UCSF Studying mHealth Data to Predict Post-Operative Recovery
UCSF researchers are applying AI technology to data gathered from mHealth wearables and other sources to develop algorithms for tracking recovery times for joint replacement surgery
The connected care program will use AI software to analyze data from a broad range of sources, with the goal of developing algorithms to help providers anticipate and treat health issues before they lead to hospitalization.
“We want to combine patient-reported outcomes, data from electronic medical records and sensor data to predict how patients will recover following joint replacement surgery,” Stefano Bini, MD, an orthopedic surgeon and professor of orthopedic surgery at UCSF and the project’s lead researcher, said in a press release.
If proven reliable, the algorithms could replace patient surveys that are filled out before surgery and at one year after surgery.
The company is partnering with Cloudmedx on the project.
“Rather than the standard and rather useless calculation of relative Hazard and Risk Ratios of one outcome versus another when looking at one variable and adjusting for all others (useless because the resulting data is incredibly hard to apply in clinical practice where patients present with multiple variables many of which like gender and age are not at all variable), the algorithm was able to clearly identify cohorts (clusters) of people whose variables (features) were more likely to be associated with a given outcome (PRO) and take any specific candidate and place them in a risk cluster,” Brini said. “This is phenomenal and way more practical.”