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For IBM, this very scenario promises growth. With more than $4 billion in annual revenue, the health care practice within IBM Global Services has the size of a Fortune 500 company. It runs large data centers for hospitals and insurance companies. It also helps them analyze the data, looking for patterns of symptoms, treatments, and diseases—as well as ways to cut costs. This is part of a trend toward statistical analysis in the industry and the rapid growth of so-called evidence-based medicine. But one of the most valuable streams of data—the doctor’s notes—rarely makes it into the picture, said Joseph Jasinski, who heads research for IBM’s health care division. This is where the doctor writes down what he or she sees and thinks. Sometimes it is stored in a computer, but only, Jasinski said, “as a blob of text.” In other words, it’s unstructured data, Watson’s forte. “There’s a strong belief in the community that if you could study clinical notes, you could analyze patient similarities,” he said. Neurologists’ notes—going back to Ferrucci’s case—could have pointed to common symptoms between patients with the suicide disease and others with knots in a muscle just below their shoulder blade. This analysis could expand, comparing symptoms and treatments, and later study the outcomes. What works? What falls flat? Which procedures appear to waste money?

Despite the growth of evidence-based medicine, many of these studies are hard to carry out, especially in the splintered $2.3 trillion American system. The doctor prescribes the treatment and the insurance company pays for it, but all too often neither of them gets the most vital feedback: how it worked. The feedback loop, in the language of statisticians, rarely closes. The most promising sites for this type of analysis, Jasinski said, are self-contained hospital networks, which keep voluminous records on patients and do extensive follow-up. They would include the Veterans’ Administration, the Mayo Clinic, and Kaiser Permanente in the United States. Many countries with national health care systems also have promising data. Denmark, where IBM has been running the data since 2006, could provide a national laboratory. There, a medical Watson could diagnose diseases, suggest treatments that have proven successful, and steer doctors away from those that have led to problems. Such analyses could save lives, Jasinski said. ”We kill a hundred thousand people a year from preventable medical errors.”

In fact, the potential for next-generation computers in medicine stretches much further. Within a decade, it should cost less than $100 to have an individual’s entire genome sequenced. Some people will volunteer to have this done. (Already, companies like 23andMe, a Silicon Valley startup, charge people $429 for a basic decoding.) Others, perhaps, will find themselves pressed, or even compelled, by governments or insurers, to submit their saliva samples. In either case, computers will be studying, correlating, and answering questions about growing collections of this biological information.

At the same time, we’re surrounding ourselves with sensors that provide streams of data about our activities. Coronary patients wear blood pressure monitors. Athletes in endurance sports cover themselves with electronics that produce torrents of personal data, reading everything from calorie burn to galvanic skin response, which is associated with stress. Meanwhile, companies are rushing into the market for personal monitoring. Zeo, a Rhode Island company, sells a bedside device that provides a detailed readout every morning of a person’s sleeping patterns, including rapid-eye movement, deep sleep, and even trips to the bathroom. Intel is outfitting the homes of elderly test subjects with sensors to measure practically every activity possible, from their nocturnal trips to the bathroom to the words they type on their computers. And each person who carries a cell phone unwittingly provides detailed information on his or her