Using big data to manage medical expectations

MIT Sloan professor helps develops model to help patients measure outcomes

CAMBRIDGE, Mass., July 1, 2014--For all the advances in both medicine and technology, patients still face a bewildering array of advice and information when trying to weigh the possible consequences of certain medical treatments. But an MIT Sloan School of Management professor and her team have developed a hands-on, data-driven tool that enables patients to obtain personalized predictions for their recovery from surgery. These personalized predictions can help patients better manage their expectations about their speed of recovery and long-term effects of the procedure.

"People need to be able to fully understand the possible effects of a medical procedure in a realistic and clear way," says MIT Sloan Associate Professor of Statistics Cynthia Rudin. Using data from the experiences of UCLA clinic patients who underwent prostatectomies, she and her colleagues were able to develop a statistical model for recovery curves. This model "not only shows people what they can expect about their recovery on average, based on their own specific characteristics, but it also clarifies the uncertainty in the shape of the recovery curve. It shows a range of possible realistic outcomes.." While Rudin and her colleagues focused their efforts on prostatectomy, the same statistical model can be used to model recovery curves from other types of surgery and recovery from other medical conditions, such as stroke.

Rudin's team, consisting of MIT PhD student Fulton Wang and Tyler McCormick and Dr. John Gore, both at the University of Washington, developed their Bayesian modeling approach to recovery curve prediction in order to forecast sexual function levels after prostatectomy. They used data from about 300 patients both before radical prostatectomy surgery and during the four years immediately following surgery. Their interactive tool is designed to be used before the patient has a prostatectomy in order to help the patient manage expectations. In their interactive tool, a central predicted recovery curve shows the patient's average sexual function over time after the surgery. The tool also displays a range of lighter-colored curves illustrating the broader range of possible outcomes.

"We wanted to help patients who are considering this particular surgery to understand what they could expect," says Rudin. "We can't tell you exactly what your recovery will look like, but at least we can forecast a personalized recovery curve for you and show you an informed prediction of your possible outcomes. The model can be used in an interactive way. For example, patients could adjust their reported age or reported sexual function levels to see how their predicted recovery curves change.

Medical data used in the paper was provided by one of the paper's co-authors, Dr. John Gore, an assistant professor of urology at the University of Washington. He plans to have the interactive tool approved and available for patient use within the clinic in a few months, says Rudin.

Patients can greatly benefit by being able to leverage the vast amounts of medical data that are now being collected in order to make data-driven decisions, she says. "Predictive medicine is getting to be a really big deal. Until now, when people had questions about possible treatment effects, their doctors might give them vague, textbook-type responses and they'd get a range of answers based on whom they ask. It's time for patients to be able to make decisions based on data. And this type of work helps the data speak."

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