
Source: Gartner

In healthcare analytics, Claims data are ubiquitous. They have been available for decades and are highly structured and standardized. Unfortunately, they are also relatively sparse, containing only a handful of procedure codes and diagnoses for each episode. EHR data, on the other hand, carries hundreds of valuable data points for each encounter, including social indicators, medical history, lab results, vitals, point of care observations and more.
Because of its complexity, however, EHR data often goes underutilized (or unused entirely). It is a massive and rich dataset that lies just beneath a bed of the more familiar claims data, waiting to be explored for improved risk stratification and population health management.
This graph visualizes the quantity of data available for a set of 500 patients taken directly from Arcadia’s Benchmark Database. Claims records are represented by the green bars above the ground, dwarfed by the immense set of underlying EHR data, represented by the brown bars below ground.


Prashant Gandhi, Somesh Khanna and Sree Ramaswamy in the Harvard Business Review:
…what are the key attributes of a digital leader? And how can companies benchmark themselves against competitors? We looked at 27 indicators that fall into three broad categories: digital assets, digital usage, and digital workers. Our research shows that the latter two categories make the crucial difference.
Digital assets across the entire economy doubled over the past 15 years, as firms invested not just in IT but in digitizing their physical assets. Digital usage in the form of transactions, customer and supplier interactions, and internal business processes, grew almost fivefold — and over the entire period, the leading sectors maintained an enormous lead in usage over everyone else. But the biggest differentiator of all comes from having a digitally empowered workforce. Over the past two decades, the leading sectors’ performance on various digital labor metrics — such as the share of tasks involving digital tools and the number of new digital occupations — rose eightfold, while the rest of the economy barely ran in place…
The technology sector comes out on top — no surprise there. Right behind it are media, finance, and professional services, all of which have far more sophisticated digital capabilities than the rest of the economy. On top of these macro-level differences, we see that even lagging sectors may have standout firms that are pushing the frontier forward for everyone else.
The most compelling story told in the new documentary “The Human Face of Big Data” (PBS, February 24), is about the collection and analysis of data to predict the onset of potentially deadly infection in premature babies.
By the time these babies are physically showing signs of infection they are very unwell, a condition a caregiver could not predict by looking at their chart, where once an hour their vital signs are recorded. “What shocked me was the amount of data loss,” says Dr. Carolyn McGregor of the University of Ontario. The solution was Project Artemis in which computers collect all relevant data continuously and watch for certain changes in vital signs. “If something starts to go wrong with that baby we have the ability to intervene [before physical symptoms appear],” says Dr. McGregor.
It’s a story of how more data may lead to better outcomes, in this case even save lives. The term “Big Data” has come to represent in recent years this promise, a potential that can only be realized if we clearly establish what we want to achieve by collecting more data and why more data is better than less data in each particular case.
Unfortunately, in our technology-obsessed world, new technologies and new technology applications tend sometimes to become buzzwords that are hyped, celebrated and often discussed irresponsibly by technology vendors and the media. Unfortunately, “The Human Face of Big Data” by and large falls into this trap, the fascination (self-delusion?) with the idea of we are living a momentous time in history thanks to technology. Going beyond “big data,” it is a paean to information technology and computerization, as Jay Walker of TEDMED declares in the film:
Billions and billions of people who have been excluded from the discussion, who couldn’t afford to step into the world of being connected, step into the world of information, learn things they could never learn, are suddenly on the network… Suddenly the world has a lot more minds connected in the simplest, least expensive possible way to make the world better… I don’t think there’s any question that we are at a moment in human history that we will look back on in fifty or a hundred years and we’ll say right around 2000 or so it all changed.
I’ll go out on a limb and venture to predict that a hundred years from now, the time around 2000 will be marked by Americans’ loss of the security they have enjoyed since the end of the Second World War, not by the rise of the Internet. And it will be clear to most observers, as it is clear to many today, that some of the additional minds that are now connected to the Internet, do not see it as a tool “to make the world better” (they may see it that way but I’m guessing Walker probably doesn’t agree with their definition of a better world).
“The Human Face of Big Data” demonstrates that giving more people access to the Internet does not automatically include them in “the discussion.” China has more people connected to the Internet than any other country, but there is no one from China among the two dozen “experts” identified by name in the film—all are based in the U.S. No one from Russia, India, Japan, Brazil—countries where one may find talking heads or, even better, data scientists, that may represent a different point of view about the role of technology, the Internet, and big data. It would have enriched this documentary tremendously if we heard their take on the pros and cons of big data, how they define it, what it means to them, and what specific types of data collection and analysis will make a difference in their countries. (In line with Anil Dash’s response to Mark Zuckerberg’s post regarding the recent decision by India’s telecom regulator: “What about pausing the Internet Basics effort and spending some time on a real effort to listen to Indian voices about what would help them have connectivity on their own terms, in a way they find acceptable?”).
The lack of diversity in the voices and opinions heard in the film, its relentless emphasis on accentuating the positive and the speculative—the two segments discussing the negative aspects of big data last a total of 7 minutes—is particularly astonishing given that there has been no shortage of intelligent discussions of the potential pitfalls in the rush to collect and analyze data.
Take, for example, Kate Crawford’s list of myths associated with big data, which includes the belief that bigger data is always better data, that correlation is as good as causation, that big data eliminates biases, and that it doesn’t invade our privacy. Instead of using these and similar objections to prompt a rich discussion and debate, the documentary—promoted by PBS as an examination of “the promises and perils of this unstoppable force”—deals only with the issue of privacy, a post-Snowden requirement.
Here and there, the documentary almost gets into what could have been turned into an engaging and educational discussion of big data, only to stop in its tracks for fear of losing its shiny, sunny, positive packaging.
One example is the discussion of Google Flu Trends which “accurately predicts flu outbreaks up to two weeks before the CDC,” based on flu-related Google searches. To its credit, the documentary then shows Stephen Downs of the Robert Wood Johnson Foundation talking about the “flip side,” the time Google Flu Trends ”got it way wrong,” because media coverage of the flu season got people to search for “flu” even if they were not sick. But then the film moves on to the next topic. A missed opportunity to talk about what has Googled learned from its failure and the dangers of blind faith in big data and algorithms, to say nothing about raising the question of how “world changing” is finding out about flu outbreaks 2 weeks before the federal government and whether it justifies the generalized claim we hear from Rick Smolan that “now we can see in real-time what’s going on and respond to it.”
Another example of a missed opportunity for an intelligent discussion is when we hear from Tim O’Reilly “I am optimistic but not blindly foolishly optimistic. Remember, the financial crisis was brought to us by big data people.” Finally, you hope, we are going to get into an interesting discussion of the empirical—what has actually happened and why, not what may happen—and practical perils of big data. But you quickly find out that (at least in this regard) you are foolishly optimistic because all we get are platitudes such as “we have to earn our future… we have to make the right choices.”
The missed opportunities are compounded by outright fiction. Here is some of the data we discover in this documentary about big data:
Really? What big data time-machine tells us exactly how much “information” and “data” and “data processing” there was in the last 3000 years or the 15th century or at the dawn of humanity?
The documentary provides a definition of big data, something that is often missing from discussions of the topic. While poetic, it is quite meaningless: Big data is a nervous system for the planet. This global definition leads to discussions in the film that have more to do with the Internet than with big data.
For example, in the segment titled “Data: The future of revolution,” Joi Ito talks about how the “Arab Spring” started with a photo shared on Facebook and then picked up by Al Jazeera and broadcast on TV as an example of linking activists, social media and mainstream media. “Technology has fundamentally changed the way people interact with everything,” says Ito. If big data is the planet’s nervous system, than every interaction is big data. QED.
In the same segment, Ito also comments “that’s one of the challenges of big data—it has so much opportunity for both good and also for screwing up our system.” But it is not clear (at least to this viewer) why he says that in this context. As with the other voices we hear in the documentary, there may have been something else there that got lost in the editing process. The impression the film makers wanted to leave with the viewer is summarized by John Battle and quoted in the press release: “The era of Big Data is an important inflection point in human history and represents a critical moment in our civilization’s development.”
The theme of we-are-living-in-a-historic-moment-because-of-technology-and-we-have-to-make-critical-decisions-because-it-may-turn-negative-but-let’s-accentuate-the-positive has been the hallmark of technology talk for a while, moving rapidly from one hype cycle to the next, with little connection to reality (big data has already been eclipsed as the buzzword of the day by the Internet of Things, Artificial Intelligence, and Virtual Reality). There’s no escape from this escapist, technology-centric, US-centric myth-making, shared and promoted by the global chattering classes. Here’s danah boyd reporting on last month’s meeting of the World Economic Forum in Davos, Switzerland:
I started to sense that what the tech sector was doing at Davos was putting on the happy smiling blinky story that they’ve been telling for so long, exuding a narrative of progress: everything that is happening, everything that is coming, is good for society, at least in the long run.
Shifting from “big data,” because it’s become code for “big brother,” tech deployed the language of “artificial intelligence” to mean all things tech, knowing full well that decades of Hollywood hype would prompt critics to ask about killer robots. So, weirdly enough, it was usually the tech actors who brought up killer robots, if only to encourage attendees not to think about them.
Not only did any nuance get lost in this conversation, but so did the messy reality of doing tech. It’s hard to explain to political actors why, just because tech can (poorly) target advertising, this doesn’t mean that it can find someone who is trying to recruit for ISIS. Just because advances in AI-driven computer vision are enabling new image detection capabilities, this doesn’t mean that precision medicine is around the corner. And no one seemed to realize that artificial intelligence in this context is just another word for “big data.” Ah, the hype cycle.
It’s going to be a complicated year geopolitically and economically. Somewhere deep down, everyone seemed to realize that. But somehow, it was easier to engage around the magnificent dreams of science fiction. And I was disappointed to watch as tech folks fueled that fire with narratives of tech that drive enthusiasm for it but are so disconnected from reality as to be a distraction on a global stage.
Similarly, veteran tech observer Steven Levy says that the virtual and augmented reality demos at TED 2016 were redundant because “At TED, you are already immersed in a kind of artificial reality.” Is there a tech backlash brewing? Are we finally going to have more sober and multi-dimensional discussions of technology?
I don’t think so, I don’t think we (especially in the U.S.) will let go of soothing escapism. Expect to see in a few years, when we will already move to other buzzwords, a PBS documentary titled “The Human Face of Artificial Intelligence.”
Originally published on Forbes.com

Investor interest in IoT startups working in healthcare has grown hand-in-hand with the broader boom in digital health.
Increasingly, Internet of Things startups are finding new applications within healthcare and leveraging connected sensors to better diagnose, monitor, and manage patients and treatment. Many are focused on clinical-grade wearables to more robustly track patient data, while others see opportunity for sensor networks within hospitals and practices to optimize healthcare delivery and monitor patient adherence.
We broke down the healthcare IoT into the following categories:
This category included startups that are using connected objects to improve the delivery of healthcare in hospitals and clinics, and also track treatments to boost the effectiveness of healthcare providers. Augmedix and Obaa, for example, enable smartglass wearables like Google Glass to be used for healthcare charting. And Simplifeye harnesses the Apple Watch for doctors to track patient visits and access EMRs. Others like Awarepoint use IoT sensors for location-tracking on patients and medical equipment in real-time, in what they call location-as-a-service. AdhereTech, another startup here, is a connected pill bottle that tracks medicine adherence.
Companies here were focused on connected biometric sensors for use in a clinical or hospital setting, and some companies in patient care (such asEarlySense and Monica Healthcare) have FDA approval. Quanttus, MC10, and others are developing clinical-grade wearables that are on the road to FDA approval. Other clinical IoT equipment included Eyenetra, a smart phone-enabled “auto-refractor” for vision testing.
This group of companies develop technology marketed to consumers for the collection of biometric information. Startups in this space range from addiction cessation tracker Chrono Therapeutics to the smart thermometer made by Kinsa. Also included were monitoring systems like Qardio andAliveCor, which allow for ECG (electrocardiogram) testing to be done from home.
This was mostly comprised of startups trying to hack the brain with high-tech consumer-targeted cranial wearables. Ybrain and InteraXon (with its brain-sending headband, marketed as Muse) read brainwaves, and Thync transmits mood-elevating neurosignals.
Others, such as Neurovigil, are more clinical-grade projects focused on noninvasive neurotech (brain wave reading/recording) in order to analyze drug efficacy and track neuropathology. Neurovigil raised a Q2’15 Series A from Draper Fisher Jurvetson and entrepreneur Elon Musk, among other investors.
These were fitness tracking consumer wearables or smart apparel such as Lumo and OMsignal.
Companies here were focused specifically on sleep tracking. Two examples in the space included Hello and Beddit.
Wearable technology that tracks infant movements and vitals. Companies here included Owlet and Sproutling, among others.
[youtube https://www.youtube.com/watch?v=Uj-rK8V-rik?rel=0]
In 2011, soon after Google first told the world about the robocars it had secretly been developing, it promised that the vehicles would be able to “drive anywhere a car can legally drive.” Its timeframe for delivering the technology was generally understood to be in the neighborhood of five years. For example, in a 2014 Wall Street Journal article, project director Chris Urmson was quoted as saying he was hoping “to field a fully autonomous car” by the end of the decade.
But last week in a speech at Austin’s South-by-Southwest, Urmson for the first time told a different story about both the delivery date and capabilities of its first self-driving cars.
Not only might it take much longer to arrive than the company has ever indicated—as long as 30 years, said Urmson—but the early commercial versions might well be limited to certain geographies and weather conditions. Self-driving cars are much easier to engineer for sunny weather and wide-open roads, and Urmson suggested the cars might be sold for those markets first.
Urmson put it this way in his speech. “How quickly can we get this into people’s hands? If you read the papers, you see maybe it’s three years, maybe it’s thirty years. And I am here to tell you that honestly, it’s a bit of both.”
He went on to say, “this technology is almost certainly going to come out incrementally. We imagine we are going to find places where the weather is good, where the roads are easy to drive — the technology might come there first. And then once we have confidence with that, we will move to more challenging locations.” [Urmson explains the projected rollout at about 28:00 in the video above.]

Funding to private robotics companies nearly doubled in 2015, reaching a record high in deals and dollars… Our robotics category excludes drones, but includes robotics companies focused on process and manufacturing automation, agricultural automation, surgical applications, and personal/social robots. Together, the companies have raised more than $1.4B in cumulative funding since 2011.
The highest funded round in 2015 was a $150M growth equity round raised by Auris Surgical Robotics, which is backed by investors including Lux Capital, Mithril Capital Management and NaviMed Capital.
Except for a 2013 slowdown in the rate of growth, robotics deals have been nearly doubling year-over-year.
Hizook (including drones):
2015 was an insane year for robotics companies; they raised $922.7M in VC funding — 170% more than in 2014. I’m almost certain that it exceeds $1 Billion, especially if you account for funding events in Asia (opaque to me) or if you take into account companies at the periphery of robotics (sensing, software, 3D printing, etc). Similar to previous years, a large portion of the funding went to medical companies and drone companies, but we also saw a lot of late-stage consumer robot financings this year (such as Jibo and Sphero) — but comparatively few agricultural or service robots. Still, I think it’s safe to say: 2015 was the year of the robotics startup!
[Funding in $Million]
Auris Surgical $150 Drone Deploy $9 DJI $75 Wonder Workshop $6.9
3D Robotics $64 Bionik Labs $6.2 Aeryon Labs $60
Squadrone Systems $5 Yuneec Electric $60 PetNet $4
Jibo $52.3 Sky Futures $3.8 Sphero $45 Gamma2 Robotics $3.5
Zymergen $44 RightHand Robotics $3.3 EHANG $42.0
Osaro $3.3 Rethink Robotics $40 Naio Technologies $3.3
GreyOrange Robotics $30 Soft Robotics $3 Medrobotics $25
RoboCV $3 Xenex $25 SkySpecs $3
CyPhy Works $22 Rapyuta Robotics $3 Fetch Robotics $20
Harvest Automation $2.9 Blue River Tech. $17 SynTouch $2.5
Peloton Technology $17 Flyability $2.5 Lily Robotics $14
Dronomy $1.5 Cruise $12.5 Catalia Health $1.5
Zimplistic $11.5 Mobile Indust. Robots $1.4 Clearpath Robotics $11.2
Dash Robotics $1.4 Virtual Incision Tech $11.2
International Data Corporation (IDC) has identified robotics as one of six Innovation Accelerators that will drive digital transformation by opening new revenue streams and changing the way work is performed. In the new Worldwide Commercial Robotics Spending Guide, IDC forecasts global spending on robotics and related services to grow at a compound annual growth rate (CAGR) of 17% from more than $71 billion in 2015 to $135.4 billion in 2019. The new spending guide measures purchases of robotic systems, system hardware, software, robotics-related services, and after-market robotics hardware on a regional level across thirteen key industries and fifty-two use cases. …
Not surprisingly, worldwide robotics spending is dominated by the discrete and process manufacturing industries, which represented 33.2% and 30.2% of total spending in 2015, respectively. Resource, healthcare, and the transportation industries are the next three largest commercial industries in terms of overall robotics spending. Process manufacturing and healthcare are two of the fastest growing industries, with worldwide spending in each forecast to nearly double by 2019.
From a technology perspective, worldwide spending on robotics systems, which includes consumer, industrial, and service robots, is forecast to grow to nearly $32 billion in 2019. However, services-related spending, which encompasses applications management, education & training, hardware deployment, systems integration, and consulting, will grow to more than $32 billion in 2019, overtaking robotics systems and becoming both the largest and fastest-growing category of spending by the end of the forecast. Total spending on system hardware (servers and storage) and software (command & control, network infrastructure, and robotics-specific applications) will grow nearly as fast as services spending.
The Asia/Pacific region including Japan accounts for more than 65% of total robotics spending throughout the forecast. Europe, the Middle East, and Africa (EMEA) is the second largest region with expenditures of $14.6 billion in 2015, followed by the Americas with 2015 spending totals of $9.7 billion. Robotics spending will nearly double in Asia/Pacific over the 2015-2019 forecast period, making it the fastest growing region followed by the Americas.
[youtube https://www.youtube.com/watch?v=W0_DPi0PmF0?rel=0]