Rene Amalberti to a Geneva Quality Conference:
http://www.isqua.org/docs/geneva-presentations/b13-rene-amalberti.pdf?sfvrsn=2
Some random, but 80 slides, often good
http://net.acpe.org/interact/highReliability/References/powerpoints/Clapper_ReliabilitySlides.pdf
A decent sweep through the available technologies and techniques with practical examples of their applications.
Some healthcare practitioners smirk when you tell them that you used some alternative medication such as homeopathy or naturopathy to cure some illness. However, in the longer run it sometimes really is a much better solution, even if it takes longer, because it encourages and enables the body to fight the disease naturally, and in the process build up the necessary long term defence mechanisms. Likewise, some IT practitioners question it when you don’t use the “mainstream” technologies… So, in this post, I cover the “alternative” big data technologies. I explore the different types of big data datatypes and the NoSQL databases that cater for them. I illustrate the types of applications and analyses that they are suitable for using healthcare examples.
Healthcare organisations have become very interested in big data, no doubt fired on by the hype around Hadoop and the ongoing promises that big data really adds big value.
However, big data really means different things to different people. For example, for a clinical researcher it is unstructured text on a prescription, for a radiologist it is the image of an x-ray, for an insurer it may be the network of geographical coordinates of the hospitals they have agreements with, and for a doctor it may refer to the fine print on the schedule of some newly released drug. For the CMO of a large hospital group, it may even constitute the commentary that patients are tweeting or posting on Facebook about their experiences in the group’s various hospitals. So, big data is a very generic term for a wide variety of data, including unstructured text, audio, images, geospatial data and other complex data formats, which previously were not analysed or even processed.
There is no doubt about that big data can add value in the healthcare field. In fact, it can add a lot of value. Partially because of the different types of big data that is available in healthcare. However, for big data to contribute significant value, we need to be able to apply analytics to it in order to derive new and meaningful insights. And in order to apply those analytics, the big data must be in a processable and analysable format.
Enter yellow elephant, stage left. Hadoop, in particular, is touted as the ultimate big data storage platform, with very efficient parallelised processing through the MapReduce distributed “divide and conquer” programming model. However, in many cases, it is very cumbersome to try and store a particular healthcare dataset in Hadoop and try and get to analytical insights using MapReduce. So even though Hadoop is an efficient storage medium for very large data sets, it is not necessarily the most useful storage structure to use when applying complex analytical algorithms to healthcare data. Quick cameo appearance. Exit yellow elephant, stage right.
There are other “alternative” storage technologies available for big data as well – namely the so-called NoSQL (not only SQL) databases. These specialised databases each support a specialised data structure, and are used to store and analyse data that fits that particular data structure. For specific applications, these data structures are therefore more appropriate to store, process and extract insights from data that suit that storage structure.
A very large portion of big data is unstructured text, and this definitely applies to healthcare too. Even audio eventually becomes transformed to unstructured text. The NoSQL document databases are very good for storing, processing and analysing documents consisting of unstructured text of varying complexity, typically contained in XML, JSON or even Microsoft Word or Adobe format files. Examples of the document databases are Apache CouchDB and MongoDb. The document databases are good for storing and analysing prescriptions, drug schedules, patient records, and the contracts written up between healthcare insurers and providers.
On textual data you perform lexical analytics such as word frequency distributions, co-occurrence (to find the number of occurrences of particular words in a sentence, paragraph or even a document), find sentences or paragraphs with particular words within a given distance apart, and other text analytics operations such as link and association analysis. The overarching goal is, essentially, to turn unstructured text into structured data, by applying natural language processing (NLP) and analytical methods.
For example, if a co-occurrence analysis found that BRCA1 and breast cancer regularly occurred in the same sentence, it might assume a relationship between breast cancer and the BRCA1 gene. Nowadays co-occurrence in text is often used as a simple baseline when evaluating more sophisticated systems.
Rule-based analyses make use of some a priori information, such as language structure, language rules, specific knowledge about how biologically relevant facts are stated in the biomedical literature, the kinds of relationships or variant forms that they can have with one another, or subsets or combinations of these. Of course the accuracy of a rule-based system depends on the quality of the rules that it operates on.
Statistical or machine-learning–based systems operate by building classifications, from labelling part of speech to choosing syntactic parse trees to classifying full sentences or documents. These are very useful to turn unstructured text into an analysable dataset. However, these systems normally require a substantial amount of already labelled training data. This is often time-consuming to create or expensive to acquire.
However, it’s important to keep in mind that much of the textual data requires disambiguation before you can process, make sense of, and apply analytics to it. The existence of ambiguity, such as multiple relationships between language and meanings or categories makes it very difficult to accurately interpret and analyse textual data. Acronym / slang / shorthand resolution, interpretation, standardisation, homographic resolution, taxonomy ontologies, textual proximity, cluster analysis and various other inferences and translations all form part of textual disambiguation. Establishing and capturing context is also crucial for unstructured text analytics – the same text can have radically different meanings and interpretations, depending on the context where it is used.
As an example of the ambiguities found in healthcare, “fat” is the official symbol of Entrez Gene entry 2195 and an alternate symbol for Entrez Gene entry 948. The distinction is not trivial – the first is associated with tumour suppression and with bipolar disorder, while the second is associated with insulin resistance and quite a few other unrelated phenotypes. If you get the interpretation wrong, you can miss or erroneously extract the wrong information.
An interesting class of big data is graph structures, where entities are related to each other in complex relationships like trees, networks or graphs. This type of data is typically neither large, nor unstructured, but graph structures of undetermined depth are very complex to store in relational or key-value pair structures, and even more complex to process using standard SQL. For this reason this type of data can be stored in a graph-oriented NoSQL database such as Neo4J, InfoGrid, InfiniteGraph, uRiKa, OrientDB or FlockDB.
Examples of graph structures include the networks of people that know each other, as you find on LinkedIn or Facebook. In healthcare a similar example is the network of providers linked to a group of practices or a hospital group. Referral patterns can be analysed to determine how specific doctors and hospitals team together to deliver improved healthcare outcomes. Graph-based analyses of referral patterns can also point out fraudulent behaviour, such as whether a particular doctor is a conservative or a liberal prescriber, and whether he refers patients to a hospital that charges more than double than the one just across the street.
Another useful graph-based analysis is the spread of a highly contagious disease through groups of people who were in contact with each other. An infectious disease clinic, for instance, should strive to have higher infection caseloads across such a network, but with lower actual infection rates.
A more deep-dive application of graph-based analytics is to study network models of genetic inheritance.
Like other graph-structured data, geospatial data itself is pretty structured – coordinates can simply be represented as pairs of coordinates. However, when analysing and optimising ambulance routes of different lengths, for example, the data is best stored and processed using a graph structures.
Geospatial analyses are also useful for hospital and practice location planning. For example, Epworth HealthCare group teamed up with geospatial group MapData Services to conduct an extensive analysis of demographic and medical services across Victoria. The analysis involved sourcing a range of data including Australian Bureau of Statistics figures around population growth and demographics, details of currently available health services, and the geographical distribution of particular types of conditions. The outcome was that the ideal location and services mix for a new $447m private teaching hospital should be in the much smaller city of Geelong, instead of in the much larger but services-rich city of Melbourne.
Sensor data often are also normally quite structured, with an aspect being measured, a measurement value and a unit of measure. The complexity comes in that for each patient or each blood sample test you often have a variable record structure with widely different aspects being measured and recorded. Some sources of sensor data also produce large volumes of data at high rates. Sensor data are often best stored in key-value databases, such as Riak, DynamoDB, Redis Voldemort, and sure, Hadoop.
Biosensors are now used to enable better and more efficient patient care across a wide range of healthcare operations, including telemedicine, telehealth, and mobile health. Typical analyses compare related sets of measurements for cause and effect, reaction predictions, antagonistic interactions, dependencies and correlations.
For example, biometric data, which includes data such as diet, sleep, weight, exercise, and blood sugar levels, can be collected from mobile apps and sensors. Outcome-oriented analytics applied to this biometric data, when combined with other healthcare data, can help patients with controllable conditions improve their health by providing them with insights on their behaviours that can lead to increases or decreases in the occurrences of diseases. Data-wise healthcare organisations can similarly use analytics to understand and measure wellness, apply patient and disease segmentation, and track health setbacks and improvements. Predictive analytics can be used to inform and drive multichannel patient interaction that can help shape lifestyle choices, and so avoid poor health and costly medical care.
Although there are merits in storing and processing complex big data, we need to ensure that the type of analytical processing possible on the big data sets lead to valuable enough new insights. The way in which the big data is structured often has an implication on the type of analytics that can be applied to it. Often, too, if the analytics are not properly applied to big data integrated with existing structured data, the results are not as meaningful and valuable as expected.
We need to be cognisant of the fact that there are many storage and analytics technologies available. We need to apply the correct storage structure that matches the data structure and thereby ensure that the correct analytics can be efficiently and correctly applied, which in turn will deliver new and valuable insights.
The quoted estimate seems a bit under…
http://www.abc.net.au/news/2014-03-06/australians-defrauding-medicare-hundreds-of-thousands-of-dollars/5302584
Video:
Updated Fri 7 Mar 2014, 1:23am AEDT
New figures show Medicare is being defrauded of hundreds of thousands of dollars each year.
Figures released to the ABC show the Federal Government has received more than 1,000 tip-offs of potential Medicare frauds to date this financial year.
It comes as debate continues over a proposal put to the Commission of Audit to charge a $6 co-payment for visits to the doctor, which would reduce costs to the health system.
The Department of Human Services says its hotline has received 1,116 Medicare-related tip-offs since July 1, 2013.
Officers have investigated 275 cases, which has translated into 34 cases submitted to the Commonwealth Department of Public Prosecutions and 12 convictions.
The value of those 12 cases adds up to an estimated $474,000, with fraudsters ripping off an average of almost $40,000 each.
Department figures suggest most of the frauds come from outside the doctor’s office.
Ten of the 12 prosecutions this year were members of the public. One involved a medical practice staff member and one a practice owner.
“The Department of Human Services takes all allegations of fraud seriously and seeks to investigate where sufficient information is provided to do so,” a spokeswoman said.
The annual review of doctors’ use of Medicare, the Professional Services Review, showed at least 19 doctors were required to repay more than $1 million between them in 2012-13.
One doctor billed Medicare for seeing more than 500 patients in a day, and more than 200 patients on several other days.
Other cases uncovered by the ABC include:
Nilay Patel, a former US-based certified specialist in healthcare compliance and law tutor at Swinburne University of Technology, says the fraud figures are the “tip of the iceberg”.
“There is a lot more that we do not know and that really comes from both camps from the patients and the medical service providers,” he said.
He says Australia is falling behind the United States at preventing, detecting and prosecuting healthcare frauds.
“The safeguards [in Australia] are quite inadequate, the detection is more reactive that proactive and whatever proactive mechanisms that are there I think they are woefully underdeveloped,” he said.
Federal Government authorities say they do not think Medicare fraud is widespread.
Minister for Human Services Marise Payne says the number of Medicare frauds are low compared to the number of transactions.
“I think when you consider that we have 344 million Medicare transactions a year it is a relatively smallish [problem] but that doesn’t mean it’s acceptable,” she said.
“One person committing a fraud effectively against the Australian taxpayer is one person too many.”
Ms Payne says the department uses sophisticated data matching and analytics to pick up potential frauds as well as its tip-off hotline.
The merger of Medicare with Centrelink also allows the bureaucracies to better share information and leads.
“The work we have done in that area is paying dividends,” Ms Payne said.
“There is more to do. The use of analytical data and risk profiling is highly sophisticated in the Centrelink space and we want to make sure we achieve the same levels in the Medicare space.”
The Australian Federal Police says it does not routinely gather statistics on the number of fake or counterfeit Medicare cards.
However, a spokesman says detections of counterfeit Medicare cards are rare.
“Intelligence to date indicates that the majority of Medicare cards seized that are of sufficient quality, are used as a form of identity, not intentionally to defraud Medicare,” a spokesman said.
A Customs and Border Protection spokeswoman says blank or fraudulent Medicare cards are not controlled under the Customs regulations and it is unable to provide seizure statistics.
The federal Ombudsman says he has not conducted any review or investigations into Medicare but did contribute to a 2009 inquiry into compliance audits on benefits.
The Medicare complaints detailed in the Ombudsman’s annual report relate to customers disputing Medicare refunds, not frauds.
Sydney man Tahir Abbas is sceptical about the Government’s claims that Medicare fraud is not widespread.
Mr Abbas detected at least 10 false bulk billing charges on his Medicare statement between November and January valued at almost $750.
He was not in the country when many of the charges were incurred.
The charges were from a western Sydney optometrist who told the ABC they were unable to explain the discrepancies.
They said while Mr Abbas was billed, they never received payment.
How many times do we go and check our statements for Medicare particularly. Maybe with credit cards, bank details but not with Medicare. These people are just looting the money.
Victim of Medicare fraud Tahir Abbas
The owner told the ABC the system would not allow them to receive bulk billing payments for more than one check-up in a two-year period.
Mr Abbas said he believed his card had been misused by others for their own benefit.
“I was very disgusted to be honest,” he said.
“It’s all bulk-billed and they are charging the Government. But in a way the Government is charging us so we are paying from our pocket – it’s all taxpayers’ money.”
He has urged people to check their Medicare statements.
“How many times do we go and check our statements for Medicare particularly. Maybe with credit cards, bank details but not with Medicare.
“These people are just looting the money.”
Medicare has told Mr Abbas they are investigating.
Technology and crime analyst Nigel Phair from the University of Canberra says the Medicare card is an easy to clone, low-tech card that has been around for three decades.
While it is low in value for identity check points, it is a well-respected document.
“The Medicare card carries no technology which gives it additional factors for verification or identification of users,” he said.
“It’s just a mag stripe on the back, very similar to a credit card from the 1990s without any chip or pin technologies, which are well known to be the way of the future.”
He says Medicare is vulnerable to abuse because people’s data is stored in many places such as doctors’ surgeries and pharmacies.
“It’s very easy to sail under the radar if you’re a fraudulent user. And like all good frauds you keep the value of the transactions low but your volume high,” he said.
“Because all we do have is anecdotal evidence and no hard statistics, we really don’t know how bad this issue is.”
Ms Payne does not support upgrading the quality of Medicare cards.
“The advice I have is that that is not really a large source of fraud and inappropriate practices,” she said.
Do you know more? Email investigations@abc.net.au
Topics: fraud-and-corporate-crime, health, health-administration, health-policy, government-and-politics, federal-government,law-crime-and-justice, australia
First posted Thu 6 Mar 2014, 12:00pm AEDT
“The best minds of my generation are thinking about how to make people click ads… That sucks.”
As a 23-year-old math genius one year out of Harvard, Jeff Hammerbacher arrived at Facebook when the company was still in its infancy. This was in April 2006, and Mark Zuckerberg gave Hammerbacher—one of Facebook’s first 100 employees—the lofty title of research scientist and put him to work analyzing how people used the social networking service. Specifically, he was given the assignment of uncovering why Facebook took off at some universities and flopped at others. The company also wanted to track differences in behavior between high-school-age kids and older, drunker college students. “I was there to answer these high-level questions, and they really didn’t have any tools to do that yet,” he says.
Over the next two years, Hammerbacher assembled a team to build a new class of analytical technology. His crew gathered huge volumes of data, pored over it, and learned much about people’s relationships, tendencies, and desires. Facebook has since turned these insights into precision advertising, the foundation of its business. It offers companies access to a captive pool of people who have effectively volunteered to have their actions monitored like so many lab rats. The hope—as signified by Facebook’s value, now at $65 billion according to research firm Nyppex—is that more data translate into better ads and higher sales.
After a couple years at Facebook, Hammerbacher grew restless. He figured that much of the groundbreaking computer science had been done. Something else gnawed at him. Hammerbacher looked around Silicon Valley at companies like his own, Google (GOOG), and Twitter, and saw his peers wasting their talents. “The best minds of my generation are thinking about how to make people click ads,” he says. “That sucks.”
You might say Hammerbacher is a conscientious objector to the ad-based business model and marketing-driven culture that now permeates tech. Online ads have been around since the dawn of the Web, but only in recent years have they become the rapturous life dream of Silicon Valley. Arriving on the heels of Facebook have been blockbusters such as the game maker Zynga and coupon peddler Groupon. These companies have engaged in a frenetic, costly war to hire the best executives and engineers they can find. Investors have joined in, throwing money at the Web stars and sending valuations into the stratosphere. Inevitably, copycats have arrived, and investors are pushing and shoving to get in early on that action, too. Once again, 11 years after the dot-com-era peak of the Nasdaq, Silicon Valley is reaching the saturation point with business plans that hinge on crossed fingers as much as anything else. “We are certainly in another bubble,” says Matthew Cowan, co-founder of the tech investment firm Bridgescale Partners. “And it’s being driven by social media and consumer-oriented applications.”
There’s always someone out there crying bubble, it seems; the trick is figuring out when it’s easy money—and when it’s a shell game. Some bubbles actually do some good, even if they don’t end happily. In the 1980s, the rise of Microsoft (MSFT), Compaq (HPQ), and Intel (INTC) pushed personal computers into millions of businesses and homes—and the stocks of those companies soared. Tech stumbled in the late 1980s, and the Valley was left with lots of cheap microprocessors and theories on what to do with them. The dot-com boom was built on infatuation with anything Web-related. Then the correction began in early 2000, eventually vaporizing about $6 trillion in shareholder value. But that cycle, too, left behind an Internet infrastructure that has come to benefit businesses and consumers.
This time, the hype centers on more precise ways to sell. At Zynga, they’re mastering the art of coaxing game players to take surveys and snatch up credit-card deals. Elsewhere, engineers burn the midnight oil making sure that a shoe ad follows a consumer from Web site to Web site until the person finally cracks and buys some new kicks.
This latest craze reflects a natural evolution. A focus on what economists call general-purpose technology—steam power, the Internet router—has given way to interest in consumer products such as iPhones and streaming movies. “Any generation of smart people will be drawn to where the money is, and right now it’s the ad generation,” says Steve Perlman, a Silicon Valley entrepreneur who once sold WebTV to Microsoft for $425 million and is now running OnLive, an online video game service. “There is a goodness to it in that people are building on the underpinnings laid by other people.”
So if this tech bubble is about getting shoppers to buy, what’s left if and when it pops? Perlman grows agitated when asked that question. Hands waving and voice rising, he says that venture capitalists have become consumed with finding overnight sensations. They’ve pulled away from funding risky projects that create more of those general-purpose technologies—inventions that lay the foundation for more invention. “Facebook is not the kind of technology that will stop us from having dropped cell phone calls, and neither is Groupon or any of these advertising things,” he says. “We need them. O.K., great. But they are building on top of old technology, and at some point you exhaust the fuel of the underpinnings.”
And if that fuel of innovation is exhausted? “My fear is that Silicon Valley has become more like Hollywood,” says Glenn Kelman, chief executive officer of online real estate brokerage Redfin, who has been a software executive for 20 years. “An entertainment-oriented, hit-driven business that doesn’t fundamentally increase American competitiveness.”
Hammerbacher quit Facebook in 2008, took some time off, and then co-founded Cloudera, a data-analysis software startup. He’s 28 now and speaks with the classic Silicon Valley blend of preternatural self-assurance and save-the-worldism, especially when he gets going on tech’s hottest properties. “If instead of pointing their incredible infrastructure at making people click on ads,” he likes to ask, “they pointed it at great unsolved problems in science, how would the world be different today?” And yet, other than the fact that he bailed from a sweet, pre-IPO gig at the hottest ad-driven tech company of them all, Hammerbacher typifies the new breed of Silicon Valley advertising whiz kid. He’s not really a programmer or an engineer; he’s mostly just really, really good at math.
Hammerbacher grew up in Indiana and Michigan, the son of a General Motors (GM) assembly-line worker. As a teenager, he perfected his curve ball to the point that college scouts from the University of Michigan and Harvard fought for his services. “I was either going to be a baseball player, a poet, or a mathematician,” he says. Hammerbacher went with math and Harvard. Unlike one of his more prominent Harvard acquaintances—Facebook co-founder Mark Zuckerberg—Hammerbacher graduated. He took a job at Bear Stearns.
On Wall Street, the math geeks are known as quants. They’re the ones who create sophisticated trading algorithms that can ingest vast amounts of market data and then form buy and sell decisions in milliseconds. Hammerbacher was a quant. After about 10 months, he got back in touch with Zuckerberg, who offered him the Facebook job in California. That’s when Hammerbacher redirected his quant proclivities toward consumer technology. He became, as it were, a Want.
At social networking companies, Wants may sit among the computer scientists and engineers, but theirs is the central mission: to poke around in data, hunt for trends, and figure out formulas that will put the right ad in front of the right person. Wants gauge the personality types of customers, measure their desire for certain products, and discern what will motivate people to act on ads. “The most coveted employee in Silicon Valley today is not a software engineer. It is a mathematician,” says Kelman, the Redfin CEO. “The mathematicians are trying to tickle your fancy long enough to see one more ad.”
Sometimes the objective is simply to turn people on. Zynga, the maker of popular Facebook games such as CityVille and FarmVille, collects 60 billion data points per day—how long people play games, when they play them, what they’re buying, and so forth. The Wants (Zynga’s term is “data ninjas”) troll this information to figure out which people like to visit their friends’ farms and cities, the most popular items people buy, and how often people send notes to their friends. Discovery: People enjoy the games more if they receive gifts from their friends, such as the virtual wood and nails needed to build a digital barn. As for the poor folks without many friends who aren’t having as much fun, the Wants came up with a solution. “We made it easier for those players to find the parts elsewhere in the game, so they relied less on receiving the items as gifts,” says Ken Rudin, Zynga’s vice-president for analytics.
These consumer-targeting operations look a lot like what quants do on Wall Street. A Want system, for example, might watch what someone searches for on Google, what they write about in Gmail, and the websites they visit. “You get all this data and then build very rapid decision-making models based on their history and commercial intent,” says Will Price, CEO of Flite, an online ad service. “You have to make all of those calculations before the Web page loads.”
Ultimately, ad-tech companies are giving consumers what they desire and, in many cases, providing valuable services. Google delivers free access to much of the world’s information along with free maps, office software, and smartphone software. It also takes profits from ads and directs them toward tough engineering projects like building cars that can drive themselves and sending robots to the moon. The Era of Ads also gives the Wants something they yearn for: a ticket out of Nerdsville. “It lets people that are left- brain leaning expand their career opportunities,” says Doug Mack, CEO of One Kings Lane, a daily deal site that specializes in designer goods. “People that might have been in engineering can go into marketing, business development, and even sales. They can get on the leadership track.” And while the Wants plumb the depths of the consumer mind and advance their own careers, investors are getting something too, at least on paper: almost unimaginable valuations. Just since the fourth quarter, Zynga has risen 81 percent in value, to a cool $8 billion, according to Nyppex.
No one is suggesting that the top tier of ad-centric companies—Facebook, Google—is going down should the bubble pop. As for the next tier or two down, where a profusion of startups is piling into every possible niche involving social networking and ads—the fate of those companies is anybody’s guess. Among the many unveilings in March, one stood out: An app called Color, made by a seven-month-old startup of the same name. Color lets people take and store their pictures. More than that, it uses geolocation and ambient-noise-matching technology to figure out where a person is and then automatically shares his photos with other nearby people and vice versa. People at a concert, for example, could see photos taken by all the other people at that concert. The same goes for birthday parties, sporting events, or a night out at a bar. The app also shares photos among your friends in the Color social network, so you can see how Jane is spending her vacation or what John ate for breakfast, if he bothered to take a photo of it.
Whether Color ends up as a profitable app remains to be seen. The company has yet to settle on a business model, although its executives say it’ll probably incorporate some form of local advertising. Figuring out all those location-based news feeds on the fly requires serious computational power, and that part of the business is headed by Color’s math wizard and chief product officer, DJ Patil.
Patil’s Silicon Valley pedigree is impeccable. His father, Suhas Patil, emigrated from India and founded the chip company Cirrus Logic (CRUS). DJ struggled in high school, did some time at a junior college, and through force of will decided to get good at math. He made it into the University of California at San Diego, where he took every math course he could. He became a theoretical math guru and went on to research weather patterns, the collapse of sardine populations, the formation of sand dunes, and, during a stint for the Defense Dept., the detection of biological weapons in Central Asia. “All of these things were about how to use science and math to achieve these broader means,” Patil says. Eventually, Silicon Valley lured him back. He went to work for eBay (EBAY), creating an antifraud system for the retail site. “I took ideas from the bioweapons threat anticipation project,” he says. “It’s all about looking at a network and your social interactions to find out if you’re good or bad.”
Patil, 36, agonized about his jump away from the one true path of Silicon Valley righteousness, doing gritty research worthy of his father’s generation. “There is a time in life where that kind of work is easy to do and a time when it’s hard to do,” he says. “With a kid and a family, it was getting hard.”
Having gone through a similar self-inquiry, Hammerbacher doesn’t begrudge talented technologists like Patil for plying their trade in the glitzy land of networked photo sharing. The two are friends, in fact; they’ve gotten together to talk about data and the challenges in parsing vast quantities of it. At social networking companies, Hammerbacher says, “there are some people that just really buy the mission—connecting people. I don’t think there is anything wrong with those people. But it just didn’t resonate with me.”
After quitting Facebook in 2008, Hammerbacher surveyed the science and business landscape and saw that all types of organizations were running into similar problems faced by consumer Web companies. They were producing unprecedented amounts of information—DNA sequences, seismic data for energy companies, sales information—and struggling to find ways to pull insights out of the data. Hammerbacher and his fellow Cloudera founders figured they could redirect the analytical tools created by Web companies to a new pursuit, namely bringing researchers and businesses into the modern age.
Cloudera is essentially trying to build a type of operating system, à la Windows, for examining huge stockpiles of information. Where Windows manages the basic functions of a PC and its software, Cloudera’s technology helps companies break data into digestible chunks that can be spread across relatively cheap computers. Customers can then pose rapid-fire questions and receive answers. But instead of asking what a group of friends “like” the most on Facebook, the customers ask questions such as, “What gene do all these cancer patients share?”
Eric Schadt, the chief scientific officer at Pacific Biosciences, a maker of genome sequencing machines, says new-drug discovery and cancer cures depend on analytical tools. Companies using Pacific Bio’s machines will produce mountains of information every day as they sequence more and more people. Their goal: to map the complex interactions among genes, organs, and other body systems and raise questions about how the interactions result in certain illnesses—and cures. The scientists have struggled to build the analytical tools needed to perform this work and are looking to Silicon Valley for help. “It won’t be old school biologists that drive the next leaps in pharma,” says Schadt. “It will be guys like Jeff who understand what to do with big data.”
Even if Cloudera doesn’t find a cure for cancer, rid Silicon Valley of ad-think, and persuade a generation of brainiacs to embrace the adventure that is business software, Price argues, the tech industry will have the same entrepreneurial fervor of yesteryear. “You can make a lot of jokes about Zynga and playing FarmVille, but they are generating billions of dollars,” the Flite CEO says. “The greatest thing about the Valley is that people come and work in these super-intense, high-pressure environments and see what it takes to create a business and take risk.” A parade of employees has left Google and Facebook to start their own companies, dabbling in everything from more ad systems to robotics and publishing. “It’s almost a perpetual-motion machine,” Price says.
Perpetual-motion machines sound great until you remember that they don’t exist. So far, the Wants have failed to carry the rest of the industry toward higher ground. “It’s clear that the new industry that is building around Internet advertising and these other services doesn’t create that many jobs,” says Christophe Lécuyer, a historian who has written numerous books about Silicon Valley’s economic history. “The loss of manufacturing and design knowhow is truly worrisome.”
Dial back the clock 25 years to an earlier tech boom. In 1986, Microsoft, Oracle (ORCL), and Sun Microsystems went public. Compaq went from launch to the Fortune 500 in four years—the quickest run in history. Each of those companies has waxed and waned, yet all helped build technology that begat other technologies. And now? Groupon, which e-mails coupons to people, may be the fastest-growing company of all time. Its revenue could hit $4 billion this year, up from $750 million last year, and the startup has reached a valuation of $25 billion. Its technological legacy is cute e-mail.
There have always been foundational technologies and flashier derivatives built atop them. Sometimes one cycle’s glamour company becomes the next one’s hard-core technology company; witness Amazon.com’s (AMZN) transformation over the past decade from mere e-commerce powerhouse to e-commerce powerhouse and purveyor of cloud-computing capabilities to other companies. Has the pendulum swung too far? “It’s a safe bet that sometime in the next 20 months, the capital markets will close, the music will stop, and the world will look bleak again,” says Bridgescale Partners’ Cowan. “The legitimate concern here is that we are not diversifying, so that we have roots to fall back on when we enter a different part of the cycle.”
Michael Bloomberg was laughed at for suggesting that New York City businesses limit soda serving sizes. It was never a perfect plan, but his public shaming shows how closely we equate food with ‘freedom.’ The problem is, there is no freedom in addiction. As the Nature Neurosciencestudy showed above, rats and humans alike will overeat (or eat less healthy food options) even if they know better.
Hence the magic bullet at the center of McDonald’s letter: a precise combination of fat, sugar and salt that keeps us craving more. As NY Timesreporter and author of Salt Sugar Fat: How the Food Giants Hooked UsMichael Moss said in an interview
These are the pillars of processed foods, the three ingredients without which there would be no processed foods. Salt, sugar and fat drive consumption by adding flavor and allure. But surprisingly, they also mask bitter flavors that develop in the manufacturing process. They enable these foods to sit in warehouses or on the grocery shelf for months. And, most critically to the industry’s financial success, they are very inexpensive.
PN: The fallacy in the rump of this discussion is that cigarettes are not that more harmful than a big mac. I’m just as likely to die from smoking a single cigarette in front of you, as I am if I were to eat a big mac in front of you. The problems arise when you smoke/eat these products every day of your life.
http://bigthink.com/21st-century-spirituality/should-big-food-pay-for-our-rising-obesity-costs
Paul McDonald didn’t expect his letter to go public. The Valorem Law Group partner had queried sixteen states, asking leaders to consider investigating Big Food’s potential role in paying for a percentage of the health system’s skyrocketing obesity costs. The Chamber of Commerce got wind of this letter and made it public, setting off a national debate over food marketing, ingredient manipulation and personal responsibility.
McDonald’s premise is simple enough: if large food companies are purposefully creating addictive foods to ensure consumer loyalty, adding to the rising obesity levels in this country, they should be responsible for covering costs associated with treatment. The backlash was immediate and biting.
Comparisons to the Big Tobacco companies came first to mind. In the 1998 Tobacco Masters Settlement Agreement, major players in the tobacco industry agreed to pay $246 billion to offset health risks and diseases associated with its product. Critics of McDonald’s idea believe there is no link between tobacco and food.
On the face of it, this would appear true: you don’t need to smoke, but eating is a necessity. Smoking is a choice, and therefore if you choose to smoke, you pay the consequences. Eating falls into an entirely different category.
Yet the neural mechanisms might be similar. A 2010 study in Nature Neuroscience found that rats consumed well past their limits when offered high-calorie foods such as bacon, sausage and cake, speculating that humans, when faced with an equivalent scenario, also choose to overeat.
Harvard University Professor of Medicine, Emeritus David Blumenthal’s study, Neurobiology of Food Addiction, found a similar link between food and drug abuse. In the summary he writes
Work presented in this review strongly supports the notion that food addiction is a real phenomenon…although food and drugs of abuse act on the same central networks, food consumption is also regulated by peripheral signaling systems, which adds to the complexity of understanding how the body regulates eating, and of treating pathological eating habits.
The argument against food addiction is a tough one, waged by industry insiders who want to keep 60,000 products on American shelves. The real question, however, is: are food companies purposefully producing addictive foods that change our neurobiology? If so, should they be held economically accountable?
American obesity costs are currently $147 billion per year. The CDC estimates that 35.7% of adults and 17% of children ages 2-19 are obese—a number that has risen dramatically over the last two decades. A joint report between Trust for America’s Health and the Robert Wood Johnson Foundation estimates that 44% of American adults will be obese by 2030. The report predicts that will add between $48-66 billion to our costs, some of which is paid for by taxpayers.
Yet food is such an emotional topic. For example, when informing someone that I’m vegan, they immediately let me know why they could never do such a thing (I didn’t ask) or that it’s ‘wrong’ for them, and sometimes by extension, me (last week’s annual blood work shows me in perfect shape).
Michael Bloomberg was laughed at for suggesting that New York City businesses limit soda serving sizes. It was never a perfect plan, but his public shaming shows how closely we equate food with ‘freedom.’ The problem is, there is no freedom in addiction. As the Nature Neurosciencestudy showed above, rats and humans alike will overeat (or eat less healthy food options) even if they know better.
Hence the magic bullet at the center of McDonald’s letter: a precise combination of fat, sugar and salt that keeps us craving more. As NY Timesreporter and author of Salt Sugar Fat: How the Food Giants Hooked UsMichael Moss said in an interview
These are the pillars of processed foods, the three ingredients without which there would be no processed foods. Salt, sugar and fat drive consumption by adding flavor and allure. But surprisingly, they also mask bitter flavors that develop in the manufacturing process. They enable these foods to sit in warehouses or on the grocery shelf for months. And, most critically to the industry’s financial success, they are very inexpensive.
Inexpensive to companies, not to consumers. Paul McDonald is striking an important nerve in how we manufacture, distribute and consume food in our country. There will be a lot of resistance and debate from both industry and citizens. But if we don’t begin this conversation now, our national and mental health is only going to continue to decline.
Image: Aliwak/shutterstock.com
Awesome quote from th CIA re. gait identification:
If there’s one entity that knows the value of the health data uploaded to these devices, it’s the CIA. Last year, at a data conference in New York, the CIA’s chief technology officer, Ira Hunt, gave a talk on big data. During the discussion, he told the crowd that he carries a Fitbit. “We like these things,” he said. “What’s really most intriguing is that you can be 100% guaranteed to be identified by simply your gait—how you walk.”
Are Fitbit, Nike, and Garmin Planning to Sell Your Personal Fitness Data?
These popular fitness companies say they aren’t selling your info, but privacy advocates and the FTC worry that might change.
—By Dana Liebelson | Fri Jan. 31, 2014 3:00 AM GMT
Lately, fitness-minded Americans have started wearing sporty wrist-band devices that track tons of data: Weight, mile splits, steps taken per day, sleep quality, sexual activity, calories burned—sometimes, even GPS location. People use this data to keep track of their health, and are able send the information to various websites and apps. But this sensitive, personal data could end up in the hands of corporations looking to target these users with advertising, get credit ratings, or determine insurance rates. In other words, that device could start spying on you—and the Federal Trade Commission is worried.
“Health data from [a woman’s] connected device, may be collected and then sold to data brokers and other companies she does not know exist,” Jessica Rich, director of the Bureau for Consumer Protection at the Federal Trade Commission, said in a speech on Tuesday for Data Privacy Day. “These companies could use her information to market other products and services to her; make decisions about her eligibility for credit, employment, or insurance; and share with yet other companies. And many of these companies may not maintain reasonable safeguards to protect the data they maintain about her.”
Several major US-based fitness device companies contacted by Mother Jones—Fitbit, Garmin, and Nike—say they don’t sell personally identifiable information collected from fitness devices. But privacy advocates warn that the policies of these firms could allow them to sell data, if they ever choose to do so.
Nike, which makes the Nike + Fuel Band, says in its privacy policy that the company may collect a host of personal information, but doesn’t say that it can be shared with advertising companies. Joy Davis Fair, a Nike spokesperson, says that the company, “does not share consumer data” with outside advertisers, but selectively shares it with other companies under the Nike’s corporate umbrella, including Converse and Hurley. Garmin’s policy says that users have to consent in order for the company to sell personal information. A Garmin spokesman says the company doesn’t sell personal or aggregated information to advertisers, and doing so isn’t part of the company’s business model. (Polar Flow, which makes the Polar Loop band, is the only company with a privacy policy that explicitly says it won’t sell personally identifiable data for advertising. It is based in Finland and subject to stringent European Union privacy laws.)
Jeffrey Chester, executive director for the Center for Digital Democracy, says that these privacy policies are so broad that they could allow the companies to sell health data—even if they aren’t doing so now. “When companies promise that they aren’t selling your data, that’s because they haven’t developed a business model to do so yet,” Chester says.
Scott Peppet, a University of Colorado law school professor, agrees that companies like Fitbit will eventually move toward sharing this data. “I can paint an incredibly detailed and rich picture of who you are based on your Fitbit data,” he said at a FTC conference last year.“That data is so high quality that I can do things like price insurance premiums or I could probably evaluate your credit score incredibly accurately.”
Even if the companies that make these devices aren’t selling the data, there is another potential privacy concern. Users can send their data to dozens of third-party fitness apps on their phone. Once users do that, the data becomes subject to the privacy policies of the app companies, and these policies do not afford much protection, according to the Privacy Rights Clearinghouse. The group examined 43 popular health and fitness apps last year, and found that, “there are considerable privacy risks for users.” A spokesperson for the FTC told Mother Jones that “fitness devices often work by having apps associated, and [Privacy Rights Clearinghouse’s] analysis here may be relevant.”
If there’s one entity that knows the value of the health data uploaded to these devices, it’s the CIA. Last year, at a data conference in New York, the CIA’s chief technology officer, Ira Hunt, gave a talk on big data. During the discussion, he told the crowd that he carries a Fitbit. “We like these things,” he said. “What’s really most intriguing is that you can be 100% guaranteed to be identified by simply your gait—how you walk.”
ReporterDana Liebelson is a reporter in Mother Jones’ Washington bureau. Her work has also appeared inThe Week, TIME‘s Battleland, Truthout, OtherWords and Yahoo! News. RSS | TWITTER
http://gigaom.com/2013/03/20/even-the-cia-is-struggling-to-deal-with-the-volume-of-real-time-social-data/2/
CIA slides:
In fact, if you think about your mobile sensor platform, there’s a really cool little app – Activity Tracker. It’s a little Android app – have you guys seen this anywhere? What they’ve discovered is fundamentally they take your 3-axis accelerometer on your phone – I actually carry a Fitbit. You guys know the Fitbit, right? It’s just a simple 3-axis accelerometer. We like these things because they don’t have any – well, I won’t go into that [laughter]. What happens is, they discovered that just simply by looking at the data what they can find out is with pretty good accuracy what your gender is, whether you’re tall or you’re short, whether you’re heavy or light, but what’s really most intriguing is that you can be 100% guaranteed to be identified by simply your gait – how you walk.
Now this could be a really good thing. Think about this as a security app. If you’re walking along and you want to access your bank code, maybe it could become simplified because they can with absolute assurance know it’s you by your gait trying to do something with your bank. On the other hand, if you don’t want to be found or you want to protect yourself, maybe you don’t want to have somebody know what your gait looks like so they can figure out where you are at all times.
http://www.foodnavigator.com/Regions/Middle-East/Overweight-Middle-East-struggles-with-heart-disease-and-diabetes/
The Middle East is grappling with a rise in non-communicable diseases such as heart disease and diabetes, the roots of which are in a rise in obesity among its populace, a new study has found.
According to report, ischemic heart disease is now the leading cause of death in middle and high-income Arab nations – and it comes in at number 4 even in the lowest-income countries in the region.
Stroke is also a leading cause of death, and Kuwait, Lebanon, Qatar, Saudi Arabia, Bahrain and the UAE are now among the 10 nations with the highest global prevalence of type 2 diabetes, it said.
The study’s authors put most of the blame for this on the change in dietary habits among the region’s population.
Fat of the land
The report added that the prevalence of overweight and obesity has increased in both young and adult populations of GCC countries, including Kuwait, Qatar, Saudi Arabia, and Bahrain.
According to the report, 66-75% of the adult population (over 18) and 25-40% of children and adolescents (under 18) in the Middle East are estimated to be overweight or obese.
“The traditional Arab diet has changed from high-fibre and low-fat food with increased integration of the Arab world into the global market over the past four decades,” the study’s authors said.
“Unhealthy dietary habits are prevalent in children, adolescents, and adults, especially in the wealthy GCC countries where a wide variety of global fast-food chains are near ubiquitous,” they added.
According to the report, people in the Arab countries have a high intake of fast food and carbonated beverages and a low intake of milk, fruits, and vegetables, and frequently consume snacks rich in calories, salt, and fat between meals.
Pricing policies?
According to the report, national policies, programmes, and action plans to improve diet and increase physical activity are undeniably important for non-communicable disease prevention.
“But the realities of implementation are likely to be very different from the written policies,” the authors said.
According to the results of a review of diet and physical activity policies in low-income and middle-income countries, only Jordan had a policy that addressed all four risk factors: salt, fat, fruits and vegetables, and physical activity.
“In particular, the review reported that diet and physical activity policies tended not to be associated with specific action plans, timelines, and budgets, and they were also mostly focused on individual behavioural changes,” they said.
“Policies that link to specific budgets and priority actions, and involve a broader range of stakeholders, are needed. Importantly, pricing regulations are needed to ensure that fruits and vegetables are more affordable than processed foods, thus targeting both obesity and micronutrient deficiencies.”
Salt and trans fats need attention
According to the authors, even slight reductions in salt intake will result in substantial reductions in medical costs and cardiovascular events.
“Reduction in salt intake can be achieved with behaviour modification efforts (through advertising and health education campaigns) and reformulation of food products by industry. In the Arab world, bread is a big source of salt in the diet, and should be the first target for reformulation by gradual reduction,” they said.
The authors pointed out that in high-income and middle-income countries, reduction of trans-fat consumption has been addressed through mandatory labelling of the trans-fat content in foods and voluntary agreements.
“But little information about trans-fat intake in the Arab world is available. A recent study in Jordan showed a high and variable content of trans fat in both locally produced and imported foods,” they said.
“The WHO has proposed various policies to reduce trans-fat intake, including further studies on trans fat with respect to labelling, pricing regulations, and import restrictions. Health education campaigns are needed to educate consumers about trans fats,” they recommended.
Source: The Lancet
Non-communicable diseases in the Arab world
doi:10.1016/S0140-6736(13)62383-1
Authors: Dr. Hanan F Abdul Rahim. Prof Abla Sibai, Yoused Khader, Prof Nahla Hwalla, Ibtihal Fadhil, Huda Alsiyabi, Awad Mataria, Shanthi mendis, Prof Ali H Mokdad, Abdullatid Husseini
Heart Foundation lays it all down… we need to lose a combined 120million KGs to return to normal healthy weight range… not as easy as it sounds.
http://www.medicalobserver.com.au/news/being-overweight-or-obese-now-the-norm
AUSTRALIANS need to lose a combined 120 million kilograms to return to a healthy weight range.
The average Australian man now weighs 85.9kg – that’s 6.5kg heavier than he was in 1989 – according to a National Heart Foundation analysis on the severity of the nation’s weight problem.
A breakdown of Heart Foundation national health surveys and government data also revealed that the average woman has gained 5.7kg in the past 25 years and now tips the scales at 71.1kg.
The Heart Foundation’s national director of cardiovascular health, Dr Rob Grenfell, said two-thirds of Australians now fall outside the healthy weight range, with nearly half a million people morbidly obese (BMI > 40).
“To return to a healthy weight range, an average man would need to lose 8.9kg and a woman would need to lose 5.7kg,” Dr Grenfell said.
“The combined weight loss required is just short of 120 million kilograms across the nation.”
The analysis highlights that the average BMI for men is up from 25.3 to 27.9 since 1989, and the average for women is up from 24.3 to 27.2.
Obesity has increased from 8.4% of the population in 1980, to 28.3% in 2011–12.
“It’s scary that two in three Australians are now above the healthy weight range, making overweight and obese weight ranges more ‘normal’ than healthy,” he said.
“The healthiest BMI is relatively lean, at around 22.5–24.9, which is equivalent to a weight of 70–77kg for an Australian man of average height and 59–65kg for an Australian woman of average height.”
In comparison to 1980, the proportion of obese adult Australians has tripled, while the number of people in the healthy weight range has almost halved.
WA and Queensland now have the highest average male BMIs at 28.2, according to the Australian Health Survey of 2011/12, with the highest average female BMIs, 27.7, occurring in SA and Tasmania.
Victoria has the lowest average BMIs at 27.6 for men and 26.9 for women.
A market snapshot of wearables… useful for presentations.
Want A Neat Overview Of What’s Going On In Wearables? Point Your Eyes Right Here…
Wearables are so hot right now. Apple iWatch rumours are in rude health. Google isapparently looking (beyond Glass) at picking up and strapping onto its business anotherstartup in the wearables space (guesses for which in the comments pls).
Jawbone, maker of the UP fitness tracker bangle (and apparently not the company in Google’s Glassy sights), is running sweat-free towards an IPO. Action camera maker GoPro — ok, not technically a wearables company but the point of its cameras are that they are, y’know, wearable — has already filed for one. Smartwatch maker Pebble has raised a tonne of money since 2012, first via Kickstarter and then, off the back of its snowballing crowdfunder, from VC checkbooks.
Even though the genuine usefulness of bits of technology that you strap to your person still has a lot of proving to do – vs the intrusion (both visual, with a lot of these early devices being best described as uuuuuuuugggglllyyy; and, more importantly, the sensitive personal data being captured and monetized) – it’s the big huge lucrative potential that’s exciting makers and investors.
Mature Western markets are saturated with smartphones — ergo step forward sensor-stuffed wearables as the next growth engine for device makers. Devices whose literal positioning on our bodies enables them to gather far more intimate data on the lives and (physical) habits of users than previous generations of consumer mobiles. If only we can be persuaded to wear this stuff.
Yesterday analyst Canalys suggested 2014 will be the year for the wearables category becomes a “key consumer technology” — with more than 17 million wearable bands (alone) forecast to ship this year, rising to 23 million by 2015, and more than 45 million by 2017.
So that’s only wearable tech targeting the wrist, such as the Fitbit fitness tracker and Samsung’s Galaxy Gear smartwatch — it does not include devices aiming to squat on other body-parts (such as Google Glass). In short: tech makers gonna put a smart ring on it. Many are already trying.
On the ‘who is already making what’ front, wearable tech research and consulting firm Vandrico has put together this neat overview of the space — tracking the number of devices in existence; areas of market focus; and even which parts of the body are being targeted most.
(The most popular anatomical target for wearables is the wrists, since you’re curious — with 56 devices vying for that small patch of flesh; followed by the head, with 34 devices wanting to cling to it. On the flip side, the least popular body part for wearables thus far is apparently the hand, with just two devices listed, although the data doesn’t delve into the crotch region, so, yeah, there’s there too. Makers apparently not falling over themselves to fashion iCodpieces…).
According to Vandrico, there are some 115 wearables in play already; with an average selling price of $431; and with lifestyle, fitness and medical being the most popular market areas targeted (in that order).
The researcher has also taken the time to list and profile every single one of the 115 wearables it reckons are currently in play, so you don’t have to — from 3L Labs Footlogger to the ZTE Bluewatch (another mobile maker doing a smartwatch, who knew?).
Or at least all of the wearables its research has turned up. It’s asking for submissions for missing devices so it can keep expanding this database. (I’m going to throw the Fin into the ring on that front.)
Click here to check out — and start quantifying — the data for yourself.
[Image by IntelFreePress via Flickr]
http://www.foodnavigator-asia.com/Markets/Oz-economy-outpaces-confectionery-growth/
The Australian confectionery market is growing slower than the country’s economy, according to a report from Market Line.
The research firm told ConfectioneryNews that stable growth in the Australian confectionery market over the past five years was forecast to continue to 2017, but said the country’s economy was growing faster.
Behind overall growth
“If we look at GDP between 2008 and follow it through to the end of our forecast period in 2017, we see an average growth rate of just below 6%. In comparison, the confectionery market in Australia is set to have an average growth rate of 2.6% a year, over the same period, meaning that this market is growing at a slower rate than the Australian economy,” Market Line said.
Chocolate dominates the Australian confectionery market and accounted for over half of value sales in the sector in 2012, while sugar confectionery accounted for around a quarter of revenues in the same year.
Market Line said cereal bars, gum and chocolate sales were expected to slowly decelerate up to 2017, meanwhile sugar confectionery sales were expected to grow slowly.
In a separate report released last year, Leatherhead estimated that the Australian confectionery market was worth 255,000 tonnes in volume in 2012, representing a 10% increase from 2008.
Competitive neighbors
Australia is Asia-Pacific’s third largest confectionery market, with Japan and China taking the top spots. “Australia has, however, grown at a faster compound annual growth rate (CAGR) than the Japanese market between 2008 and 2012 at 4.8% as opposed to 0.5%,” Market Line said.
The Chinese market has grown at over double the rate of the Australian market at a CAGR of 4.8%, where sugar confectionery is the dominant segment.
Looking at Australian confectionery on this global stage, the Leatherhead report put Australia’s per capita consumption of chocolate at 6.3kg, higher than New Zealand (4.8kg) but below Switzerland (11.9kg) and the UK (9.4kg).
