‘Mobility matters’: Using remote tracking to predict hospital readmission

Most hospitalized patients are eager to get their discharge papers and go home — but far too many return to the hospital too soon. Nationally, about 20 percent of patients discharged from the hospital return within 30 days, according to the Agency for Healthcare Research and Quality.

The cost to their health — and the nation’s health care system — is considerable, notes Daniel Polsky, Bloomberg Distinguished Professor of Health Economics at Johns Hopkins Carey Business School.

“No one really wants to be in the hospital, and readmissions drive up health care spending,” says Polsky, whose research explores how health care is organized, managed, financed, and delivered, especially for low-income people.

To date, most research on hospital readmission has relied on data gathered in the hospital, up until the time of a patient’s discharge, he notes. In a new study published in Scientific Reports, he and his colleagues instead focused on the 30-day period after patients returned home. The clinical trial tracked patient activity levels using either a smartphone or wearable device. The researchers crunched the flow of data coming from these devices using machine learning tools, building on their earlier work that used more traditional regression techniques.

“We found that prediction of 30-day hospital readmission significantly improved when using remotely monitored patient data and machine learning approaches for analysis,” says Polsky.

A way to intervene

Attention to hospital readmission rates has intensified in the decade since the launch of the Affordable Care Act, which established penalties for hospitals that experience “excess” readmissions when compared to “expected” levels of readmissions. Since that time, hospitals have incurred nearly $1.9 billion in penalties, according to the American Hospital Association.

“Many readmissions are preventable, so hospitals have been looking very hard at what brings people back, but so much of that happens outside the view of the hospital,” says Polsky. “The idea with remote patient monitoring is that if we can very subtly be able to monitor what’s going on with patients at home, perhaps we can find a way to intervene.

“The hypothesis of the value from remote patient monitoring [is] centered [on] on the signal offered by changes in patient mobility,” he adds. “Mobility matters.”

In the randomized clinical trial of 500 patients who were discharged from two hospitals in Philadelphia, half were monitored via their smartphone, which tracked activity patterns, and the other half via a wearable device, which tracked activity as well as sleep patterns.

“We found that while data from the wearables outperformed data from smartphones, the difference was small,” says Polsky.

While this study did not capture heart rate or other biometrics that can be measured by some wearable devices, the researchers suggest that future studies could incorporate these metrics to see if the additional data could further improve hospital readmission prediction rates.

Targeting the most vulnerable

While Polsky is hopeful that this work might encourage hospital systems to begin investigating — and adopting — remote monitoring efforts after hospital discharge, he notes that it will be important not to lose sight of those who are at highest risk of readmission: the elderly and those who are socially and economically disadvantaged.

“People who are most likely to be tracking their health and most comfortable using smartphones and wearable technology are typically the healthy and the wealthy — people with resources who have the ability to prioritize their well-being,” he says. “If remote monitoring is going to work, hospitals will need to develop a process that is equitable and that targets the most vulnerable patients in our community.”