Data from Health Care Reviews Could Power "Yelp for Health Care" Startups

Data-driven decision engines will need patient experience to complete the feedback loop.

Alex Howard, O'Reilly Radar, October 17, 2012

Given where my work and health has taken me this year, I’ve been thinking much more about the relationship of the Internet and health data to accountability and patient-driven health care.

When I was looking for a place in Maine to go for care this summer, I went online to look at my options. I consulted hospital data from the government at HospitalCompare.HHS.gov and patient feedback data on Yelp, and then made a decision based upon proximity and those ratings. If I had been closer to where I live in Washington D.C., I would also have consulted friends, peers or neighbors for their recommendations of local medical establishments.

My brush with needing to find health care when I was far from home reminded me of the prism that collective intelligence can now provide for the treatment choices we make, if we have access to the Internet.

Patients today are sharing more of their health data and experiences online voluntarily, which in turn means that the Internet is shaping health care. There’s a growing phenomenon of “e-patients” and caregivers going online to find communities and information about illness and disability.

Aided by search engines and social media, newly empowered patients are discussing health conditions with others suffering from disease and sickness — and they’re taking that peer-to-peer health care knowledge into their doctors’ offices with them, frequently on mobile devices. E-patients are sharing their health data of their own volition because they have a serious health condition, want to get healthy, and are willing.

From the perspective of practicing physicians and hospitals, the trend of patients contributing to and consulting on online forums adds the potential for errors, fraud, or misunderstanding. And yet, I don’t think there’s any going back from a networked future of peer-to-peer health care, anymore than we can turn back the dial on networked politics or disaster response.

What’s needed in all three of these areas is better data that informs better data-driven decisions. Some of that data will come from industry, some from government, and some from citizens.

This fall, the Obama administration proposed a system for patients to report medical mistakes. The system would create a new “consumer reporting system for patient safety” that would enable patients to tell the federal government about unsafe practices or errors. This kind of review data, if validated by government, could be baked into the next generation of consumer “choice engines,” adding another layer for people, like me, searching for care online.

There are precedents for the collection and publishing of consumer data, including the Consumer Product Safety Commission’s public complaint database at SaferProducts.gov and the Consumer Financial Protection Bureau’s complaint database. Each met with initial resistance by industry but have successfully gone online without massive abuse or misuse, at least to date.

It will be interesting to see how medical associations, hospitals and doctors react. Given that such data could amount to government collecting data relevant to thousands of “Yelps for health care,” there’s both potential and reason for caution. Health care is a bit different than product safety or consumer finance, particularly with respect to how a patient experiences or understands his or her treatment or outcomes for a given injury or illness. For those that support or oppose this approach, there is an opportunity for public comment on proposed data collection at the Federal Register.

The power of performance data
Combining patients review data with government-collected performance data could be quite powerful in helping to drive better decisions and adding more transparency to health care.
In the United Kingdom, officials are keen to find the right balance between open data, transparency and prosperity.

“David Cameron, the Prime Minister, has made open data a top priority because of the evidence that this public asset can transform outcomes and effectiveness, as well as accountability,” said Tim Kelsey, in an interview this year. He used to head up the United Kingdom’s transparency and open data efforts and now works at its National Health Service.

“There is a good evidence base to support this,” said Kelsey. “Probably the most famous example is how, in cardiac surgery, surgeons on both sides of the Atlantic have reduced the number of patient deaths through comparative analysis of their outcomes.”

More data collected by patients, advocates, governments and industry could help to shed light on the performance of more physicians and clinics engaged in other expensive and lifesaving surgeries and associated outcomes.

Should that be extrapolated across the medical industry, it’s a safe bet that some medical practices or physicians will use whatever tools or legislative influence they have to fight or discredit websites, services or data that puts them in a poor light. This might parallel the reception that BrightScope’s profiles of financial advisors have received in industry.

When I talked recently with Dr. Atul Gawande about health data and care givers, he said more transparency in these areas is crucial:

“As long as we are not willing to open up data to let people see what the results are, we will never actually learn. The experience of what happens in fields where the data is open is that it’s the practitioners themselves that use it.”

In that context, health data will be the backbone of the disruption in health care ahead. Part of that change will necessarily have to come from health care entrepreneurs and watchdogs connecting code to research. In the future, a move to open science and perhaps establish a health data commons could accelerate that change.

The ability of caregivers and patients alike to make better data-driven decisions is limited by access to data. To make a difference, that data will also need to be meaningful to both the patient and the clinician, said Dr. Gawande. He continued:

“[Health data] needs to be able to connect the abstract world of data to the physical world of what really happens, which means it has to be timely data. A six-month turnaround on data is not great. Part of what has made Wal-Mart powerful, for example, is they took retail operations from checking their inventory once a month to checking it once a week and then once a day and then in real-time, knowing exactly what’s on the shelves and what’s not. That equivalent is what we’ll have to arrive at if we’re to make our systems work. Timeliness, I think, is one of the under-recognized but fundamentally powerful aspects because we sometimes over prioritize the comprehensiveness of data and then it’s a year old, which doesn’t make it all that useful. Having data that tells you something that happened this week, that’s transformative.”

Health data, in other words, will need to be open, interoperable, timely, higher quality, baked into the services that people use, and put at the fingertips of caregivers, as US CTO Todd Park explains in the video below:

There is more that needs to be done than simply putting “how to live better” information online or into an app. To borrow a phrase from Robert Kirkpatrick, for data to change health care, we’ll need to apply the wisdom of the crowds, the power of algorithms and the intuition of experts to find meaning in health data and help patients and caregivers alike make better decisions.

That isn’t to say that health data, once published, can’t be removed or filtered. Witness the furor over the removal of a malpractice database from the Internet last year, along with its restoration.

But as more data about doctors, services, drugs, hospitals and insurance companies goes online, the ability of those institutions to control public perception of the institutions will shift, just as it has with government and media. Given flaws in devices or poor outcomes, patients deserve such access, accountability and insight.

Enabling better health-data-driven decisions to happen across the world will be far from easy. It is, however, a future worth building toward.

IBM's Watson Is Learning Its Way To Saving Lives

A few years ago, IBM’s new computer was a game-playing curiosity. Now Watson is poised to change the way human beings make decisions about medicine, finance, and work.

Jon Gertner, Fast Company, October 15, 2012.

The woman was gravely ill. Her name was Ms. Yamato. Thirty-seven years old, born in Osaka, Japan, she had never smoked, and yet there it was anyway: a spot on her lung.
A doctor had already performed a bronchoscopy and had made the diagnosis of cancer. Then he referred the patient to Mark Kris, an oncologist at Memorial Sloan-Kettering Cancer Center in New York. Seated alongside me in his office on the Upper East Side of Manhattan, Kris is showing me Ms. Yamato's electronic medical record on an iPad. "I'm preparing for the first visit," he explains, swiping the screen to show what that entails. He's interested in running at least two tests on the patient. The first is an MRI, to find out if the cancer has spread to her brain. The second involves a deeper diagnostic regimen. Lung cancer tumors are not all the same; there are thousands of variations. So a test that examines the mutations within a tumor will be crucial, he says. It so happens that cancer patients born in East Asia who have never smoked often have a particular mutation that responds well to a medication by the name of Erlotinib. That may be the case here. One can hope.

Over the past year, IBM executives have come to believe that Watson represents the first machine of the third computer age.

The woman is not real. She happens to be a character within an app that IBM has created for Watson, its new computer. Watson's special talent, its reason for being, is a singular ability to grasp the intricacies of human language and answer exceedingly difficult questions. You may have heard about Watson already. Back in 2007, a group of computer engineers at IBM's research labs in upstate New York began building the machine--named for IBM's founder, Thomas J. Watson--with the goal of creating a question-and-answer technology that would be more authoritative and powerful than anything on the planet. The initial objective of the Watson group was simple: to win in the game show Jeopardy!, something Watson famously achieved in February 2011. Yet the group had a far more important goal: to turn Watson into a business, hopefully one of some scale. So starting in late 2009, a business development team at IBM began holding meetings outside the company in an effort to understand the ultimate worth of this new technology. No doubt it could be a business one day. But what kind of business?

"The first thing that hit us about Watson," recalls John Kelly, IBM's chief of research, "was that this thing could be applied almost anywhere." Early on, IBM executives decided to focus on a field in which Watson could have a notable social impact while also proving its ability to master a complex body of knowledge. The team chose medicine. They believed Watson could help doctors make diagnoses and, even more important, select treatments. Specifically, they thought Watson could be the perfect tool to chart the complex decision trees that cancer specialists like Kris negotiate every day as they weigh treatment options that might involve radiation, surgery, and any of countless chemotherapy drugs. Watson can ingest more data in a day than any human could in a lifetime. It can read all of the world's medical journals in less time than it takes a physician to drink a cup of coffee. All at once, it can peruse patient histories; keep an eye on the latest drug trials; stay apprised of the potency of new therapies; and hew closely to state-of-the-art guidelines that help doctors choose the best treatments. Watson never goes on vacation. And it never forgets a fact. On the contrary, it keeps learning.

This fall, after six months of teaching their treatment guidelines to Watson, the doctors at Sloan-Kettering will begin testing the IBM machine on real patients. The Ms. Yamato app shows how it will work. After Kris inputs the results of her medical tests, Watson begins deliberating. "It's going through its algorithms," Kris says as we stare at the iPad. "It's seeing where the data sends it today." On the screen, a colorful globe spins. In a few seconds, Watson offers three possible courses of chemotherapy, charted as bars with varying levels of confidence--one choice above 90% and two above 80%. "Watson doesn't give you the answer," Kris says. "It gives you a range of answers." Then it's up to Kris to make the call. He regards the options on the screen and wonders how they might change if Ms. Yamato happened to develop a common symptom: hemoptysis, or coughing up blood.

"Let's try that," he says. He inputs the information and shows me the result approvingly. Watson has dropped one drug from the top chemo regimen. That's just what Kris would have done.

To make sense of all this--that is, to gauge both the value of Watson to a hospital like Sloan-Kettering and its potential to change forever the worlds of medicine and business--you could follow two different paths. You might consider Watson's evolutionary promise. Watson can almost certainly generate huge administrative benefits. Already, one large health insurer--Indiana-based Wellpoint--has begun using a Watson computer in its Virginia data center to speed along the authorization for medical procedures. Usually, authorizations are evaluated by a team of trained nurses and can sometimes take weeks to come through. Watsonizing the process would speed it up--a boon for a doctor like Kris, who now must wait while assistants exchange faxes with insurers before he can get clearance for any expensive tests.

Kris shows me what happens when Watson's treatment plan calls for an MRI. A button pops up on his screen to ask for preauthorization. "I just click that," he says, and it's done instantly.

I ask him what if Watson's request is denied.

Kris seems amused by the question. Watson has already consulted the latest medical literature, and it's been trained by the best cancer doctors in the world. "Who is the authority that is going to trump that?" he asks. Insurers balk at paying for unnecessary procedures; Watson's expert opinion essentially guarantees the necessity.

But the more intriguing path is the second one--a consideration of Watson's potential to do something revolutionary. This is the trail that captivates Kris. Eventually, he thinks, Watson could provide any doctor anywhere with the world's best second opinion. A physician in a community hospital in the Midwest, or at a remote medical center in China, could have instant access to everything that the medical field's best oncologists--people like Kris and his colleagues at Sloan-Kettering--have taught Watson. What is more, Watson will be able to excavate facts beyond the ken of Sloan-Kettering's current lineup of specialists. As Kris says, "We could ask Watson: What is the best treatment for this rare condition based on all of Sloan-Kettering's records?" It could then go through several years of cancer cases looking for the most successful outcomes. In time, it could even look at hospital records from around the world. As Manoj Saxena, the IBM executive now in charge of commercializing Watson, tells me: "It's like being able to take a knowledge worker--cancer specialist, nurse, bond trader, portfolio manager, whatever--and equip that person with the best knowledge, and have it available at their fingertips." As Watson evolves, Saxena believes, these knowledge banks will significantly alter how, and how well, humans make decisions.

Within a few years, for instance, Watson may be reaching well beyond oncology to assist patients suffering from any chronic disease and help general practitioners make diagnoses in their offices. Ultimately, Saxena believes, Watson could play an essential role in the diagnosis and treatment of mental health; in the financial services industry, where Citibank is testing it now; and in education. It could become the world's smartest dietitian.

How Watson Works
IBM's Watson computer begins trials in the health care industry this fall. The initial goal is to help oncologists make better decisions for cancer treatment; eventually, the computer will also aid in the diagnosis and treatment of other chronic diseases.

1. For well over a year, the Watson computers have been "trained" in science and medicine. Technicians feed Watson medical textbooks and journals, patient histories, and treatment guidelines.

2. At Memorial Sloan-Kettering Cancer Center in New York, doctors have begun using a Watson appon a tablet to access the computer through the cloud. The doctor logs in to Watson and begins to input data and ask questions.

3. When the oncologist queries Watson about a course of treatment for a lung or breast cancer patient, the computer--with its ability to understand natural language--notes keywords in the query, such as the particular type of cancer and the genomic variant of the tumor.

4. Watson then springs into action, using its massively parallel processors to review millions of pages of text in seconds. It explores the patient's medical history, medications, and other existing conditions. It then combines this information with recent data from the patient's medical tests and may comb through studies of patient groups at Sloan-Kettering who have had similar types of cancer. It also reviews doctors' and nurses' notes, recent medical research, journal articles, and treatment guidelines.

5. Watson then generates hypotheses for treatment. On the tablet app, these appear as separate options with varying levels of confidence. For instance, Watson might score one treatment option--a combination of chemotherapy drugs--with a 95% confidence level, suggesting it would be the most sensible path. It might also highlight options with lower scores as alternative treatment courses. The doctor then weighs the options and makes the call.

Saxena now commands a team of about 200 people who are working to adapt Watson's skills for various IBM clients. He and I are discussing his progress over lunch one day near IBM's upstate New York headquarters when he leans back and tells me that after creating two successful tech startups, both of which he sold (the second to IBM), his current job is far and away the most meaningful endeavor of his life. Those startups, he confides, were exciting, important. "But this," he says of the Watson rollout, "this is stuff that is going to change the course of history."

Over the past year, IBM executives have come to believe that Watson represents the first machine of the third computer age, a category now referred to within the company as cognitive computing. As Kelly describes it, the first generation of computers were tabulating machines that added up figures. "The second generation," he says, "were the programmable systems--the mainframe, the first IBM 360, PCs, all the computers we have today." Now, Kelly believes, we've arrived at the cognitive moment--a moment of true artificial intelligence. These computers, such as Watson, can recognize important content within language, both written and spoken. They do not ask us to communicate with them in their coded language; they speak ours. And perhaps most important, they can learn, so they improve without constant human instruction.

Siri, on the iPhone, might be considered an elementary example. Watson is industrial strength. "Computers do numerical calculations, they move data around, and they've been doing that forever," David Ferrucci, the IBM researcher who commanded the team that built the first Watson computer, tells me one day at IBM's research labs. "When I think about Watson, it's interpreting the information in human terms. It's saying: What does this mean to me? And that's a big deal." Also significant is how Watson renders an answer. Unlike its responses in Jeopardy!, in the real world it will perform as it did for Kris at Sloan-Kettering--by giving not a single solution but a range of probable solutions, each backed up by Watson's evidence and ranked by its level of confidence. In the lingo of computer science, that makes the machine probabilistic rather than deterministic. One might say this trait gives Watson a humanizing glow of humility and diminishes concerns that it marks a stride toward a computer-led dystopia. Watson, in IBM's marketing schema, is here to help with our questions, rather than solve them. In the case of medicine, it--for Watson is not really a he--is here to support doctors, not replace them.

The Watson of today is not precisely the same machine that won in Jeopardy! IBM has fine-tuned its software and algorithms for medical applications (or, in the case of Citibank, financial services applications). Watson has shrunk, too, from a row of about a dozen server racks that would have filled a small bedroom to an assemblage about the size of a double-door refrigerator. But for all the concentrated power, it doesn't look like anything special. Its sleek black servers are standard IBM Power 750s. You could wander around Watson and regard its blinking lights, as I did on a quiet midsummer afternoon at IBM's research labs, and not think something unusual is happening inside it. But there is. The way Watson solves problems--or, rather, the way it looks for answers, simultaneously sending out thousands of inquiries in all directions and then scoring the evidence it collects--is different from how other computers work. One person at IBM likens Watson's process to (1) gathering hundreds or thousands of possible solutions from a vast data bank, (2) pouring them into a giant funnel, (3) stirring with a dash of algorithms, and (4) letting only the best drip out of the bottom.

At the moment, a half-dozen Watsons are scattered around the country. Some are on the premises of IBM clients, as with the insurer Wellpoint, while others are cloud based, which is how hospitals such as Sloan-Kettering will access Watson. "Effectively, there's no limit to how many Watsons there can be," Bernie Meyerson, IBM's VP of innovation, tells me. Watson is a creation of software, not hardware. "That's the beauty of it," he says.

Watson is different from big servers and mainframes in other ways, too. The best computers of today have the extraordinary processing power needed to create, say, complex supply chains for building a new automobile or planning a satellite launch. These machines are good at manipulating the vast amounts of clearly defined data--numbers and facts--known as structured information. But most of the world's information is more ambiguous and less precise and lies beyond their reckoning. "We now have this proliferation of what we call Big Data," Saxena, Watson's business manager, tells me, referring to the flood of information created by our computers, our electronic sensors, and ourselves. "Ninety percent of the world's information was created in the last two years," he says. "But 80% of that 90% is unstructured or semistructured information, like doctor's notes or product reviews on Amazon." This near infinitude also includes tweets, blogs, emails--all the noise and scribble of modern life. So any company that aspired to manage the data of all the world's businesses would today be able to analyze only a small part of it. Watson, though, is a genius at reading unstructured information. And it's precisely this facility that explains why IBM sees such a rich business opportunity here.

It likewise explains why medicine is a logical first choice. While some health information is indeed structured--think of blood-pressure readings or cholesterol counts--the vast majority is unstructured. This cache includes textbooks, medical journals, patient records, and nurse and doctor evaluations. In fact, medicine embodies so much unstructured information that its proliferation has, by the account of many medical professionals, far outstripped the ability of doctors to keep up. Neither better training nor continuing education could ever wholly remedy this problem. When I meet with Herbert Chase, a professor of clinical medicine at Columbia University who consulted with IBM during the early stages of the Watson project, he says it is "not humanly possible" for a busy doctor to keep abreast of the current literature.

One result of information overload is a high rate of misdiagnosis and consequently incorrect treatment. By some estimates, Saxena tells me, 20% of initial diagnoses of cancer are eventually altered. "Imagine the implications of cancer care if there is a one in five chance that for the next six months whatever therapy they're giving you is wrong," he says.
Deciding on a course of treatment is even tougher than making a diagnosis. "It's still possible for a doctor to know the ways that people get sick," says Chase, who is also a kidney specialist. "But what is unmanageable, and what has been for decades, is knowing what the best option is today." Some applications now available to doctors are meant to alleviate this problem; one popular web-based tool is named Isabel. But Watson, in Chase's view, reaches a different level of sophistication. "I'll give you an example of a test we thought up for Watson," he tells me one day in his Manhattan office. "A patient was pregnant, had Lyme disease, and was also allergic to penicillin. And Watson came up with a drug. The first thing I thought was, Watson made a mistake. That drug can't be given to someone allergic to penicillin." But Chase was wrong, not Watson. "My knowledge was about five years old," he says. "And in the past couple of years, all the muckety-mucks had reviewed all the studies and had concluded yes, you can give that drug to someone who's allergic to penicillin."
To Chase, this proves a point: If you're a patient, you don't want to believe your doctor doesn't know everything. But he or she doesn't, and can't. At its best, the dispensation of treatment is inefficient today. "At its worst," Chase says, "it's subpar, incorrect, wrong therapy," and doesn't reach the standard of care to which his profession aspires. "As you can imagine," he adds, "this is not something we like talking about."

Last year, IBM turned 100 years old, which sets it apart from West Coast counterparts like Amazon, Apple, Google, HP, and Microsoft--all younger and ostensibly the tech world's leading innovators. To delve into IBM's recent research, though, is to wonder if our perception of technological leadership sometimes suffers from the distortions of branding and familiarity. We use iPhones and search engines and laser printers every day. But IBM's technologies are lodged deeper within the infrastructure of daily life; you're tapping into them whenever you send an email, for instance, or log on to a website. IBM has been granted more patents than any other company in the world for 19 years in a row. Yet since getting out of the laptop business in 2004, it has not produced a single product that it sells directly to the consumer.

If you're a patient, you don't want to believe your doctor doesn't know everything. But he or she doesn't, and can't.

To understand how Watson figures into the company's culture of ideas, or to see how it represents the kind of large-scale innovation that arguably lies beyond the capabilities of any startup, it helps to understand what the company actually does these days. IBM has operations in 172 countries and an organizational chart that resembles a vast Soviet bureaucracy. It employs about 433,000 men and women. Though IBM still sells hardware--big mainframe computers, silicon chips, and supercomputers--mainly it makes money selling software and consulting services to businesses and governments. The company's strategy has been validated of late by its performance: IBM's stock price has been on an upward trek for the past five years, and its winning streak has attracted the likes of Warren Buffett, who last year decided the company merited an investment of $10.7 billion. Meanwhile, as one of the few global titans to invest staggering sums on R&D ($6 billion to $7 billion a year), IBM maintains one of the world's last great industrial laboratories. At its main research center in Yorktown Heights, New York, a jet-age dream of glass curtain walls and rusticated stone designed by the Finnish-American architect Eero Saarinen, IBM employs the bulk of what is likely the world's largest mathematics department, with 300 members. If you're looking for a new PC design, you're out of luck here. But if you're shopping around for a new or better algorithm, IBM can build you one.

Not everyone is impressed by the direction of IBM's management. A relentless focus on earnings and cost cutting has led to a significant offshoring of domestic jobs, and a vocal corps of disillusioned or laid-off IBMers regularly take to the web to lament that the company's best days are behind it. IBM has also had its share of technological stumbles, apparently bungling several high-profile government contracts in recent years (in Texas and Indiana, for example) that left the company embroiled in disagreements with unhappy clients. And though these flare-ups may be uncommon, the company otherwise rarely quickens the pulse, with a long-standing reputation for being slow, steady, reliable, and maybe a little dull. IBM doesn't have big growth spikes or ballyhooed product launches; rather, it has plodding, long-term client contracts built around its ability to help optimize, say, a company's global IT services or a public utility's electrical grid. The corporation moves along like a supertanker. "IBM's annual revenue base is huge--$100 billion," says Toni Sacconaghi, a technology analyst for Sanford C. Bernstein. "So to move the needle is tough. It's hard to find big new products."

The managers and engineers keep looking anyway. One way IBM tries to infuse the troops with a sense of mission is through its periodic attempts to create for itself a Grand Challenge, such as the construction of Deep Blue, a chess-playing computer, or, more recently, Watson. The Grand Challenges are focused and expensive efforts--IBM will not verify Watson's cost, but estimates put the sum between $100 million and $1 billion--to push the company beyond the competition.

Watson's origins can arguably be traced back some years to a more modest annual initiative IBM calls the Global Technology Outlook, or GTO. Anyone at IBM can contribute to the outlook, and most of the results are eventually made public. The GTO tries to identify future business opportunities by putting a spotlight on various technology trends. A while ago, the IBM outlook pointed to analytics as a potentially huge field. Not long after, then-CEO (and current chairman) Sam Palmisano green-lighted IBM's acquisition of about $16 billion in smaller companies that had computer technologies to do this kind of work--essentially, to comb through vast stores of data, both structured and unstructured, and help extract nuggets from the global corporate babel.

Like Big Data or cloud computing, analytics is one of those contemporary catchphrases that everyone talks about but no one pauses to define. Bernie Meyerson, IBM's VP of innovation, argues that the great promise of analytics is not just to spot trends or glean information for boosting sales but to use computers and software to change the future. "Analytics is the capability to see what no human can," he says. Recently, at a public event, Meyerson was asked if IBM missed out by not building a tablet to compete with the iPad. He responded that as part of its Smarter Cities Initiative, IBM had just spent several years gathering all of the data on car transportation in Singapore; it then fed the data into a model it had built to predict the time and location of traffic jams. "We know from history what happens in Singapore if you slow the lights down in one direction by three seconds, and how to tweak the model so the jam never happens," he told his questioner. "And so there will be a traffic jam that never occurs because we can predict what happens 20 minutes from now, because we can take enough Big Data and crunch it, and do analytics on it. So we're predicting the future, and changing it. And you're asking me if I'm worried about a tablet?"

Watson, too, fits into Meyerson's conception of analytics, though it aims to change not the future of a traffic jam but of illness and investing. And by all indications, that tantalizing promise is not lost on the business community. "I have my shoulder against the door," Saxena tells me. He means he is turning clients away--something I heard from several other sources, too--until IBM executives feel confident Watson has proved its credibility at places like Wellpoint and Sloan-Kettering. Saxena seems certain that Watson will be a multibillion-dollar business, though he will only go so far as to say that by 2015, IBM will have annual revenues of about $16 billion from its analytics portfolio, of which Watson will be a part. When I put the question of Watson's potential to John Kelly, IBM's chief of research, he says: "It's like asking, at the very beginning, How big will the PC industry be?"

Kelly notes that the business model for Watson is still to be determined. He isn't sure whether selling Watson as a computer or marketing it as a service will make the most sense. But he feels he has time to decide. None of IBM's competitors, more than a year after the Jeopardy! victory, has announced a Q&A technology like Watson. "I think we have a huge lead," Kelly tells me. "When people realize this is not a one-off game machine but a new era of computing, then you'll see other companies tripling down to catch up."

I asked a number of people, both within IBM and outside of it, whether other organizations could have built this machine first. The consensus was probably not. The reasons did not precisely connect to IBM's technological capabilities--Google and Microsoft have plenty of computer prodigies in their ranks too. Rather, it was the combination of assets at IBM that made the difference. The company had its vast corporate lab, huge sums it was ready to invest, a profound expertise in hardware as well as software, and a collaborative culture that brought in lots of help from academia. And crucially, it had its business clients. In this respect, being a company that doesn't cater to consumers has advantages. Watson is only as bright as its teachers. Without the staff at Sloan-Kettering, where doctors like Mark Kris teach it oncology, Watson would not be nearly so smart. In fact, it might be kinda dumb. Or it might get all sorts of things wrong, like Siri does, except you'll be looking not for a pizza parlor but for a tumor.

From the start, the team that originally built Watson under David Ferrucci has worked out of a big room on the second floor of IBM's Hawthorne Labs in Westchester County, New York. Hawthorne is a large glass cube of a building situated about 30 miles north of New York City. Inside the Watson work space are five fake wood-grained tables, each home to a group of computer engineers who sit around and alternately immerse themselves in their screens or break to discuss coding with a neighbor. The mood here is sober. The staffers bring water bottles, not junk food. These aren't the unlined faces you'll see at a startup. Indeed, Ferrucci, who sits off to the side, is a suburban dad who looks like he'd be just as comfortable standing in front of a grill with a basting brush as he is overseeing his team. The walls here are covered with huge whiteboards crammed with the hieroglyphics of computer science. Overhead lights cast the room in gloomy fluorescence. The place has the neglected feel of a finished basement in a 1970s-era subdivision.

In early fall, the Watson team, now about 45 strong, began moving its work to a gleaming new space in IBM's main Yorktown Heights research laboratory--a promotion that reflects their importance as they support Saxena's much larger business development group while simultaneously working on the next iteration of Watson, known as Watson 2.0. One of the team's goals is to make Watson adaptable enough so that it doesn't require several dozen people spending a year to get it ready for every new application, such as medicine or financial services. But a more immediate project is to help Watson through the U.S. Medical Licensing Examination, the complex test all med-school graduates must take before practicing. If it passes, says Ferrucci, "that doesn't mean I can have a computer be a doctor." But IBM would gain what he calls "a crisp metric" that proves Watson has a real proficiency in medicine. The credential would no doubt help Watson's standing with health insurers, doctors, and patients, too. Passing the licensing exam is a difficult task--far harder than winning at Jeopardy!--but in early September, Ferrucci seemed pleased by the results. The computer is doing "interestingly well," he said. He sounded confident that Dr. Watson will ace the test by year's end.

Harder to intuit is how soon afterward Watson will infiltrate society. When I ask Jaime Carbonell, a computer science professor at Carnegie Mellon, he says he has no doubt the impact of Watson will be significant. "But I don't think there will be one moment of, 'Now we have it and yesterday we didn't,'" Carbonell remarks. "It will take time to permeate. Like cell phones, which were big, clumsy things you could barely carry at first." Was there a year, or month, or day, he asks, when cell phones began to change the world? "I can't think of when that was," he says. "But now we can't do without them."

Such is the course of technology: Electronic tools initially available only to the elite grow ever faster, smaller, cheaper. Kelly tells me he believes that eventually Watson will shrink to the size of a handheld device. Randy Katz, a computer science professor at UC Berkeley, sees a more approachable Watson, too. "Can the person in the street ask Watson a question now? No, he can't," says Katz. "But in five or 10 years, will there be systems like that--like Siri, but much better? I think the answer is yes."

In many of my conversations at IBM, the talk often drifts to applications of Watson. All sorts of intriguing scenarios are presented to me--for instance, that Watson will soon analyze not just words but images, such as MRIs and EKGs. Or it will diagnose a spider bite on a child's arm in a crop field in Africa, transmitted via smartphone by his worried father to a U.S. hospital. One afternoon, Saxena suggests this one: When you think you're coming down with the flu, Watson will be able to discern, before you even arrive at the doctor's office, that it might be a ragweed allergy, based on your medical record (you've had the same symptoms twice before at this time of year); your symptoms (gleaned from the insurance claim and diagnostic information in journals); and recent news (it just read an article in the Austin-American Statesman on a ragweed outbreak near your hometown).

It all sounds amazing. It's also speculative. Watson has not yet saved a life or a dollar of medical costs, or added anything, really, to IBM's bottom line. It has not yet faced its resistors--doctors who may find the technology objectionable and slow its adoption. It has not yet, as Saxena believes it will, changed the course of history. It has only won a television game show.

Still, Saxena predicts the computer will begin to scale up dramatically late next year. "By then," he says, "we will have built the technology, demonstrated it, built the tooling and methods around it. We will have the recipe book, and then we'll just push it out." But he will only have reached the end of Watson's beginning.

A version of this article appears in the November 2012 issue of Fast Company.

Tim O'Reilly: Open Health Data in Practice: Increase Your Access to Lab Results Voice Your Support for a Proposed Federal Rule that Expands Patients' Access to Test Results

Tim O'Reilly, O'Reilly Radar, October 16, 2012

I’m convinced that there’s a wave of innovation coming in healthcare, driven by new kinds of data, new ways of extracting meaning from that data, and new business models that data can enable.  That’s one of the reasons why we launched our StrataRx Conference, which focuses on the importance of data science to the future of health care.

Unfortunately, much of the data that will enable an entrepreneurial explosion is still locked up — in paper records, in proprietary data formats, and by well-intentioned but conflicting privacy regulations.

We’re making progress towards open data in healthcare, but there are still so many obstacles!  Ann Waldo recently introduced me to one of these.

A 2009 law modernized patient access rights by allowing individuals to get copies of their medical records in electronic format. Unfortunately, however, these patients’ access rights surprisingly do not include lab test results – one of the types of medical records that people are most likely to find urgent and useful. Due to the interaction of HIPAA (the Federal medical privacy law), CLIA (a Federal laboratory regulatory law), and state laws, patients can only get direct access to their their test results from labs in a handful of states.
A recent New York Times story highlighted just how much pain and suffering can be caused by this inability to get access to your own lab results.

In 2011, the Department of Health and Human Services put forward a proposed Rule that would give patients the right to get their test results directly from laboratories. This Rule is still waiting to be finalized. In hopes of breaking the logjam, O’Reilly Media and a variety of other players have written a consensus letter that voices our whole-hearted support for that proposed Rule and encourages the Federal government to finalize it promptly.
We’d love to invite you to join us in signing this letter.

Patients’ rights should include direct access to their lab results, just like all their other medical records!

Should High Schools Teach Big Data?

Given the anticipated shortage of data scientists, some high school educators have jumped in to expose students to big data concepts.

Ellis Booker, Information Week, October 16, 2012.

Changing when advanced database technology is taught has real-world implications, given the realities of today's job market. Both data analytics and big data skills are in high demand in private industry and government.

But there is a looming shortage of workers with these abilities. McKinsey & Co. sounded this alarm back in 2011 with its seminal report that predicted the U.S. would face ashortage of 140,000 to 190,000 workers with the skills to manage and analyze big data.

The popular technology job board Dice.com has seen a spike in listings for "data scientist," up from just a handful a year ago to more than 35 at the start of October. While still an imprecise job designation, "data scientists" command high salaries compared to other IT job titles. (Separately, the unemployment rate for technology professionals dropped in the third quarter to 3.3%, as compared to 4.2% in the same quarter a year ago, according to the Bureau of Labor Statistics.)

These listings--many of which request a PhD in fields like mathematics, economics, or statistics--today cluster in financial services, retail, and e-commerce. Job listings using the phrase "big data" have increased from around 200 in January to nearly 800 in October.

"But increasingly, every industry is dealing with big data questions," said Alice Hill, managing director of Dice.com and president of Dice Labs.

Preparing the U.S. for future high-tech jobs, specifically ones oriented around data, was also a focus of TechAmerica Foundation's Big Data Commission.

Given the anticipated shortage of data scientists, should students start learning the precepts of big data in high school?Analytics and data science are central to making "business, the global economy, and our society work better," Steve Mills, senior VP and group executive at IBM, and co-chair of the Big Data Commission, said in a statement. "That's why it's critical that our country prepares a new generation of experts who know how to corral today's data deluge for world-changing insights."

The commission's new report, "Demystifying Big Data: A Practical Guide to Transforming the Business of Government," included the following recommendations for skills development: Strengthen and expand public-private partnerships to invest in skills-building initiatives for the federal workforce in the area of big data. These should include formal career tracks for IT managers; an IT leadership academy to provide big data and related training and certification; data-intensive degree programs; and scholarships to prepare a new generation of data scientists.

Big Data In High School?

Among those trying to push big data classes down to the high school level is Alex Philp, PhD. Philp is founder and CTO of TerraEchos, a developer of advanced intelligence and surveillance security systems. Philp has been working with the high schools in Missoula, Mont., and has even created a scholarship program at one--Sentinel High School--to introduce these topics to computer science students.

"[We] cooked up the idea of promoting a merit-based, micro-challenge grant at Sentinel," Philp explains. Currently, four student teams have been awarded grants. "Ultimately, I hope these high school students feed into opportunities at the University of Montana and then ultimately into the most exciting businesses and markets involving big data," Philp said. "This also relates to aspects of the overall economic competitiveness of our country and a renewed commitment to science, technology, engineering, arts, and mathematics (STEAM) competency in our country."

Meanwhile, at the university level, Philp has been working with Eric Tangedahl, IT director for the school of business administration at The University of Montana, to create a multidisciplinary course, now in its first year.

"The course draws students from computer science, management information systems, and math," Tangedahl said. "We felt that to tackle these problems, students have to work together in teams, teams with different skill sets," he said.

The three-hour class, now with 18 students, is half lecture about big data and half lab--specifically around IBM Infostream, a programming language that takes advantage of IBM's DB2 and WebSphere platforms.

Another high school on this path is the science and engineering magnet (SEM) school at Yvonne A. Ewell Townview Center in Dallas. SEM, ranked by Newsweek this year as one of the best high schools in the U.S., recently participated in IBM's annual Master the Mainframe Contest for high school students in the U.S. and Canada.

Marilyn Cadenhead, who teaches Advanced Placement computer science at SEM, has many students participating in the contest.

Cadenhead says her students use technology very effectively, and already have a computer-mediated learning style. "This is a digital age, very different from when I was in school," she wrote in an email. "I learned to type on a manual typewriter. If we as teachers do not engage [students] with the latest technology and teaching styles, we will be 'boring' and the students will not be motivated to learn."

"I want my students to know what is going on in the real world," Cadenhead concludes, explaining her enthusiasm for the IBM mainframe contest, as well the annual IBM Innovation summer camp that some SEM students attended this year.

IBM's Innovation summer camp Facebook page describes the program this way: "The students will gain hands-on experience with visualization, mobile application development, Linux, and DB2 coupled with demos of research underway at UTD in mind control, gaming, and robotics."

Other observers, however, point out that big data analysis is powerful precisely because it is about more than raw technology. The highly valued professionals in this space are those who ask the right questions, who can see business-relevant answers inside the data. That kind of maturity and domain expertise is beyond the capacity of high school students, they say.

The University of Montana's Tangedahl concurs. "I wouldn't throw high school student in and expect them to come up with a [big data] program for the Defense department," he said. But, he adds, "you've absolutely got to put the building blocks in place." He recommends adding data statistics and programming classes in high school to better prepare students for college-level classes, like his, that push teams of students to answer "real-world problems."

Like Tangedahl, Dice.com's Hill isn't sold on the idea of pushing big data instruction to high school students. She thinks students should learn relevant technologies, such as data interpretation, data extraction, and data modeling. "It's never too soon to learn those skills," she said.

But TerraEchos' Philp disagrees, arguing it is essential that educators encourage excellence in students at all ages, and not set limits.

"I've spent many years of my life working with thousands of students at various ages, attempting to raise the bar," he said. "High school kids are proposing [projects] that are as good as I'm seeing in college." With motivation, inspiration, and passion, he said, "my experience is, students rise to the occasion."

McKinsey Anthology: Government Designed for New Times

To explore the approaches that governments around the world are taking to common problems, this anthology convenes political leaders and civil servants, economists and policy experts, generalists and specialists.

McKinsey Anthology, 2012
               
Contents
        Transforming government
·       Tony Blair—Leading transformation in the 21st century
·       François-Daniel Migeon—Interview: Transforming government in France
·       Frank-Jürgen Weise—Behind the German jobs miracle
·       Tim Brown—Quick take: Designing a tech-enabled government
·       Michael Fullan—Transforming schools an entire system at a time
·       Todd Park—Interview: Unleashing government's 'innovation mojo'
·       Diana Farrell—Government designed for new times
        Innovating government services
·       James Fishkin—What the people think when they're really thinking
·       Nandan Nilekani—Interview: For every citizen, an identity
·       Matthew Taylor—Citizens: The untapped resource
·       Susan Zielinski—The new mobility
·       Peter Shergold—A social contract for government
·       Wim Elfrink—The smart-city solution
·       Karan Bhatia—Quick take: Building the world's infrastructure
·       Salman Khan—Teaching for the new millennium
·       Xie Chengxiang—Interview: Home for the urban poor
·       Elena Berkowitz and Blaise Warren—Quick take: How Estonia became E-stonia    

        Building new competencies
·       Douglas Holtz-Eakin—Fiscal management fix: Simple math—and a very big stick
·       Coen Teulings—Why politicans prefer austerity to long-term fiscal reform
·       Lu Mai—The urbanization solution
·       Göran Persson—How to tame a budget crisis
·       Peter Ho—Coping with complexity
·       Mohamed Ibrahim—Better data, better policy making    
        Understanding government in new times
·       Daron Acemoglu—The servant state
·       Parag Khanna—The rise of hybrid governance
·       Neil deGrasse Tyson—Why exploration matters—and why the government should pay  
        for it
·       Ray O. Johnson—Quick take: The research imperative
·       Hernando de Soto—Interview: Building a nation of owners
·       Nicolas Berggruen and Nathan Gardels—A middle way for governance       

Dark Social: We Have the Whole History of the Web Wrong

Alexis Madrigal, The Atlantic, October 2012

Here's a pocket history of the web, according to many people. In the early days, the web was just pages of information linked to each other. Then along came web crawlers that helped you find what you wanted among all that information. Some time around 2003 or maybe 2004, the social web really kicked into gear, and thereafter the web's users began to connect with each other more and more often. Hence Web 2.0, Wikipedia, MySpace, Facebook, Twitter, etc. I'm not strawmanning here. This is the dominant history of the web as seen, for example, in this Wikipedia entry on the 'Social Web.' 

tl;dr version

1. The sharing you see on sites like Facebook and Twitter is the tip of the 'social' iceberg. We are impressed by its scale because it's easy to measure.
2. But most sharing is done via dark social means like email and IM that are difficult to measure.
3. According to new data on many media sites, 69% of social referrals came from dark social. 20% came from Facebook.
4. Facebook and Twitter do shift the paradigm from private sharing to public publishing. They structure, archive, and monetize your publications.

But it's never felt quite right to me. For one, I spent most of the 90s as a teenager in rural Washington and my web was highly, highly social. We had instant messenger and chat rooms and ICQ and USENET forums and email. My whole Internet life involved sharing links with local and Internet friends. How was I supposed to believe that somehow Friendster and Facebook created a social web out of what was previously a lonely journey in cyberspace when I knew that this has not been my experience? True, my web social life used tools that ran parallel to, not on, the web, but it existed nonetheless.

To be honest, this was a very difficult thing to measure. One dirty secret of web analytics is that the information we get is limited. If you want to see how someone came to your site, it's usually pretty easy. When you follow a link from Facebook to The Atlantic, a little piece of metadata hitches a ride that tells our servers, "Yo, I'm here from Facebook.com." We can then aggregate those numbers and say, "Whoa, a million people came here from Facebook last month," or whatever. 

There are circumstances, however, when there is no referrer data. You show up at our doorstep and we have no idea how you got here. The main situations in which this happens are email programs, instant messages, some mobile applications*, and whenever someone is moving from a secure site ("https://mail.google.com/blahblahblah") to a non-secure site (http://www.theatlantic.com). 

This means that this vast trove of social traffic is essentially invisible to most analytics programs. I call it DARK SOCIAL. It shows up variously in programs as "direct" or "typed/bookmarked" traffic, which implies to many site owners that you actually have a bookmark or typed in www.theatlantic.com into your browser. But that's not actually what's happening a lot of the time. Most of the time, someone Gchatted someone a link, or it came in on a big email distribution list, or your dad sent it to you. 

Nonetheless, the idea that "social networks" and "social media" sites created a social web is pervasive. Everyone behaves as if the traffic your stories receive from the social networks (Facebook, Reddit, Twitter, StumbleUpon) is the same as all of your social traffic. I began to wonder if I was wrong. Or at least that what I had experienced was a niche phenomenon and most people's web time was not filled with Gchatted and emailed links. I began to think that perhaps Facebook and Twitter has dramatically expanded the volume of -- at the very least -- linksharing that takes place. 

Everyone else had data to back them up. I had my experience as a teenage nerd in the 1990s. I was not about to shake social media marketing firms with my tales of ICQ friends and the analogy of dark social to dark energy. ("You can't see it, dude, but it's what keeps the universe expanding. No dark social, no Internet universe, man! Just a big crunch.")

And then one day, we had a meeting with the real-time web analytics firm, Chartbeat. Like many media nerds, I love Chartbeat. It lets you know exactly what's happening with your stories, most especially where your readers are coming from. Recently, they made an accounting change that they showed to us. They took visitors who showed up without referrer data and split them into two categories. The first was people who were going to a homepage (theatlantic.com) or a subject landing page (theatlantic.com/politics). The second were people going to any other page, that is to say, all of our articles. These people, they figured, were following some sort of link because no one actually types "http://www.theatlantic.com/technology/archive/2012/10/atlast-the-gargantuan-telescope-designed-to-find-life-on-other-planets/263409/." They started counting these people as what they call direct social. 

The second I saw this measure, my heart actually leapt (yes, I am that much of a data nerd). This was it! They'd found a way to quantify dark social, even if they'd given it a lamer name! 
On the first day I saw it, this is how big of an impact dark social was having on The Atlantic. 

Just look at that graph. On the one hand, you have all the social networks that you know. They're about 43.5 percent of our social traffic. On the other, you have this previously unmeasured darknet that's delivering 56.5 percent of people to individual stories. This is not a niche phenomenon! It's more than 2.5x Facebook's impact on the site. 

Day after day, this continues to be true, though the individual numbers vary a lot, say, during a Reddit spike or if one of our stories gets sent out on a very big email list or what have you. Day after day, though, dark social is nearly always our top referral source. 

Perhaps, though, it was only The Atlantic for whatever reason. We do really well in the social world, so maybe we were outliers. So, I went back to Chartbeat and asked them to run aggregate numbers across their media sites. 

Get this. Dark social is even more important across this broader set of sites. Almost 69 percent of social referrals were dark! Facebook came in second at 20 percent. Twitter was down at 6 percent. 
All in all, direct/dark social was 17.5 percent of total referrals; only search at 21.5 percent drove more visitors to this basket of sites. (FWIW, at The Atlantic, social referrers far outstrip search. I'd guess the same is true at all the more magaziney sites.)

There are a couple of really interesting ramifications of this data. First, on the operational side, if you think optimizing your Facebook page and Tweets is "optimizing for social," you're only halfway (or maybe 30 percent) correct. The only real way to optimize for social spread is in the nature of the content itself. There's no way to game email or people's instant messages. There's no power users you can contact. There's no algorithms to understand. This is pure social, uncut.

Second, the social sites that arrived in the 2000s did not create the social web, but they did structure it. This is really, really significant. In large part, they made sharing on the Internet an act of publishing (!), with all the attendant changes that come with that switch. Publishing social interactions makes them more visible, searchable, and adds a lot of metadata to your simple link or photo post. There are some great things about this, but social networks also give a novel, permanent identity to your online persona. Your taste can be monetized, by you or (much more likely) the service itself. 

Third, I think there are some philosophical changes that we should consider in light of this new data. While it's true that sharing came to the web's technical infrastructure in the 2000s, the behaviors that we're now all familiar with on the large social networks was present long before they existed, and persists despite Facebook's eight years on the web. The history of the web, as we generally conceive it, needs to consider technologies that were outside the technical envelope of "webness."

People layered communication technologies easily and built functioning social networks with most of the capabilities of the web 2.0 sites in semi-private and without the structure of the current sites. 

If what I'm saying is true, then the tradeoffs we make on social networks is not the one that we're told we're making. We're not giving our personal data in exchange for the ability to share links with friends. Massive numbers of people -- a larger set than exists on any social network -- already do that outside the social networks. Rather, we're exchanging our personal data in exchange for the ability to publish and archive a record of our sharing. That may be a transaction you want to make, but it might not be the one you've been told you made. 

* Chartbeat datawiz Josh Schwartz said it was unlikely that the mobile referral data was throwing off our numbers here. "Only about four percent of total traffic is on mobile at all, so, at least as a percentage of total referrals, app referrals must be a tiny percentage," Schwartz wrote to me in an email. "To put some more context there, only 0.3 percent of total traffic has the Facebook mobile site as a referrer and less than 0.1 percent has the Facebook mobile app."

Inside the MIT Media Lab: Big Data, Privacy, Modern Cities and More

Some of the MIT Media Lab’s most groundbreaking research has the potential to transform business as we know it.

Deloitte, Wall Street Journal, October 12, 2012

For 27 years, the MIT Media Lab has sought to balance the visionary and the practical. One of its early innovations, the Aspen Movie Map, allowed users to take a virtual tour of Aspen, Colo., paving the way for Google Earth. It also exemplifies the Media Lab’s goal of exploring and inventing technologies that enhance the way people live, work, learn, and play.

Current research projects focus on endowing computers with human-like intuition—to spark collaboration and help people make social and professional connections in the physical world. Faculty and students are also working on ways to make human-computer interfaces more natural, and they’re harnessing big data to improve public health, better understand human behavior, and discover new business opportunities.
In June, a group of Deloitte professionals spent a day at the MIT Media Lab. Research directors briefed them on seven projects—from among the more than 350 currently underway—based on their relevance to Deloitte and its clients. Deloitte professionals shared their perspectives on four of the research initiatives that most impressed them.

Using Big Data to Predict Human Behavior
Sandy Pentland, MIT professor of media arts and sciences, discussed research that his “Human Dynamics” group conducted in human behavior and predictive modeling. Pentland and his team asked research volunteers to use smartphones equipped with sensors as they went about their daily activities. The researchers collected volumes of anonymous data on subjects’ whereabouts, phone calls, emails, text messages, and social networking.

Analysis of the smartphone data revealed patterns of activity that indicated when an individual’s behavior was about to change. For example, researchers began to be able to predict when subjects were coming down with the flu because they went out less frequently and made fewer phone calls to friends. Researchers were also able to predict with 45 percent accuracy what apps subjects would likely download onto their phones based on the apps their friends downloaded.

The takeaway for businesses from this research: If companies want to predict or influence their customers’ behavior, they should look to their customers’ social circles.

Duleesha Kulasooriya, head of research for Deloitte’s Center for the Edge, says using mobile data as a leading—rather than lagging—indicator of consumer behavior is groundbreaking.

“It will allow businesses to target their messaging to people based on their behavior, and much more effectively than other channels, like TV, radio, or newspapers,” he says.

Glen Dong, chief of staff for the U.S. Technology, Media & Entertainment, and Telecommunications Industry practice, was impressed by the potential of predictive modeling to prevent the spread of communicable diseases.

“Predictive models like those Pentland and his research staff developed could have stanched the H1N1 epidemic within days of its outbreak,” he says.

Addressing Privacy Questions
A complementary area of Pentland’s research aims to address escalating concerns over privacy in the age of big data. He proposes treating personal data as an asset that’s owned and controlled by the individual. In other words, people would have control of their personal data in much the same way they control their financial assets, and would be compensated in some way, whether with cash or a discount, for sharing it.
“Treating personal data as an asset seems obvious in retrospect, but it is a tremendously important concept,” says Scott Buchholz, director of Deloitte Consulting LLP’s Federal practice. “If I control the use of my personal data, and we (individuals and businesses) can standardize agreements and transactions involving its use, then personal data can be monetized in ways that could transform business.”

For example, Buchholz can envision companies with large data sets—for example, Facebook, Google, insurance providers, and retailers—mining their data while removing personally identifiable information. When a company discovers a strong correlation between some specific data and results (e.g., a particular set of genes or behaviors tends to lead to certain medical conditions, or living in a particular neighborhood generates higher sales), it could arrange to exchange that data with customers for some form of consideration.

“As a customer, I might be willing to make some personal data available to a third party if it gets me a discount or perk, and if I have a reasonable assurance that my data will be used only for a specified purpose,” he says. “A system like this could enable companies to benefit from their analysis and insights while removing the restrictions associated with having to manage large sets of personal information—an activity that is increasingly coming under government scrutiny in the United States and elsewhere.”

The City of the Future
Kent Larson’s “Changing Places” research group is prototyping new cities in China, India, and the Middle East. They are designing city layouts that encourage walking, developing energy-efficient fleets of folding, self-driving electric cars, and conceptualizing sustainable, modular housing complexes where dwellers can grow their own produce and customize their living spaces. These city prototypes combine the walkability of Paris with the futuristic utopianism of Gene Roddenberry’s Star Trek: The Next Generation.

Elina Ianchulev, a senior sector specialist in Deloitte’s Technology industry practice, envisions the plans for cities being applied to workplaces and campuses.

“Large corporate and college campuses could benefit from the modular buildings being designed at the Media Lab,” she says. “If companies could reconfigure work spaces at the push of a button, they could more easily adapt to shifting workforces and use their space more effectively.”

Dong was struck by the impact this new approach to urban planning could have on industries as diverse as auto manufacturing, energy, and construction.

“Car makers may have to come up with entirely new business models if they want a market in cities being designed for walking,” he says. “Retailers may need to come up with new real estate strategies and store formats since the strip mall and big box store concepts are unlikely to exist in these new cities.”

“Digital Intuition” Sparks Communication and Collaboration
Visitors to the MIT Media Lab are given badges equipped with RFID tags that identify them each time they come near one of the many RFID readers positioned throughout the Lab. Catherine Havasi and two fellow researchers have developed a mobile social discovery application called CharmMe that works in tandem with the RFID technology.

The purpose of CharmMe is to help people connect with others who share complementary personal, professional, or research interests. When a visitor to the Media Lab approaches one of the RFID readers, the reader identifies on a display screen other people in the area with similar interests.

Buchholz sees CharmMe as “a useful way of connecting people in large organizations or at conferences who wouldn’t otherwise run into one another.”

CharmMe is part of Havasi’s larger body of work dedicated to developing what she calls digital intuition. Her aim is to teach computers to understand and mediate interactions among people. She notes how humans use their intuitive knowledge of the world and experiences they’ve had in the past to react intelligently to the world around them. If we were to give machines these capabilities, she says, they could help us make better-informed decisions, conquer mountains of data, and expand the reach of our creativity and intelligence.
What business does not wish to achieve those same goals?