Sarah, a 60-year-old woman in remission from colon cancer, moves from San Francisco to Boston. She celebrates reuniting with her grandkids by eating a delicious Indian dinner. But it’s a little spicy. During the next few days, she suffers from a dull pain in her lower abdomen. Her son, concerned, sets up an appointment with a new internist.

After hearing Sarah’s complaint and history, the doctor orders a series of tests including a CT scan. The image shows a 2-centimeter, ill-defined nodule in her pelvic area. Other than that, the scan is unremarkable.

The radiologist compares the new scan with a series of digitally stored CT scans from the office of Sarah’s San Francisco-based oncologist. The internist is relieved to see that the nodule is unchanged. Furthermore, a series of gene chip analyses that indicate which genes are over-expressed and which are under-expressed show that Sarah’s loss of imprinting markers are low, indicating that cancer has most likely not returned. After considering an ever-larger data set, the internist sits Sarah down and makes his only suggestion: Avoid spicy foods.

A few years ago, Sarah probably would have had to endure further treatments and perhaps exploratory surgery. Now, with a rich and accessible trove of data from a variety of digital and genomic sources, health-care providers can make better decisions faster. This is good. It is not, however, the whole story.

For most of medicine’s history, the average patient has generated very little data. Most patients see their doctors a couple of times per year, if that, and their diagnoses and treatment regimes are simply written down. A typical patient file consists of about 1.5 pounds of paper. This includes not just clinical data but also insurance, pharmaceutical and other administrative information. Except for people who end up in intensive care and are hooked up to monitors, individual medical records still consist of relatively small data sets. Now, however, new technologies are generating massive and increasingly personalized health-care data sets.

Simply sequencing one version of the human genome required that a new company build the world’s largest private computer and maximize the parallel processing capacity of Hewlett-Packard’s Alpha chips. Genomic code is four letters on two dimensions. Proteins have a 21-letter code that has to be modeled in three dimensions. Never mind imaging or modeling whole organs.

The volume of data that will flow through a doctor’s office in the not-too-distant future will explode, as will the need to coordinate treatments and specialists. Hospitals, pharmaceutical companies, doctors’ practices and all types of IT companies will increasingly seek life-science-literate CIOs.