Report: The importance of benchmarking clouds

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Windowed City Skyscraper Architecture Beneath Cloudscape in Black and White
The importance of benchmarking clouds by Paul Miller:
For most businesses, the debate about whether to embrace the cloud is over. It is now a question of tactics — how, when, and what kind? Cloud computing increasingly forms an integral part of enterprise IT strategy, but the wide variation in enterprise requirements ensures plenty of scope for very different cloud services to coexist.
Today’s enterprise cloud deployments will typically be hybridized, with applications and workloads running in a mix of different cloud environments. The rationale for those deployment decisions is based on a number of different considerations, including geography, certification, service level agreements, price, and performance.
To read the full report, click here.

White House shines spotlight on health care tech companies

It’s the most wonderful time of the year, and it goes hand-in-hand with one of the most tedious deadlines of all — at least until April 15 (aka tax day) rolls around. December 15 marks the deadline to sign up for health coverage in order to be covered in 2016. And, seemingly recognizing that making sense of the complex health coverage landscape is about the least festive thing there is, the White House spoke up today with a dispatch about health care technology.

White House digital strategy director Joshua Miller took to the blog to discuss the record low of uninsured Americans, as well as highlighting few of the companies that are leveraging technology to help the uninsured get coverage. Some of those companies include ZocDoc, an online appointment-booking service that will be reminding its users to enroll for coverage and Oscar, a health insurance startup that made a PSA aimed at demystifying coverage.

“We know that many uninsured Americans question whether they can afford coverage, and may not realize that more than 7 in 10 HealthCare.gov customers can find insurance for $75 a month or less after tax credits,” said Miller in the White House blog post. “So for uninsured Americans who remain skeptical about the costs of getting insured, health insurance company Oscar Health has created a digital video public service announcement (PSA) that explains why health insurance is actually more affordable than people may think. Oscar will distribute this video in key markets online, too, including in California, New York, and Texas.”

On the receiving end of a hefty $32.5 million investment from Google back in September, Oscar is a health coverage startup that helps people choose coverage options with straightforward, plain-language plan comparisons. Leaning heavily on technology and simple design, Oscar helps customers keep track of care and connects them to providers in an attempt to improve upon the outdated systems that create separation between customers and coverage. 

“At Oscar, we are constantly pushing ourselves to find new and creative ways to engage consumers through the use of technology and provide members, as well as the uninsured in our communities, with the knowledge they need to get care, says Oscar CEO Mario Schlosser. “We are incredibly motivated by the support the White House has shown us today on this initiative and are proud to share this video we created on just how simple it is to sign up for an affordable plan through the ACA.”

The deadline, it creeps. But understanding the way coverage works in the age of the ACA is a big part of solving the uninsured problem, and the administration’s recognition of the vital role of technology in tackling some of our country’s biggest issues remains a pivotal thread in the tapestry of what Miller calls “today’s collective challenges.”

Health care’s future is data driven

Darren is the chief executive at Apixio.
Despite spending more than $3 trillion a year on health care in the U.S., we do not yet have a way to easily access your complete medical history. The average hospital reinvests in MRI machines every five years or so at a cost of millions of dollars. Yet, comparatively little has been directed toward unlocking some of the most valuable information health insurers, physicians, and hospitals already have about you.
The U.S. produces 1.2 billion clinical care documents annually, but nearly 80 percent of the data they contain is unstructured. This information is difficult for entities to understand and use. The medical chart contains a record of your health care — visits with doctors and hospitals, treatments, procedures, medications, diagnoses, and the results of workups. It is the key to understanding your health and improving the care provided to you. Yet, the challenge of accessing and making that information useable is immense.
The typical medical chart is stored in various fragments across different locations and systems. Your primary care physician has their record of you but not the record from your cardiologist or gynecologist or from the emergency department doctor you saw six months ago for bronchitis, for example. Imagine your entire medical record as a jigsaw puzzle in which the pieces are scattered and stored in different locations and different types of boxes, each of which is hard to open. No wonder people feel as if they are repeating themselves every time they visit a medical facility.
Luckily, technologies that make sense of the immense amount of data and preserve the patient narrative are rapidly emerging. With the rise of  cognitive computing, natural language processing (NLP), and data science in health care, we now have the power to unlock untold value in health care data and drive proactive, targeted health care.

Enabling insights

The first step is being able to make sense of the rich narrative in the medical chart whether from a primary care physician or specialist practicing in different organizations, different regions or both. While your doctor is familiar with your record, the medical system as a whole is not. So medical care continues to be reactive rather than proactive.
This is where cognitive computing and NLP enter the picture. NLP tools can help extract data from free text found in the patient record creating valuable material for big data technologies to analyze. Cognitive computing platforms use NLP along with pattern recognition models and data mining techniques to simulate human thought processes in a self-learning computerized system. A cognitive computing platform refines the way it looks for patterns as well as the way it processes data so it becomes capable of anticipating new problems and modeling possible solutions.
Once doctors have access to patient data, the question is how can they use it to accurately predict what treatments are most effective. Data science gives rise to a better understanding of the relationship between treatments, outcomes and patients. Now health care organizations have the tools to combine data from different sources and paint a more complete picture of the patient to personalize their treatment.

A data-rich health care future

These technologies give health care organizations the ability to access the previously untapped 80 percent of health care data so providers have real-time access to information and a deeper understanding of patients. If doctors know more about patients, then they can make more intelligent decisions that will result in quicker recoveries, fewer readmissions, lower infection rates and fewer medical errors. Ultimately, it will supercharge the value of care.
Beyond benefiting individual patients, access to this data will also create a living laboratory of clinical data to better inform health care decisions. Now that information about clinical care can be machine read, physicians can access it and base research on the everyday clinical care of millions of patients. Rather than depending on narrowly designed studies that do not directly relate to individual patients, health care organizations and researchers can learn about health care delivery from everyday real-world data.
Big data technologies can make use of information that is locked up in our medical charts in different systems and locations so we can transform how we look at and interpret patient health. With access to the untapped 80 percent of patient data and tools that put the data to use, we can change the delivery and consumption of health care as we know it, and usher in a new data revolution in health care that will improve patient care and result in high-quality outcomes.

Exploring what an Apple medical gadget might entail

Apple isn’t interested in transforming the Apple Watch into the ultimate health monitoring device, but the company is open to creating an entirely different product that would.

In a recent interview with The Telegraph, chief exec Tim Cook hinted at future involvement from Apple in health, but cited federal regulation processes as a reason it wouldn’t do so via its smartwatch. He did, however, talk about the possibility of an adjacent product (a gadget or app) that could have a serious impact on the health care industry — because, of course, Apple’s involvement tends to result in a pretty serious impact on every industry it touches.

While he gave few specifics, there are a few key factors surround FDA approval and technology in the health care sector that might help suss out what Apple has in mind. I caught up with Venkat Rajan, a health care analyst with market research firm Frost & Sullivan to clarify a few things about health care tech, FDA testing and Apple’s place in the medical arena.

“To be clear in terms of definition,” says Rajan, “the FDA doesn’t dictate how a doctor can use a device as much as it dictates how you market what the device says it does.”

He gives the example of hearing aids versus sound amplifiers — two devices that do essentially the same thing and function in much the same way, but are marketed differently. Hearing aids are positioned as a treatment for hearing impairment and, as such, typically come from audiologists.

Sound amplifiers, by way of contrast, aren’t marketed as treatment devices. Because of this distinction, this class of device isn’t subjected to the scrutiny and regulation of the FDA and, as a result the product reaches the market and the consumers much more quickly.

So where does that leave Apple? Cook made it clear that they’re not going to chase down FDA approval for Apple Watch, but that certainly doesn’t mean that the company will leave a potentially lucrative market untapped — nothing would be more unlike Apple.

“The real opportunities for Apple are something adjacent to the Watch and that probably means some type of peripheral, wearable-type technology,” Rajan says. “There are a lot of different potential wearables out there that are being explore and so it remains to be seen if Apple has a specific type of patient or disease profile in mind.”

There are already FDA-approved apps and peripherals that are available for use with Apple Watch, though the watch itself is not FDA-approved. Apple Watch’s current role in the health and medical arena is as a conduit for health-related data and communications for those who work in health care. Its role isn’t particularly different from that of a smartphone or tablet though it is, perhaps, much more portable.

While its health data collection and activity tracking work well for personal use, the lack of FDA-approval means that the data from Apple Watch isn’t particularly useful for doctors looking to treat patients and track their progress, which is a key element of wearable technology in the medical space. The future of powerful medical technology for consumers will likely revolve around the degree to which doctors and patients can trust the data gathered by their devices.

“A lot of these devices do a good job of tracking information,” Rajan says. “But I think it is taking a lot of the types of information — whether it’s heart rate or activity or blood sugar sensing — and taking it a level deeper, [such as] being able to collect medical-grade data that an insurance company, hospital, or physician could take a look at and make decisions around.”

Why Google is taking a closer look at disrupting health care

In its first investment since the announcement that Google would become Alphabet, Google Capital has put a major vote of confidence into the future of health care in the tech sector.

A vote of confidence to the tune of $32.5 million.

Google Capital, a growth equity fund and part of Google/Alphabet’s investment arm, has previously backed ventures like Duolingo, Survey Monkey, and Glassdoor, as the Wall Street Journal points out in its report. Now, Oscar Health Insurance Corp. joins those ranks, but there’s reason to believe Google’s interests in health care go beyond the investment.

Oscar is a health insurance startup that hopes to change the way that people buy and interact with their health care coverage by using technology paired with simple and intuitive design. By clearly laying out coverage options, connecting customers directly to providers, and keeping track of care, Oscar already sets itself apart from the pack of large health insurance providers, which continue to lean on outdated technology that drives a wedge between customers and their coverage.

Health care is slow to change, and the tech is outdated,” says Forrester Research health care analyst Kate McCarthy. “New competitors help push large payers forward and are a good way to test the market to see what works.”

Oscar isn’t the only startup attempting to push the health insurance industry forward, though. Accordion Health‘s customer-facing insurance solution Pistachio helps customers explore their options by comparing Medicare Advantage plans side-by-side.

“I think Oscar is a starting point for a huge change in health care, and we are working just as hard (or harder) in bringing about consumerism within health care through our tools, such as Pistachio,” says Accordion Health CEO Sriram Visiwanath. “We have a fraction of the resources of Oscar, but the same shared goal of making health plan risk management way more operationally, financially efficient, consumer-driven and UX-centric.”

Startups have a habit of moving industries forward (usually), and the health insurance industry is no exception. As companies like Oscar enter the marketplace and provide customers with options and transparency, expectations within the open market shift.

Health coverage is already moving in two directions,” says McCarthy. “More plans are being offered with high deductibles. This shifts much of the upfront investment in health expenses to the patient… In turn, this is pushing patients/consumers to expect more options in health plans and greater transparency on cost and quality outcomes. Startups that can be good patient navigators and agents of price transparency have a big spaaaaaace to fill in the industry.”

Google Capital’s $32.5 million investment boosts Oscar’s valuation up to $1.75 billion, according to a source from the WSJ report.  Though certainly a new direction for Google in the health care space, the recent investment is far from the company’s only foray into the health industry. For instance, Google Life Sciences, which is focused on technologies that push health care technology forward (like glucose-monitoring contact lenses), very recently hired Dr. Thomas R. Insel, who was previously the director of the National Institute of Mental Health.

Google (Alphabet) is clearly making a play to expand their presence in the health care marketplace,” says McCarthy. “Oscar represents an opportunity to invest in a model consumers are responding positively to and is a smart choice for Google.”

For now, Oscar is only available in New York and New Jersey, but plans to extend service to California and Texas beginning in 2016.

Why deep learning is at least inspired by biology, if not the brain

As deep learning continues gathering steam among researchers, entrepreneurs and the press, there’s a loud-and-getting-louder debate about whether its algorithms actually operate like the human brain does.

The comparison might not make much of a difference to developers who just want to build applications that can identify objects or predict the next word you’ll text, but it does make a difference. Researchers leery of another “AI winter” or trying to refute worries of a forthcoming artificial superintelligence worry that the brain analogy is setting people up for disappointment, if not undue stress. When people hear “brain,” they think about machines that can think like us.

On this week’s Structure Show podcast, we dove into the issue with Ahna Girschick, an accomplished neuroscientist, visual artist and senior data scientist at deep learning startup Enlitic. Girschick’s colleague, Enlitic Founder and CEO (and former Kaggle chief scientist) Jeremy Howard, also joined us for what turned out to be a rather insightful discussion.

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Below are some of the highlights, focused on Girshick and Howard view the brain analogy. (They take a different tack than Google researcher Greg Corrado, who recently called the analogy “officially overhyped.”). But we also talk at length about deep learning, in general, and how Enlitic is using it to analyze medical images and hopefully help overcome a global shortage of doctors.

If you’re interested in hearing more from Girshick, Enlitic and deep learning, come to our Structure Data conference next month, where she’ll be accepting a startup award and joining me on stage for an in-depth talk about how artificial intelligence can improve the health care system. If you want two full days of all AI, all the time, start making plans for our Structure Intelligence conference in September.

Ahna Girshick, Enlitic's senior data scientist.

Ahna Girshick

Natural patterns at work in deep learning systems

“It’s true, deep learning was inspired by how the human brain works,” Girshick said on the Structure Show, “but it’s definitely very different.”

Just like with our vision systems, deep learning systems for computer vision process stuff in layers, if you will. They start with edges and then get more abstract with each layer, focusing on faces or perhaps whole objects, she explained. “That said, our brain has many different types of neurons,” she added. “Everywhere we look in the brain we see diversity. In these artificial networks, every node is trying to basically do the same thing.”

This is why our brains are able to navigate a dynamic world and do many things, while deep learning systems are usually focused on one task with a clear objective. Still, Girshick said, “From a computer vision standpoint, you can learn so much by looking at the brain that why not.”

She explained some of these connections by discussing a research project she worked on at NYU:

“We were interested in, kind of, the statistics of the of the world around us, the visual world around us. And what that means is basically the patterns in the visual world around us. If you were to take a bunch of photos of the world and run some statistics on them, you’ll find some patterns — things like more horizontals than verticals. . . . And then we look inside the brain and we see,  ‘Gee, wow, there’s all these neurons that are sensitive to edges and there’s more of them that are sensitive to horizontals than verticals!’ And then we measured . . . the behavioral response in a type of psychology experiment and we see, ‘Gee, people are biased to perceive things as more horizontal or more vertical than they actually are!'”

Asked if computer vision has been such a big focus of deep learning research so far because of those biological parallels, or because that’s companies such as Google and Facebook have the most need for, Girshick suggested it’s a bit of both. “It’s the same in the neuroscience department at a university,” she said. “The reason that people focus on vision is because a third of our cortex is devoted to vision — it’s a major chunk of our brain. . . . It’s also something that’s easier for us to think about, because we see it.”

Structure Data 2012: Ryan Kim – Staff Writer, GigaOM, Eric Huls – VP, Allstate Insurance Company, Jeremy Howard – President and Chief Scientist, Kaggle

Jeremy Howard (left) at Structure: Data 2012.

Howard noted that the team at Enlitic keeps finding more connections between Girshick’s research and the cutting edge of deep learning, and suggested that attempts to distance the two fields are sometimes insincere. “I think it’s kind of fashionable for people to say how deep learning is just math and these people who are saying ‘brain-like’ are crazy, but the truth is … it absolutely is inspired by the brain,” he said. “It’s a massive simplification, but we keep on finding more and more inspirations.”

The issue probably won’t be resolved any time soon — in part because it’s so easy for journalists and others to take the easy way out when explaining deep learning — but Girshick offered a solution.

“Maybe they should say ‘inspired by biology’ instead of ‘inspired by the brain,'” she said. “. . . Yes, deep learning is kind of amazing and very flexible compared to other generations of algorithms, but it’s not like the intelligent system I was studying when I studied the brain — at all.”

Deep learning might help you get an ultrasound at Walgreens

A new startup called Butterfly Network, from genomic-technology pioneer Jonathan Rothberg, hopes to improve the world of medical imaging using advanced chip technologies, tablet devices and deep learning. Rothberg explains how and why deep learning is key to the company’s plans.

Box acquires medical-imaging startup MedXT

Cloud storage and collaboration provider Box has acquired MedXT, a startup that has built technology for storing and sharing medical images in the cloud. The acquisition is an early step toward bolstering the Box for Healthcare initiative the company recently launched. Box founder and CEO Aaron Levie announced the acquisition in a blog post, explaining that “the expertise of the MedXT team members and their medical image viewing technology will be incredibly important in our effort to deliver HIPAA-compliant sharing and collaboration for all critical content types.”

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Lumiata raises another $6M to make hospitals smarter with machine learning

A machine-learning-based health care startup called Lumiata has added $6 million to its series A round, first announced in January, bringing its total raise so far to $10 million. The company analyzes everything from medical papers to EKG readings to help predict each patients health.