that?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“That's going to be key because a doctor does not have time to sort of go through pages of things to understand he or she wants to be able to go very rapidly, just get a sense of the lay of land, and from that say where we are and what we need to do. And I think we talk about friction. There's friction in each of these levels. Friction at the patient gang, the data, friction at sort of what the data is, and friction at the doctor side. I think the ideal is to reduce all three.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So, you know, one of the things that I think gets sort of covered almost too fast is this unstructured or structured, because structure could mean lots of different things. Really, like if you can go all the way deep with ontologies and a true semantic structure, you really go from just words to understanding. And understanding is like the holy grail for machine learning and AI right now. It's a hard thing to do. And when you can finally use ontology and other things that people have driven, now the data actually really becomes useful. And I think Excel is a very natural one. And the key thing is that it's not just even in a spreadsheet. It's in a spreadsheet where a computer actually knows what each of these things mean.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, but we'll take it from here and we'll run a calculation that might be a cardiac calculator, or we'll aggregate populations together and do a population survey. Or ideally, because we can automate clinical trial inclusion exclusion, you could have a series of algorithms running in the cloud all the time. And every time a patient becomes a citizen in our parlance, a trial match can be detected on their behalf. That's the way we reduce friction, as Vijay said.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Correct, then the final question is, so what? Now I have my structured data. What can I do with it? Well, you can certainly share it like you share a LinkedIn profile with your physician. That physician he or she might say, thank you, God, for bringing all this because I never get to see this sort of back. But beyond sharing it with people, you can also share it through an API, as Susanna has said, to other app developers who can then say, look, citizen, you've done a great job producing this fuel, this computable from your friends.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“You're basically taking unstructured to structured, but you're actually adding a step even before the unstructured, which is making that data machine readable in the first place, especially in the case of a PDF.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“This really is machine readability. And a lot of the cancer information is in the pathology report, which doesn't come out of APIs yet. We got our fingers crossed. So we need a long pole in the tent about a data refinery that takes this crude oil of documents and converts it to data. It's like turning a Word document into Excel. In Excel, we know how to apply a feature or a function and operate on it.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, absolutely. And they can help other physicians by being part of the referral network because patients are in the middle. The second thing is most of the information that's released is not in any coded form. It documents and PDFs and XML files. This is the darkness.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“But the three things that we have found that we absolutely need to solve for patients is lower the friction of three touch points. So three long poles in the tent. One is how does someone actually request their records to be released? And it should be eventually something as”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Her big brother was in the industry, so of course Tanya used Glimpse, which is a former company. Apple acquired it in a transaction, and some of the health records that we see them releasing it to the world is some of that technology. And so she had the ability to share her profile, her portable health record with all her meds, all her labs, all her genomic information. What we had done is we had built a depth of health record that could operationalize her cancer care. This is in contrast to others who think that a mile wide and an inch deep in data is the way to go. We actually think in order to operationalize health data, you have to find self-incented people, and that is cancer patients, lupus, HIV, autoimmune disease. We found that we needed to add imaging and genomic, which Glimps didn't have. We want to democratize that across all 7 billion people.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Every time she went to a new oncologist either there was a restart of a frustration factor where she had to explain everything. But because she had her electronic health record, imagine a LinkedIn of your health profile.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“So the question we get asked is what will we do with it? And the answer is, you'll be able to share it with an oncologist who will know how to operationalize that data and help treatment. For folks who know me, my little sister contracted a number of years ago, late stage metastatic breast cancer, diagnosed straight into stage four because someone missed the diagnosis and blew it a years earlier when it was stage one. So the point is, in her last year of life, Tanya was seen at 14 facilities all using, not all, but many of them using different EHR or medical record systems across multiple states, which have their own transmission of health data across state lines issues. And she was deemed by 23 oncologists. So that sets the groundwork.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“In the case of wearables, you're dealing with something that's very broad and very shallow, and also often not very clinical, like 10,000 steps versus 9,000 versus 11,000, that's largely just made up in terms of its clinical significance. Maybe let's turn it upside down, which is what are the clinical areas where you have clinical data that could create actionable outcomes for patients? Maybe you start with what is the greatest need? Maybe that could easily die in a year or two. People that have mid to late stage cancer is an obvious example. And there's data they have from numerous different areas, and imaging and genomics and all of that. And it's just kind of amazing how much data there is now to get. And then another question is, how do you gather that and how can you empower the doctors via the patients to actually make a difference?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“What's the impact? I do want to talk about what you can do with it now because going back to the original question of do people really need their data and does it really make a huge difference? I'm thinking of the analog of what happened with wearables. People have long talked about wearables. And just like the analogy of the car, there are lots of sensors out there measuring our bodies. But the number one problem with wearables and data is nobody actually takes that as actionable data and does anything with it. So it's a case where you have a lot of data just gathering that's not being used. Are we talking about the same problem here?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Once you realize that the patients at the center of it, you realize there's a huge opportunity there. If companies or startups empower the patient, then the patient can drive this whole thing. They can quarterback if they can ask for the data. They can send the data. Now the question is actually, what can you do with it and what's the impact?”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, taking your circle, most of the entities are sharing with each other, but not through the patient. So the longest way around the circle, from one side to another, is around the circle. The shortest path is through the center. Yes. That's the diameter. That's where the patient is.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“This is a very important idea. If I were to visualize this structurally and you think about all these players in the healthcare system, you have insurance and hospitals and clinics and universities, and I could keep going pharma companies, et cetera. If you picture this whole ecosystem as a circle, right now what you're talking about is putting the patient at the center of that circle, and therefore they can pull on all these pieces. But otherwise, these entities cannot necessarily talk to each other directly with the patient's data.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source
“Is correct. Patients are actually outside of HIPAA. So you can ask as a patient a HIPAA qualified entity to do something for you and share your information, whether it's through an API or whether it's through a PDF or email, and they have to do it. Most people don't know. They have that much flexibility.”
2019-01-16 · a16z Podcast · a16z Podcast: Dark Data in Healthcare · IDENTIFIED FROM THE TRANSCRIPT · source