The Future of Digital Health
Learn about the major trends, biggest investments, and key players in Digital Health, courtesy of CB Insights
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Let’s end with some more open-ended questions:
First off, who's going to own this data? If it’s generated by the patient, should I own it? But since the data is touched by so many different organizations like your EMR system or your provider, your insurance, the question really has to be asked, who's going to own this data and are patients going to have to consent every time it's sent? And the reason is because that patient data is extremely valuable since it's so personal.
Below is a transcript of our webinar, "The Future of Digital Health," led by Research Analyst, Nikhil Krishnan
Now this Data 2.0 movement is going to continue for a few reasons, but I think one of the areas that can really help is by reducing hospital admissions. Like we talked about before, the shift for fee for value medicine rewards quality outcomes rather than the number of tests run which we used to have in the system previously. But now hospitals are actually incentivized to keep patients healthy and out of the hospital. One part of pushing that initiative is actually penalizing the
QUESTION & ANSWER
Does BlockChain apply to digital health?
I think this is a really interesting question. I was just reading about this the other day. One of the things that's very interesting about BlockChain is it can actually help with a very crucial issue that's happening in healthcare right now, which is creating a patient identifier number, so basically help coordinate with data as it comes from a lot of disparate places without any sort of unique identifier. But the BlockChain can actually help with that by using distributive ledgers and basically identify transactions to monitor the movement of healthcare data as it moves through the system.
How can we know that the data being cleared by these devices is accurate?
We talked a little bit about this before, but I think when it comes to medical-grade technology, the FDA process is a real necessity to make sure that the data capture is accurate, but also that the data that's being collected is actually useful in providing better care and improved outcomes. Some of the wearables out in the market today, which haven't gone through FDA regulation, can sometimes provide a wide range of readings. There have been some reports that certain wearables like to flatter their users and basically give them an edge towards more beneficial readings. But there are also some wearables that are now being analyzed in peer-reviewed journals to determine their accuracy. You can take a look to see if the devices you're talking about are in any of these journals.
What about cost transparency data and how is that going to affect the system and consumer choices?
I think that's a good question and it's one that I really didn't have time to get to in the session, because it's sort of a topic in itself that could have its own webinar. But cost transparency is definitely one of the biggest drivers in the shift from fee for service to fee for value. And it gives a lot more responsibility to a patient who can now do things like a price comparison from insurance or understand what procedures cost what, where. I do think that cost transparency will block some issues like the fact they were not going to be doing price dropping when they're in an emergency situation and the fact that there still aren't great ways to assess quality of life post certain procedures. I think that's a good question. And I think that cost transparency has generally good intentions for the healthcare system. It definitely has a lot of important implications and not all of them are positive.
Why do you think pharma has been slow to adopt that on digital health?
I think one is because pharmaceutical companies are extremely large. And large organizations, as a whole, are generally much slower to change processes that are current and they are investing in digital health overall. A lot of the bigger ones are still very profitable. In terms of digital health adoption, I'm sure it'll happen in the future as they need to increase efficiency and once they see the viability of reducing things like FDA clinical trial time. I think it will happen. Others think differently so, because as a whole, they're much larger companies.
And what should be the next step so that we can better utilize this data? Well we need larger longitudinal studies to really assess the validity of these gene markers and we need to do it with a more diverse population, since right now it's limited to people that can afford it, have it available to them, and are active about getting their DNA sequenced. This will change as we lower the cost of gene sequencing. But as we do that, a lot more of this data might be created.
Digital health is a huge space, so today we’re really just going to be drilling down into what's happening in the data side of healthcare, especially in the U.S. We'll only be talking about things like regulation, insurance, changes in processes as they relate to data itself.
We'll start about the activity that's happening in the private markets in digital health as a whole to get a sense on how the industry is doing and where the activity is happening. Then, we'll move from
Looking more granularly at the stage level, we saw a wrapping up of seed investment going to 2012 as excitement in the space increased. And since then, the deal share has stayed relatively the same with early stage deals taking the majority of deal share. And unsurprisingly, California has seen, by far, the most activity. But interestingly, nearly every state has seen at least one deal into a digital health company since 2010, and 15 of them have seen more than 50 deals.
EHR/Health IT Beginnings
A good way to get some context about why this is important is to take a look at the private market trends. We use CB Insights data to track some of the trends in digital health, which we consider to be any software solution that's used to improve the efficiency or process within healthcare. As you can see here, there are record number of deals with dollars invested last year was more than $5.7 billion invested across 889 deals to the whole industry. It's really a trend going up into the right.
The most active investors (see image below) in the space come from pretty much every different side. We're seeing accelerators like Y Combinator and TechStars, tech corporates like Qualcomm, healthcare corporates like Merck and also general venture capital firms like Lux Capital and Norwest Venture Partners. All this is setting the stage for how big a deal health is right now.
But let's get back to the issue of healthcare data. We're going to start by talking about some of the macro trends that are happening and then we'll dive more specifically into each part and how healthcare data is changing.
One of the biggest shifts we're seeing is the empowered patient. Thanks to a shift to fee for value medicine and healthcare, which is putting outcomes and quality at the top of the priority list, patient care and experience have become a very central focus of the industry. And not only that, but new tools are letting patients generate their own data outside of
Precision medicine is a phrase that's definitely picking up speed in healthcare. These things are things like individual level data from gene sequencing and tracking devices. Because of those data, those sets of data, we can actually give a much more personalized individual level of care now that we know more individual data about each patient. And by doing that, we can actually increase the population health objectives by focusing on each individual, one at a time.
much more into some smaller pharmaceutical companies rather than investing a change in process itself.
Something I'm going to talk a little bit about is the lack of interoperability, and that means the ability for data to move freely across health systems. And that lack of data flow is largely thanks to a broken EMR system, an electronic record system, which profits from data moats and lacks that ability to process real-time data. But there's some promising news in the future, thanks to new standards, which will hopefully allow us to understand more as we conduct analysis across a wide range of data types.
And Watson's powerful analytical capabilities are already being used by a variety of large players, including CVS which is using predictive analytics to identify risky patients, large drug companies like Sanofi so they can better understand the effects of their chemical compounds, and Johnson & Johnson to have their own virtual AI coaching and rehab program. They're also powering the genetic analysis for the New York Genome Center. And it's not just big companies. They're also partnering, empowering some of the smaller private companies involved in a lot of different types of data from genetic to EMR to personal health records.
Now we'll start getting into some of the specifics of the different shifts that are happening. To do that, we'll start with the EMR, EHR systems, and the digitization of health records. A little backstory here, the HITECH Act of 2009, which was part of the post-recession stimulus package, was basically designed to bring hospitals into the modern era of tech, and gave hospitals and doctors a reason to start using electronic records instead of paper ones that they were used to before. And then
I think something really exciting that actually happened very recently is that Andy Slavitt, who's the current chief of the Center of Medicare, of Medicaid services, has basically acknowledged that the concept of meaningful use needs to change. Rather than measure the success rates on the literal number of people that are using and interacting with EMRs, we need to start looking at things like actually improving the quality of care and the outcomes. Andy's tweetstorm that I took a screenshot of is clearly placing an emphasis on doing this by opening the EMR APIs and promoting interoperability as a whole.
THE ROLE OF TECH GIANTS
And this is some exciting stuff. As we see here (to the left), Carl Dvorak, who's the president of Epic, talks about why they decided to be helping with FHIR, which is basically that innovators work with the data to improve workflows are already trying to work with. And several other executives from a bunch of different EMR providers have actually expressed similar sentiments to this.
only that, but a common complaint is that physicians now spend a lot more time looking at their computer instead of the patient. And if you've ever been to a new doctor recently, you can probably attest to that. The rollouts for these systems were very long and expensive and that only increased the switching costs even more. And finally the UIs are incredibly non-intuitive and unnecessarily complex. I don't know if any of you guys have ever seen the face of an EMR system, but there’s definitely something left to be desired.
WHAT DOES THE FUTURE OF THE EMR LOOK LIKE?
And why is that? Well it's because when the EMR roll-up was designed, it was really just designed to make physicians start using the EMRs but didn't really have incentives for interoperability, and therefore the software providers created data moats to keep customers from switching. And not
And finally, we can make sure that critical information like lab results, past exams are not missing from patient records. When doctors take a look at their patients, they'll have a full and really holistic view of what's happening with them. And that's what it should be like in theory. But if you talk to any doctor about their EMR system, I'm sure they'll have very different sentiments and will probably have a few choice words about how they feel.
And we could decrease the number of patient safety events. Things like medication errors or complications due to misinterpretation of information. Here, in one of the analysis that I took a look at, which analyzed an EMR system, patient safety events not only reduced by 15-20% leading up to time zero, which was the time the EMR is adopted. But after it was adopted, we're seeing a downwards sloping curve, which suggest that the EMR system is actually helping prevent further safety events.
And the missions of EMRs was a good one. Ideally, we move away from paper records, like the one you see to the left, which has some legibility issues, that's a physical copy that can only be accessed physically. Plus now things like alerts and reminder functions, can help keep doctors aware of what's happening with their patients as a whole. Not only that, but they can actually, in theory, promote communication between doctors and their patients, but also between doctors themselves to improve coordinated care for a patient set might have to see multiple specialists.
These are some of the companies that are already doing condition-specific monitoring that I talked about. One example is Ybrain who's monitoring brain activity for neurodegenerative disorders or iRhythm which uses ECGs to measure arrhythmias in real-time.
We're also seeing wearables for doctors, though not a ton of activity, there are some companies that do some pretty interesting use cases like Augmedix which utilizes Google Glass and basically
pushes patient info to the glasses or Simplifeye which uses the Apple Watch in hands-free situations.
Another huge set of data that's being created comes from the form of at-home diagnostics for patients, which is a market that continues to grow. And there's some clear benefits to diagnostic tests in the hands of patients instead of just doctors. For one, we're seeing that there's a lot more data
the original wearables users.
points being created as a whole and that can help avoid things with mis-measurement that might happen due to only one data point which might happen due to a fluke or circumstance. And also the measurements can actually be taken at a variety of different times instead of just at the hospital, so for example, at night or after meals.
The first iteration of wearables basically targeted general consumer markets, since they were much larger and since they were only providing health monitoring, they didn't need to go through FDA regulation. But now, those markets are becoming saturated. We'll probably see more wearables go through the FDA process and create data that doctors will trust more and devices that they can actually train their patients to use. And finally, an underfunded space is wearables for physicians themselves, because they're
What are the issues? Why does it have such a low penetration rate? Well, 24% of people say that wearables are too complicated to use, and within the products that actually have the larger market share, so companies like Fitbit, there's been a noticeably high churn with people engaging with them less and less. And importantly, there's a variation in the actual measurements that are being used across these devices, thanks to different techniques that are being used to capture the data and really just a lack of standards that are in place.
But luckily today, that's changing. Thanks to the decreasing cost of sensors, we have a whole host of devices that can now provide us data in real-time outside of the hospitals. And we'll dive a little bit more for each of these going forward.
First, we're going start with wearables. Now this is probably one of the hottest concepts in the list in the last few years. And when we take a look at the funding trends, it's clear that the hype grew through 2014 but since then has slowed down a bit in 2015. And despite the hype, wearables actually have a pretty low penetration rate in the population.
All that brings us to the next point, which is that this rise of this patient-generated out-of-hospital data is happening, and that's a big push into then dynamic health records we might see in the future. Again, a little backstory, I took this snapshot from a 2003 study about data collection and analysis, just to get a sense on how it used to be done before. And mostly it was done with techniques like self-reporting, observation, and pulling data from disparate data sources.
Only 8% of people who are surveyed are actually currently wearing a fitness monitor and only 6% of them were wearing one that was related to health. Though the projections are, those percentages were going to get much bigger in future.
You can find more private companies like that in the CB Insights database itself. But I do know that in part of the Cybersecurity Act of 2015, which was passed very recently, the Department of Health and Human Services was tasked with basically creating a task force to understand what security safeguards are working in other industries and then report back about how secure our current health system is and ways that it can be improved.
And once the data moats actually fall, the EHR vendors are going to have to differentiate themselves based on things like good user-centered design rather than a snapshot of what you see here to the left.
Rather than actually be judged by the amount of data they have, vendors are then chosen based on usability and the analytical tools that they provide.
communication and record sharing among relevant parties. And on top of all that, Qualcomm Life is actually powering more than 109 different organizations with these different offerings and this chip technology.
Finally, once the records are actually easier to access and the data starts flowing in more real-time, we might actually see the takeoff of personal health records, which historically have been pretty unsuccessful ventures and have been tried by multiple of the big tech giants. But once the PHRs actually become dynamic instead of static and much easier to access, we'll probably see a lot more people actively monitoring their health. As we see from this survey published taken by Rock Health, the interest is definitely there.
It recently passed FDA regulation and offers a $200 home-testing kit. But now we're seeing funding into companies like Spiral Genetics which are applying big data techniques to help make sense of this new data. And that kind of data analysis is very tough and requires a lot of processing power, sensors, and a whole host of other tech-related components. And that's why tech giants see a huge opportunity in the healthcare space. We're going to just take a little bit of time to talk about a few case studies in some of the big tech companies and what they are doing in healthcare.
Now, knowing all that you know, what are some predictions for what might happen to the future EMR systems? I think when the APIs finally open up and we finally achieve more interoperability between these separate systems, we might see a much more flourishing ecosystem get built around these new data streams similar to the third party applications that are built on technology platforms like Salesforce or Slack. These are some of the companies that I found in the CB Insights database that are already working with
And what can we do with this genetic data? Well some applications that we're seeing now include things like understanding the likelihood to develop conditions in the future and taking active measures to prevent that. For example, we're slowly understanding that certain gene mutations are linked to certain cancers. People with that mutation can now more actively monitor themselves or they can get preventive surgeries.
EMR data in some way, shape or form. There are other doing things like improving care coordination or providing actual recommendations and doing quite a bit more.
One of the most promising ways that's happening is with the implementation "Faster Healthcare Interoperability Resources," or FHIR. FHIR is a standard for data exchange and that's not only going to open up the EMR APIs, but it's also going to allow data to be exchanged in real-time rather than now where it's a lot more static. And actually some of the biggest EMR providers are helping with the rollout of this, contributing to the Argonaut Project which is the name of the product that's basically helped to create the standard, set guidelines, and create the first generation API.
All that brings us to what does that mean for the future of the EMR system? It can't stay like this forever. I think one thing that's important to note is that almost 80% of doctors are above the age of 40 and are very much used to the era of pen and paper. And when you combine that with the fact that when we take a look at the breakdown of EMR sentiment, it's mostly those older doctors who are upset, dissatisfied with EMR usability and function, and think, in general, that the EMRs are just very inefficient systems that detract from the practices.
The younger doctors, the ones who are trained to use the EMR from the beginning and in general have grown up surrounded by more tech have had much more positive experiences with these record systems and believe that they're a very crucial part of medicine going forward. As more people are trained to use these EMRs and they enter the medical field would most likely see a general shift about EMR sentiment as a whole.
But the older insurance companies are not staying still about this data movement either. Here you see a quick snapshot from the CBI database on where BlueCross’s venture arm is investing, and a lot of the companies they've been investing actually deal directly with some of these new data streams.
Companies like Change Healthcare which monitors and analyzes claims data to help give cost saving recommendations or EveryMove which warns patients based on fitness goals.
Here's an example of a group that is so far focused on the clinical data. But they're highly interested and impacted by this new wave of hospital data that's being created. We're already seeing new insurance models come up which basically take a very data-driven approach to their business and claim that that's their differentiator and value add. And they're doing this by doing things like integrating wearables or doing advance population health analysis. Two of the more well-known funded private companies are Oscar and Clover Health.
And I think what's even more interesting is that the companies that are doing these predictive analytics are actually doing it basically with one arm tied behind their back, since their focus in mostly on clinical data and claims data, which is largely static. Less than half of these companies are working with a real-time patient-generated data, which presents a huge opportunity for companies and investors. In the future, companies will probably use several of these data sources simultaneously, but we'll have to wait until those data sources can actually talk to each other.
And even before patients reach the hospital, we actually might be able to help them by just keeping them out in the first place. There's a bursting area of predictive analytics that actually help us identify at-risk patients. And in a study that was done by Optum, 55% of the patients that they had identified as high risk ended up being admitted to the hospital within six months. With more data, we'll be able to intervene with these risky patients and take preventive measures to help them avoid excessive hospital visits, and save the healthcare system money as a whole.
hospitals based on the readmissions that they have. And as you can see from the chart here, that cost is going up. Mobile health data can actually help hospitals reduce this cost.
There was a study done by Mayo Clinic which used mobile health cardiac rehab program which let patients monitor their health and their lifestyle in real-time, and that group actually saw a 20% readmission rate compared to the 60% that happened in the control group. There's definitely a lot of promise in that area and a lot of ways that companies can help these hospitals.
currently more than 165,000 health-related apps in the App Store today. And what's going to be important is that we have good algorithms that can help contextualize this data, which right now is very difficult because you have all these data silos erected and interoperability is such a huge issue which we've talked about in previous slides. Right now, only 2% of mobile apps can sync into a provider system.
These are some of the companies that are taking advantage of that lack of EMR interoperability that I've been talking about, and they're aggregating real-time data and providing key analysis. I think what's important is also that by aggregating multiple data streams, these companies can actually do things like comorbidity analyses which helps understand how different conditions might affect each other, and also analyze population health in real-time.
Now that we have all these new data being created in real-time by the patient, we're getting a much better individual look in what's happening to a patient at any given part of the day. But what's happening with all that data? How are we using it efficiently? Because the worst thing that can happen is that we have this data and we basically just throw it against the wall, or in this case the physician, and they have no idea what's relevant, what they're looking at, what the context is. We need
to do a better job of basically contextualizing this data. And that's where the Health Data 2.0 movement sort of happens. And that's making sense of this data.
Now we're seeing we have much smarter ways of helping patients. In a Medisafe Study which was using more push notifications to remind patients about taking their drugs, there was a significant lift in adherence across the board, with some conditions like hyperlipidemia seeing a lift of more than 10%.
And these are the diseases that, again, like I said, really contribute to a huge part of drug adherence expenditure. And private companies are approaching adherence in several different ways. There are companies like AdhereTech or TowerView Health which use smart pill boxes and pill bottles, while companies like AiCure are using artificial intelligence to remind and make sure patients are taking their meds.
EMRs are slowly trying to integrate this, but because of poor infrastructure and the fact that each EMR is set up so differently, they aren't prepared to handle this wave of data creation which provides an opportunity for some smaller, more nimble companies to do that. And an obvious change that's driving this is the use of mobile as the central point of information relay, which not only lets the average consumer monitor their health actively, but it also lets them share information with the doctors in a lot more manageable forms. And just to understand the sheer size and impact that mobile has had, there are
We're seeing financing to some private companies that do this already. Companies like AliveCor, which uses a mobile case to conduct ECGs through your phone, or something like Cue, which can do a variety of hormone monitoring levels with do-it-yourself kits.
The last first of new out-of-hospital data that we're going to talk about today is drug adherence which is a
huge problem and contributes somewhere between $100 billion and $300 billion in healthcare expenditure. And a third of that is due to three chronic diseases, which is cholesterol, blood pressure and diabetes. A big reason that adherence has been such a problem is that we really have no good ways of measuring or helping patients adhere. In the past, it was mostly done with techniques like self-reporting or questionnaires or analyzing pharmacy claims. We had no real way of observing patients take their medicine or intervening when there was a problem.
And these diagnostic tools will actually help primary care physicians who are already one of the most taxed specialties in medicine right now, and it can help them take measurements that they don't have to do every single time you go visit their office. And finally, the anomalies can be caught earlier so that we can take measures to make sure that there's a healthy outcome for the patient.
WHAT ARE WE DOING WITH THIS DATA?
THE NEXT FRONTIER OF PATIENT DATA: GENOME SEQUENCING
I think what's interesting to note is that when we plot out the investments that pharmaceutical companies have made in private markets using our business software graph tool, most big pharma companies, with the exception of Roche and Merck, have actually not invested in digital health companies despite the fact that a lot of the data that's being created is directly applicable to them and the processes for the clinical trials we're talking about. Pharma has definitely been on the slower side to take advantage of these trends in digital health, and they focus their private market investments
And these are some of the companies that are helping to facilitate some of the changes that I've been talking about. Whether that's helping patients navigate the eligibility criteria for these studies or companies like Elemental Machines which very recently raised with a founder's fund which can use this data from the lab to help improve reproducibility and identify problems.
But now an era of the ResearchKit and mobile health solutions, we can actually do a lifestyle cardiovascular study on more than 40,000 people who are geography agnostic and it's pushing information to their phones. It has a very high penetration and engagement rate. Really, this is just an entirely new era of doing clinical research thanks to mobile solutions and out-of-hospital data.
The end goal should be using data in every part of the process to make this much more efficient as a whole. We'll be using clinical data to help patients find trials that are relevant to them. Those trials will be conducted as remotely as possible with a central coordinated center. And we capture data during all times during the day, regardless of geography, and takes data from a wide variety of sources simultaneously.
there were a little bit more than 3,200 participants. In this study, we saw a closer to 17,000 participants but they're all Canadian. And here, in an intervention study, it was a little bit more than 500 patients and the intervention was an automated phone call or bringing a physical tracker to your physician appointments. Now keep in mind, these are all studies that were done relatively recently, so between 2005 and 2010.
A huge advancement in this field comes in the form of Apple's ResearchKit which allows developers to utilize the mobile and smart watch technology to become better tools for research. And the companies are already working with several different organizations to conduct studies on conditions like asthma, autism, Parkinson’s, cardiovascular disease, and they're doing that by using some of the hardware that's already in these phones like the accelerometer or the camera. The ResearchKit, and in general, the mobile clinical trial solutions we've been talking about are going to be huge for reducing costs associated with maintaining multiple sites for these studies.
This chart shows, and I know it’s a little bit hard to see, but they actually contribute to a third of the costs that are required for these studies. Using these mobile solutions, we can actually reduce the number of physical sites necessary which reduce cost but also make sure that patient’s unlimited by their physical proximity to the site or their physical ability to get to the site.
And the number and the types of patients we get for these studies are critical. I took a snapshot of some previous studies that were done that were studying relatively similar things, so in this case, lifestyle and how it relates to cardiovascular disease. And as you can see here,
We've been talking about the integration of some of the current technology as it enters the healthcare space. But we've been focusing a lot more on the providers and the payers. But the next frontier of patient data is going to be based on our genetic data and how it’s associated with one of the buzziest words in healthcare which is precision medicine.
The price of gene sequencing has dropped very drastically, even faster than Moore's law, from the hundreds in millions it used to cost years ago to now a thousand dollars less than it cost today.
manner. Nine out of ten studies have significantly extended their timelines just to find enough people to participate in the study. And 48% of studies under-enroll as a whole. And the big issue is that because a large majority of patients don't even know that the trials exist that are relevant to them and that's a huge disconnect because a lot of them actually want to enroll in trials that might be relevant. A survey done by Zogby Analytics have found that 53% of patients were not aware of these trials and those are number one reasons for them not enrolling as a whole. Several companies are now working on aligning clinical data to make sure patients and providers aware of some of the relevant clinical trials that might be going on.
All in all, these kind of problems, they took very expensive studies but also very long timelines. Drugs can end up going through clinical trial processes for 15 years in some cases. Companies want to get their drugs into products out as quickly as possible. Any solutions that save them time will be helpful to them.
The biggest time savers would be the ability to recruit patients in a faster and more targeted
you need to travel to, which, in and of itself, presents issues with what kind of people can participate in these studies. And like what we talked about before, adherence and retention is a huge issue for these studies. But we already talked about some of the mobile solutions that are addressing those issues.
are set up and conducted to not only curb cost but actually more importantly, save time. As Stephen Cutler, who's the COO of ICON which is a clinical research organization says, “A big way we can do that is by engaging patients more and having access to the real-time data.”
Now we know how some of the new dynamic real-time data is changing all processes and affecting large corporates like insurance. But that's more focused on the pair and the provider. What can we say about other large corporates in healthcare like pharmaceutical companies? Well data is basically completely changing the way that we're doing research, conducting clinical trials, and helping drug development happen. Even the top executive at clinical research organizations understand that things need to change the way and the trials
What is it about the trials now that how they're structured makes itself problematic? Well for one, the recruitment for trials are still very ad hoc process and mostly the patients are recruited either through partnerships like providers and physicians or targeting patients through ads, websites, etc. But also some of the biggest cost drivers for these studies are actually rooted in the overhead that's associated with maintaining multiple physical sites that are just meant actually
there and talk about the beginning of the digital health movement, which started with EMRs and the digitization of data. And from there, we'll move into the new wave of data that's being generated outside of the hospital.
We'll also talk about the entire area of research and clinical trials and how that's being disrupted by the new way of digital health data. And then finally, we'll wrap up on talking about how the future of healthcare looks, thanks to genetic sequencing and that data that's being created, how some tech giants are entering the space. And then we'll end it by asking some open-ended questions for the future.
Even before we know who's owning the data, how do we make sure that the data is secure in the first place? Data breaches in other industries are much less costly compared to data breaches that happen in the healthcare space. And finally, does de-identifying the patient data even help anymore? As we get more granular data, specifically genetic data, we can actually piece together what you look like and the characteristics about you. We can do that even without patient identifiers. But now, thanks to strict HIPAA regulations, data that could be incredibly helpful if it were free-flowing and in the right hands is instead tied up behind privacy. At a certain point we have to ask, are the costs of slowing information down worth the benefits of keeping it private?
And I'd be remiss if I didn't talk a little bit about Alphabet in Google as a player in this area. We won't have time to touch on everything they're doing, but between their research divisions, investment arm, and the parent company itself, it is very active in the healthcare data arena in multiple different ways. I took a few select partnerships that I thought were interesting. On the one hand, we're seeing Google powering the movement on wearables by partnering with companies like Dexcom and Novartis to create new types of glucose monitoring wearables. But we're also seeing that the company's applying its powerful analytics to help people make sense of their data. That's what they're doing with Sanofi. They're also doing it with the Mayo Clinic where they're helping to provide relevant medical information and answers to some commonly searched symptoms and diseases.
And lastly, it's clear that Google is interested in the personal health record space. As discussed, that space has been typically hard in the past and the Google health initiative actually tried to do this many years ago but ended up failing. But now, Google has revealed the Google Fit platform which aggregates data across different Android platforms to provide users a look into their lifestyles. And then Google themselves aren't doing it. They're investing in companies that are. A significant part of their portfolio includes health data companies which are working in spaces like oncology or genetics, predictive analytics and even remote clinical labs.
The next case we're going to take a look at is the chip maker Qualcomm who has an opportunity of powering a lot of these devices that are outside the hospital. If you look at their private market activity, which includes both investments and acquisitions, it's clear that there's a surge of interest for them moving into 2014 and still exists, though has tapered off a bit in 2015. Qualcomm is powering what they call the IoMT movement, which is the Internet of Medical Technology. And they have a few different product offerings such as a platform to capture all connective devices that are transmitting biometric data and also assess offering to facilitate
We're going to start with IBM. which is Watson, the supercomputer. IBM has been working on Watson for a while now and now they're moving in to a commercial product. And the company has highlighted quite a few use cases for healthcare. Not only can the computer analyze millions of medical journals and other disparate data sources, but it can actually use that ability to synthesize information and act as a Siri for doctors, help match clinical trials, but also help drug development companies in the development phase. To bolster their capabilities, they've acquired at least three companies including a billion dollar acquisition of Merge Healthcare for their clinical images. They've also acquired Explorys and Phytel for their data cleansing and population health analysis.
MACRO TRENDS IN HEALTH DATA
We're going to need better ways of analyzing all these genetic information which will be done once the ecosystem software and services starts getting built around it. And we're seeing that that's already happening. This graphic is a little bit old, but just gives us a sense on how more money is now going into the services and software part rather than the actual sequencing itself, which is really just a sign of a maturing industry.
I did a keyword search using genetics in the CBI database and I found a bunch of different companies that were working in the space, 23andMe probably being the most well-known.
We can also now understand if couples are carriers for hereditary diseases which can help them with family planning. And we're also seeing the rise of a field called pharmacogenomics which helps us understand how patients react to drugs, and that way we can again, going back to personalized medicine, we can give a more specific dosage and monitor the efficacy or the side effects in those patients. And this is an area that pharmaceutical companies really have taken a big interest in and have partnered with some of the top genetic sequencing companies to really understand what to do with this trove of genetic data.
One thing I want to highlight that I think is really important here is that we're still in the infancy of this field and we don't have a great understanding of what this genetic data means. Here we see snapshots of two different studies which analyze the same thing, which was using genotype guidance to understand the effect of warfarin therapy. And what's interesting is that both studies came to the opposite conclusion. Here we're seeing that the hypothesis that using the genotype-guided dosing actually is not superior. And here we're seeing that it is superior. This goes to show you that even though we're generating this data, we still don't quite understand a lot about it.
the hospital settings. But we still have a long way to go, especially since most patients don't know where to access their own healthcare data.
And that drop in cost is largely associated with a grand scheme that was concocted by the government to bring all the leading scientists in the field together to improve sequencing techniques. And the decline of sequencing costs has made it much more regularly available to the average person. We're seeing an explosion of genetic data being created as more and more people get their gene sequenced. And this is only going to accelerate in the future. We're also seeing some predictions saying, like 2024, we'll see 34 million people with their entire genome sequenced.
And this new set of data is pretty much relevant to everyone. We're seeing people from insurance, tech, pharmaceuticals. We're seeing a more partnerships happening between these players. But the issue is that because healthcare data is so personal, there are a lot of issues that go about who should be seeing the data and who should be owning the data.
the incentives and penalty system was created to encourage the meaningful use of EHRs, which has so far billed out more than $32 billion to get physicians and hospital systems set up on electronic record systems. Before 2009, less than half of physicians were using electronic system. But today, we're seeing something more along the lines of an 83% penetration rate, but some studies are actually saying closer to 90% by the end of 2015.
A big part of the root cause of these problems is because after the legislation has passed and the incentives were given, a land grab occurred where basically a large portion of the users were concentrated on the top three software providers. I think what's really interesting to note is that Epic, which not only has 11.6% of the total market share, but is also the primary software provider to the larger practices, which means that most number of individual physicians are interacting with it, but also, going back to the long expensive rollouts we talked about before, these are the customers that are much more unlikely to remove the software regardless of the quality just because the switching costs were so high.