The race is on to develop better ways to predict if patients will develop diabetes, heart disease or other critical conditions.
Spurred by employers and insurers that want health-care providers to prevent illness—and not merely treat it—doctors and hospitals are creating ever more complex algorithms to forecast their patients’ medical future. And they’re searching for new kinds of data to make those predictions as accurate as possible, mining behavioral, consumer and financial data for potential clues.
Some hospitals are collecting new information from patients directly, while others have sought data from companies that sell consumer and financial information, or federal agencies that provide statistics on poverty, housing density and unemployment.
Knowing more about how people live—from their interests to their income—could prove useful as doctors look for clues to poor health and tailor interventions to address patients’ needs, potentially preventing illness and saving money, proponents of this approach say.
“So much of what determines a person’s health and well-being is independent of medical care,” says Rishi Sikka, senior vice president of clinical operations for 12-hospital Advocate Health Care in Downers Grove, Ill.
Advocate’s efforts offer a window into these new missions to get better insight into patients’ health. In 2012, Advocate introduced an algorithm to predict which hospital patients would come back less than a month after leaving. The algorithm relies on a history of patients’ ailments, prescriptions and laboratory tests, vital signs and other medical care to single out who is at risk. In its first nine months, the predictive tool slashed readmissions for high-risk patients by 20% by allowing Advocate to better direct aid to those in need, says Tina Esposito, vice president of Advocate’s Center for Health Information Services.
Now the provider wants to make its algorithm even stronger by mining new information about its patients. By the end of the year, Advocate says it will acquire consumer data from a company that mines purchasing and demographic information for retail chains and marketers. Advocate’s team of data scientists will analyze the new trove of data—a slew of statistics on marital status, hobbies and household income—for its predictive potential.
The data could fill gaps in what doctors know about financial or lifestyle factors that influence patients’ health. Doctors are “largely blind” to details that are not included in patients’ medical records, says Dr. Sikka.
So far, the verdict is that the more-complex algorithms need stronger data to be truly effective. One study looked at more than 70 predictive models that largely relied on patients’ medical history to identify people at risk for an unexpected hospital admission.
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