Big Data Helps Kaiser Close Healthcare Gaps

 Analytics from massive clinical data repository are central to closing gaps in care, HIMSS attendees told.

Neil Versel,  Information Week, March 07, 2013

One benefit of Kaiser Permanente spending an estimated $6 billion for an integrated electronic health records (EHR) system to serve 9 million people across eight regions from coast to coast is it that has amassed a vast repository of clinical data. That storehouse also contains information from a patient portal, ancillary systems, smart medical devices and even home-based patient monitoring systems.

All those terabytes of electronic data now are helping to fuel a massive analytics operation, part of an overall organizational goal of improving care and reining in costs. "It's all about the data and information, not the electronic health record," Carol Cain, senior director of clinical information services for the Kaiser Permanente Care Management Institute, said this week at the Healthcare Information and Management Systems Society (HIMSS) annual conference in New Orleans.

Kaiser has embraced a concept of "complete care," which one Southern California Permanente Medical Group described as "giving my patients everything they need, whether they know it or not," according to Cain's presentation.

"We need to incorporate so much more data that is available," Cain said. Data needs to be "synthesized in a meaningful way" and delivered to primary care physicians at the point of care to help suggest appropriate interventions.

Cain said Kaiser views big data as being characterized by "volume, variety and velocity." The term "refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze," she said.

"Our ability to monitor our members' health is greater than our members' ability to know what needs to be monitored," Cain explained.

[ Are your patients taking leadership for their own health? See 7 Portals Powering Patient Engagement. ]

Kaiser Permanente has developed several modules of population management, all designed to identify and close gaps in care. If a patient shows up with knee pain, for example, management tools suggest doctors ask about a cancer screening, in an effort to make office visits "proactive" and organize care around the concept of the patient-centered medical home, Cain said.

The analytics also has to be done in a way that won't make patients feel like Big Brother is watching over them, Cain said. Instead, Kaiser wants people to think that the integrated delivery system is helping to prevent illness and find health problems early. If patients allow Kaiser to access information linked to their supermarket loyalty cards, the organization will not send warnings every time they purchase a candy bar, Cain said.

What Kaiser can do is rely on its platform to combine patient-specific knowledge, such as whether an individual has filled a prescription. This can help with medication adherence, according to Cain. Analytics are helpful for developing care plans before patients are discharged from hospitals, too.

Kaiser also can advise patients to telephone or schedule e-visits if a primary care physician determines a problem is not worth an in-person appointment. "That is something that is often appreciated by our members," Cain noted.

Cain said that patient needs are not always clinical, either. During a 12-hour hackathon in the analytics department, Kaiser IT professionals were able to correlate access to parks with rates of obesity in Oakland, Calif. "In some of our communities, we are investing in building parks," Cain said. Kaiser also has partnerships with YMCA and schools in some areas to address lifestyle issues that can affect health.

A Cure for Cancer? This ‘Big Data’ Startup Says It Can Deliver

Christina Farr, The Washington Post, January 17

‘Big data’ is one of the most over-used buzzwords in the startup vernacular, and founders rarely have the goods to back it up. So you’ll understand that I was intrigued — but highly skeptical — when an email with the subject line “using data to cure cancer” popped into my inbox.

But Ayasdi, a startup that closed $10 million in venture funding Wednesday, doesn’t just talk the talk. Stanford researchers have been baking the complex algorithms behind Ayasdi (its quirky name means “to seek,” in Cherokee) for over a decade, with the goal of unlocking the hidden value in human genetic data.
In 2008, the founders, Gurjeet Singh, Dr. Gunnar Carlsson, and Harlan Sexton, decided to commercialize the technology.

With the government stepping up its funding for science, they were able to pull in $3.5 million in grants from DARPA, the department of defense agency responsible for building new technology for the military, and the National Science Foundation. The result? A synthesis of machine learning technology and topological data analysis (TDA) that has impressed a score of Silicon Valley investors.

Rather than typing in search-style queries, the tools allow for automated discovery of information. As Dr. Carlsson explained in an interview, “The idea is to answer questions that you didn’t know to ask.”

This year, the 30-person team of engineers will expand its marketing and sales efforts with funding from Khosla Ventures, Floodgate, Data Collective’s Matt Ocko, serial entrepreneur, Steve Blank, and more.

Storied investor Vinod Khosla, who rocked the medical world with the statement that 80 percent of doctors would be replaced by machines, said Ayasdi’s “machine powered intelligence” has the potential to unearth “previously unattainable insights that will help solve some of our most pressing global, social, and economic issues.”

Eric Schadt, Director of the Institute for Genomics and Multiscale Biology, has a team of researchers using the technology to identify the genetic predispositions of many diseases, including cancer, which they hope will help them “glean new insights that will lead to breakthrough drug therapies.”

Related: In the burgeoning field of genomics, entrepreneurs aim to deliver more personalized treatments for life-threatening diseases.

Ayasdi is working with the nation’s top hospitals and medical researchers to uncover more targeted treatments for disease. Singh, the company’s CEO, told me that hospitals and big pharmas are routinely pulling data from public sources — medical researchers are required to publish their data  – and combine it with private data to yield new insights.

The data isn’t anything new — it’s the technology that has evolved. “We have automated the discovery of knowledge from data,” said Singh in a phone interview. “We were able to discover a new type of breast cancer without asking questions.”

Singh was referring to a recent breakthrough where Ayasdi mapped 14 variants of breast cancer. Using data collected during a 15 year period, and studied by thousands of scientists, the algorithms discovered a sub-group of patients that have a higher chance of survival based on their genetic profile.

If a patient falls into this sub-group, it is unlikely that they will require chemotherapy.

In another recent partnership, with Mount Sinai Medical Center, Ayasdi was used to point to targeted treatment options for E. Coli sufferers. E. Coli affects more than 265,000 people in the U.S. every year, and millions around the world. It is known in the medical community for developing resistance to many drugs, and doctors are never 100 percent sure if a treatment will work or not.

Mount Sinai is using Ayasdi to analyze the entire E. Coli genome sequence, which includes more than 1 million DNA variants. This will further our understanding of why some types of E. Coli develop resistance to antibiotics and how we can combat the spread of the bacteria.

Singh, a former researcher at Stanford, told me that the company has secured 20 customers in the oil and gas, government, pharmaceutical, and healthcare sectors. Big name customers include Merck, the Food and Drug Administration, and the U.S. Department of Agriculture.

Copyright 2013, VentureBeat

FT: Data Prescription for Better Healthcare

“It’s quantifying intuition,”

April Dembosky, The Financial Times, December 11, 2012  

Machines beep and drone throughout the neonatal intensive care unit at Toronto’s Hospital for Sick Children. Intimidated parents stand by as nurses scurry between the glass cases. 

Among their various checks, nurses chart a baby’s heart rate once an hour. This is standard practice at most hospitals, but Carolyn McGregor, a professor of health informatics at the University of Ontario, Institute of Technology, says it leaves a lot of critical data to waste.

While a baby’s heart beats around 120 times a minute, it is the pattern of those beats over time that that can give early warning if something is wrong.

With the help of Watson, IBM’s supercomputer that trounced the top Jeopardy! champions last year, Ms McGregor analysed live streams of every heartbeat in a project nicknamed “data baby”. She found patterns that revealed signs of infection 24 hours before the baby showed any visible symptoms. In premature babies, advancing treatment by even an hour can be life-saving.

“At the moment, all nurses have are some very poor alarms that go off all the time, so everyone ignores them,” Ms McGregor says. “We’re trying to watch more streams of data that will give more intelligent alarms.”

The healthcare industry is under extreme pressure from regulators and market forces to reduce costs and produce better outcomes. The US, in particular, spends 30 per cent more on healthcare per capita than other developed countries – about $750bn in total. But despite this higher level of spending, its life expectancy or infant mortality rates are not significantly better, according to a McKinsey report.

The US healthcare industry has therefore become the target of sweeping reforms aimed at reducing tests and procedures that bulk up the bottom line but show minimal to no impact on making patients better. Hospitals and insurance companies must demonstrate efficiency and the improved health of its customers to remain compliant with government policy changes and stay competitive in the free market.

Harnessing vast troves of data is increasingly seen as the solution. From medical devices and insurance claims, to scribbled doctors’ prescriptions and social media sites, the data in healthcare is massive and messy.

One of the promises of organising and analysing all those data is the ability to predict the future, with the goal of early intervention preventing heart attacks or hip fractures from happening in the first place.

“Historically, quality in medicine has been a retrospective affair,” says Stephan Fihn, a doctor and director of analytics and business intelligence at the US Veterans’ Health Administration. “The Holy Grail right now is what would be termed ‘predictive analytics’.”

One of the earliest health systems to begin keeping health records electronically, the VA now has data on 20m patients, including 2bn text notes, 16.2m X-rays, and 1.5bn drug prescriptions. From analysing that data, it can, for example, identify characteristics of patients who suffered renal failure after getting a certain drug, then use that to predict who else is likely to have the same reaction.

Such measures are now of supreme interest to private hospitals in the US. Under new health reform laws, hospitals now face financial penalties if certain patients are readmitted to the hospital within 30 days of being discharged. To maintain profit margins over the long term, they must invest now to develop new protocols that conserve resources and improve performance.

Several hospitals are running data analyses to determine which patients are at the highest risk of being readmitted, for example after a heart attack or pneumonia, then prescribing in-home monitoring devices and outreach programmes to keep them on track.

“We’re moving away from pay-per-pill and pay-per-procedure, and going into a business model that is pay-per-person,” says David Dimond, healthcare strategist at EMC.

Health insurance companies are just as invested in correcting the inefficiencies of the system. Premiums have risen in step with the overspending, and customers, whether employers that sponsor health plans for their employees, or individual consumers, are at a breaking point of what they are willing to pay. Under US reforms, insurers cannot deny coverage based on pre-existing conditions, so they must manage the risk instead of avoiding it.

WellPoint, a health insurer that covers more than 33m patients, has contracts with dozens of technology suppliers, including several to manage and analyse its data.

One of the main goals is to use computers to make the human decision-making process more efficient, says Elizabeth Bigham, vice-president of health IT strategy for WellPoint. For example, the current process for pre-authorising claims can take up to two weeks and involve input from several nurses and physicians. Once a computer is trained on how the company decides to pay or reject claims, most of these humans could be replaced and the patient could have an answer almost instantaneously.

It is even exploring what it can do with all the data generated by medical tracking devices, such as glucometers and heart rate monitors, and even health tracking devices such as the FitBit, a pedometer that tracks the wearer’s activity, calorie intake and sleep patterns. It uses those data to inform diet and lifestyle coaching programmes for patients with diabetes, obesity and hypertension with an eye on preventing a catastrophic – and expensive – heart attack or amputation.
       
John Edwards, director of the health advisory at PwC, expects health insurers to offer lower premiums in the next one to two years to customers willing to wear a mobile health tracking device, similar to car insurance companies that give discounts to drivers willing to install a GPS device on their cars that tracks speed.
“It’s not far-fetched at all that tracking exercise could become part of an insurance design that says ‘you’re doing the right thing’ in order to control healthcare costs,” he says.

Wireless sensors are also seen as a way to control costs associated with the ageing population. Motion sensors in beds, walls and floors of elderly people’s homes can track patterns in activity and movement to predict devastating falls. Researchers at the Sinclair School of Nursing at the University of Missouri are identifying patterns of restlessness during sleep and changes in physical activity that precede a devastating fall or visit to the emergency room.

Bonnie Wakefield, a research professor at the University of Missouri School of Nursing, says such data patterns and predictive algorithms could soon aid nurses making critical decisions at all levels of healthcare.

“It’s quantifying intuition,” she says.

Social Media a Healthcare Data Gold Mine

April Dembosky, The Financial Times, December 11, 2012

Bill Schmarzo envisions a future where holiday photos posted on Facebook become a gauge of a person’s weight loss or gain over time.

The chief technology officer for EMC’s consultant services acknowledges that privacy advocates are unlikely to allow his fantasy to become reality, but the technology that can measure minute body changes in photographs and feed it into someone’s electronic health record already exists.

 “The scary thing is if that data could be used to deny care and insurability,” Mr Schmarzo says.
Healthcare companies are loathe to tread into such sensitive territory, but they are keenly aware of the gold mine of health data stored in people’s social media accounts.

“Studies have shown that people are more willing to share more private medical information in social media than they’re willing to share with their medical providers,” says Martin Kohn, chief medical scientist at IBM.

Pharmaceutical companies already analyse social media sites to track reports of side-effects of their drugs. That data can help correct formulas more quickly than waiting for the results of years-long clinical trials; it can also be used to set prices and test marketing slogans.

Public health officials are also interested in social media data as a source of information on disease outbreaks. Several start-ups are working on algorithms that study Facebook, Twitter, and blog posts to track early signs of infectious disease outbreaks, as people generally complain to their friends long before public health agencies can collect doctors’ reports and issue official warnings.

One outbreak of the Norovirus stomach bug at a student journalism conference in Canada was live-tweeted earlier this year, with posts such as “Motion that nobody else on this bus puke” and “36 hours without leaving my hotel room” signalling its coming and going before any traditional surveillance system had noted it.

The trouble with social media data are that they are fragmented and incomplete, warns James Kaufman, manager of public health research at IBM’s Silicon Valley lab, and there are limits to the types of insights that can be drawn from such patient-reported data.

“Colds, flus, sure people report that,” he says, “but no one’s going to report Aids or haemorrhoids.”

Why Data is the Key to Better Medicine - And Maybe a Cure for Cancer

Derrick Harris, GigaOm, November 28, 2012

The health care industry might have embraced the big data movement with open arms, but embracing it with open data probably would be more effective. Hospital organizations, researchers and the tech companies serving them have lots of great ideas — and have achieved some great results, too — but, ultimately, efforts to use big data to transform the industry will only be as good as the data these stakeholders have to work with. Right now, that isn’t always everything they need.

Have access, will innovate
Wired published an interesting profile Tuesday morning that exemplifies what’s possible when smart people have access to good data. The piece showcases the work of a man named Fred Trotter who has accessed reams of buried Medicare data via a Freedom of Information Act request and is uncovering some potentially valuable information. Already, the article explains, he has built a “Doctor Social Graph” by analyzing some “60 million relationships between doctors, and how often they refer patients to one another.” His next mission is to build a doctor rating system based on data he’s uncovered about credentials, nursing home inspections and other relevant info.

Elsewhere, companies such as Palo Alto, Calif., startup Apixio are trying to make hospitals more efficient by using semantic analysis to connect the dots between patient charts, electronic medical records, billing data and whatever other sources of information that hospitals generate. (We covered Apixio in early 2011, although the company has significantly expanded its services since then.) In health care, everyone seems to have their own way of doing things, as Apixio natural-language-processing scientist Vishnu Vyas told me recently, so “the variety of the data becomes as important as the volume of the data.”

Linda Drumright, GM of the Clinical Trial Optimization Solutions group at IMS Health, agreed. She explained that her company is able to do its job because it has access to mountains of data from pharmacies, insurance claims, medical records, partners and other sources. All told, it houses 17 petabytes of data spread across 5,000 databases. Her division’s clients, which generally include pharmaceutical and biotech companies running patient trials, need all this data in order to ensure their trials will actually be successful.
One recent customer wasn’t able to recruit test subjects fast enough, she noted, and IMS helped it comb through its criteria about who to or not to include in the trial only to find “that the patient population they were looking for didn’t exist.” As IMS went back and began eliminating criteria and iterating design, it realized that trial never should have begun in the first place.

There are a million ways to think about how to use this data, Drumright said, and as more customers begin to fully understand what they can do with it, her goal is to “make this information accessible in a way where it’s easy at the point where it’s needed, and consumable where it’s needed.”

The key to curing cancer might be more data
But whatever Trotter, Apixio, IMS and others accomplish will have been made possible because they have access to some valuable datasets, albeit not always with great ease. Many individuals who’d like to improve the health care system — if not our health, generally — aren’t so lucky. Take, for example, the world’s genetic researchers. It’s very possible the data they need to discover the medical Holy Grail of a cure for cancer is locked in gene sequence data that only very few people will ever see.

According to University of California, Santa Cruz researcher David Haussler, the limited access that many geneticists and computer scientists like himself have to valuable genetic data is “a crime.”

“We are on the brink of a real new understanding of cancer by being able to sequence cancer genomes,” he told me during a recent interview, but big data will be the key to unlocking it.

There are 1.6 million cases of cancer in the United States every year, Haussler explained, and most of the information from those tumors is being ignored. This is partially because of privacy restrictions about who can access personal medical data and for what purposes, and partially because there isn’t yet a concerted effort to collect the necessary genetic samples. As genome sequencing gets faster and cheaper, he says researchers need access to healthy and cancerous samples from the same person — and as many of these samples as possible — in order to analyze the “astounding” number of molecular changes that occur in every type and variation of cancer.

“We can’t completely understand what we’ll find, but we know we the only way we’ll pull out signal from the noise is to [analyze all these genes],” Haussler said.

Haussler understands the need for privacy regulations, but thinks there’s an opportunity to at least ease some current restrictions on how researchers access data. Even when there are relatively large (if not ideal) datasets available such as with the Cancer Genome Atlas project, researchers must apply to the National Institutes of Health for access, and the data must always remain behind an organizational firewall. Every cancer patient in the country could agree to having their data available to researchers, he said, but as long as that data isn’t accessible over the internet it’s only of limited utility.

He — along with others in the field — thinks cloud computing could be the solution because it gives genetic researchers a central location where they can access and perform computations on the data. Haussler and his team that house the Cancer Genome Atlas and a couple other projects currently have more than 400 terabytes of data and expect to have around 5 petabytes of data eventually. Downloading that is infeasible save for access to high-speed research networks, so “we need a place where people can experiment with these big data problems,” Haussler said.

In the meantime, Haussler and his peers will keep on collecting and accessing genome data however they can. And they’ll keep building software packages and algorithms that analyze that data better and faster than ever before. However, he lamented, “If we had the big data out there in an unrestricted setting, then all the best minds in the world would already be crunching on it.”

Big Data Won't Save Pharma, But Smart Data Might

Data analytics has the potential to do much more if applied across the pharmaceutical enterprise.

Guy Cavet, Genetic Engineering and Biotechnology News, November 2012

Intelligent use of large-scale data has become fundamental to other industries: finance, insurance—even sports. But despite its importance in areas of research, data analytics has the potential to do much more if applied across the pharmaceutical enterprise.

In the past 15 years, biology has been transformed by the availability of large-scale genetic and genomic data. The first ten years of work on the human genome yielded one draft genome. The last ten years have yielded over ten thousand. Advances in technology have enabled high-throughput gene expression profiling, cancer genome analysis, and other disciplines to change the way biology is studied. Cheminformatics allows companies like Numerate to screen millions of compounds for activity by purely computational prediction. However, there are much greater opportunities for data-driven transformation across the broader pharmaceutical enterprise.

These opportunities arise, in part, because of the broad trend toward data being tracked and recorded in new and far-reaching ways. Importantly, many of these are outside the pharma industry. Medical records are collected electronically on an unprecedented scale, driven in part by federal “meaningful use” programs. These records reveal how diseases manifest and how treatments are used in the real world. Social media also contains vast amounts of information on real patient experiences with both diseases and treatments. And in the sales and marketing of drugs, data on program effectiveness is collected in real-time by reps, and companies like Aktana are interpreting it to understand where physicians perceive value.

Getting data is only the first step. The true value arises from analytics that generate actionable insights. In many cases, this means predictive modeling: developing algorithms that reveal what drives an outcome of interest (such as response to therapy or drug choice) and allowing that outcome to be predicted in the future. The data scientists that can carry out this type of analysis are multidisciplinary experts with skills from statistics, computer science, biology, chemistry and other fields, and they are highly sought after.

The conventional ways to engage data analysts involve building internal teams of scientists or buying time from consultants. However, data analytics is also particularly well-suited to crowdsourcing, which opens up a problem for many people to address. It’s inevitable that most of the world’s experts in any domain are outside any single pharma company. Even with strong internal teams, as Bill Joy of Sun Microsystems insightfully noted, “Most of the smartest people work for someone else.” Crowdsourcing allows those people to be tapped in a highly flexible and cost-effective manner. A team of experts can coalesce around a problem, working on it only as long as necessary, and then move on.

In 2006, Netflix used crowdsourcing to improve their ability to suggest movies to their customers. Rather than just inviting people to work in isolation, they set up an online competition in which people submitted entries in real-time and vied to come up with the best solution. This is a particularly effective approach to predictive modeling analytics. Seeing their rivals above them on a leaderboard drives people to continuously generate better results. In the Netflix competition, the company’s internal method was surpassed within six days, and the eventual winner was more than 10% better.

The competition approach is equally applicable to pharmaceutical industry problems. For example, Boehringer Ingelheim sponsored a contest to develop methods to predict small molecule safety that resulted in a 25% improvement over an industry standard approach. In the Heritage Health Prize competition, methods are being developed to predict which patients will require hospitalization, and for how long, over the next twelve months. Other competitions have been used to predict patient outcomes, sales patterns, and clinical outcomes. In each case, the results were better than any methods that had previously existed.

The full potential of data analytics requires accessing and using data in creative ways. For example, after a drug launches, information about the drug is rapidly generated in the outside world through patient and physician experiences. This information is currently largely untapped. It is entered into electronic medical records, tweeted, posted on Facebook, and entered into community sites such as Patients Like Me. This data is often unstructured and very noisy, but companies such as Israeli startup Treato are beginning to systematically organize it. Despite the complexities of working with data like this, skilled data scientists can extract meaningful patterns about drug-drug interactions, what drives patients to start and stop medications, or which patients will not adhere to their prescriptions, to name a few.

Predictive models even have the potential to tackle some of the most critical decisions in drug development, such as whether a clinical trial will be successful or whether a licensing deal will eventually lead to a drug. Billions of dollars rest on these decisions, but it is rare that all available relevant data is systematically employed to predict the probability of success. Of course, no algorithm can make such predictions with perfect accuracy, and no computation can replace a clinical trial. However, for an organization deciding between multiple costly development programs, having any improvement in ability to predict results is immensely valuable.
Putting data beyond the company firewall for outside experts to use may not be a natural step for organizations that are accustomed to carefully protecting their sensitive information. However, with the appropriate steps, the confidentiality and privacy of pharmaceutical and medical data can be carefully preserved. For example, when Boehringer Ingelheim sponsored a competition to predict small molecule activity, neither the structures of the molecules nor the specifics of the activity were revealed. In a competition to identify patients with type 2 diabetes using electronic medical records, the data was carefully de-identified to meet HIPAA standards. Privacy and confidentiality concerns can also be addressed by restricting access to trained and trusted individuals.

With drug development costs rising and approvals declining, new approaches are sorely needed. It’s too simplistic to see “big data” as a knight in shining armor, but the intelligent use of rich data, regardless of size, has the potential to help dramatically with problems from basic research to commercial operations.

Crowdsourcing Medical Treatments

Samahope Crowdsources Simple, Life-Saving Surgeries For The Poor

Ellen McGirt, Fast Company, November 2, 2012

Veteran social entrepreneur Leila Janah of Samasource recently co-launched a new project to crowdfund medical treatment for the very poor. Think of it as Kiva for surgery.


“I just started bawling,” Leila Janah is telling me about a trip she took to Sierra Leone earlier this year. "I’m usually pretty steely as a matter of course. But I’ve been crying a lot more lately.”

Janah, the founder of Samasource, a nonprofit organization that brings paid digital work to very poor women and youth, is no stranger to harsh realities. She studies them for a living.

But a sweet teenaged girl named Tiangay Kaiwo moved Janah to tears. Kaiwo was waiting for surgery at the government hospital in Bo to repair the extensive damage to her body after her teacher brutally raped her. Traditional tribal remedies involving herbs and a bath in boiling water exacerbated her condition. Kaiwo had been living with a painful rectovaginal fistula for over a year, but best as Janah could tell, the rapes began when she was 12 years old. Janah met dozens of girls and women with similar stories, all needing life-changing surgeries that nobody could afford. “Nobody even to hold their hands, to tell them that this wasn’t their fault,” Janah said.

When the slice of the market you are trying to corner is as troubled as the one that Janah is, then tears are clearly a rational response. But after tears, at least if you’re built like Janah, comes action.

Enter her latest project, Samahope, an experiment in crowdfunding medical treatment--like burn care--for the very poor. Think of it as Kiva for surgery. “There are millions of people who need corrective surgeries that we take for granted in the West,” she says. But the very poor often need types of care, like fistula surgery, that are now wholly unfamiliar and largely unnecessary in the developed world. (You can contribute to the development of the site via their Indiegogo campaign here.)

The site launched last month and has already funded a handful of surgeries; there are over 70 profiles on the site. If you believe in the premise of the League of Extraordinary Women, then the business case is clear: If you get one girl back on her feet, she can go to school. If she goes to school, she can get a job. Enough girls join the workforce and a country gets uplifted. But Janah sees another benefit. “They want to say what happened to them, to tell their own stories,” she says.

She has collected so many of these stories--of the poor and the embattled and their search for basic human rights through employment--that she’s writing a book. “I just interviewed a security guard at a hotel in Freetown," Janah said. "He grew up as a rebel and child soldier in the conflict--think about that for a minute--then forced into the diamond mines. His life was so full of conflict, a constant struggle to access basic human resources, that it’s impossible to wrap my mind around.” Recalling young Kaiwo, “For someone like her, being able to tell her story and help other girls not become a victim is a very powerful thing.”

Samasource, which last month closed a $7.5 million round of philanthropic funding led by The MasterCard Foundation, has become the darling of the tech crowd for its deft use of the Internet to match an excess capacity of potential workers with the jobs they need to live in dignity. “But the scale of the problems can seem so great compared to the resources you have to address them,” says Janah--thus, the crowdsourcing project. Unlike her peers in the for-proft tech world, she is not going to be able to turn to her staffers with breathless reports of sky-high valuations, rounds of venture funding, or promises of equity upside.
And yet, Janah is convinced that dignified work can resurrect even the most damaged lives, and that her own business case is sound. “We’ve gotten the microwork model on the agenda of a lot of foundations and government entities. We just have to prove it can scale.”

Janah recalls with fondness the “aha” moment when she knew that Samasource could actually be a business. But the slog of ramping up to achieve a massive goal is largely free of lightbulb moments. “Not a day goes by when I don’t doubt myself or question something,” she says. So instead, she takes the power of microwork and puts it to work for herself and her team. “I had to manage my own psychology around this. So, I’ve trained myself to pause and celebrate each step.”

She rattles off a list of things that sound more startup than do-good: Realign your expectations, hit your goals, stay close to the customer, stay connected to your mission. She ends up sounding more like a Zen master than elevator pitcher. “It’s about looking down and doing what’s in front of you. Truly savor it. Then do the next thing. My job is to make sure we’re all going in the right direction, and at the end of the year, the sum of those steps adds up to something really great.”

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.