Machine Learning Techniques Cut Surgical Infections By 74%

By applying predictive analytics and machine learning techniques to patient data and real-time data from operating theatres the University of Iowa Hospital in the US managed to reduce the incidence of wound infections acquired during surgery by 74 percent. The hospital has now spun-off a company, Dash Analytics, to commercialise its technology.

Dr John Cromwell, associate chief medical officer at University of Iowa Hospital, and now also CTO at Dash Analytics, initiated the project in 2012. Speaking at Tibco Live in Las Vegas, he said: “We started work with the hypothesis that if we could predict which patients would get surgical infections we could change the wound management strategies at the time of surgery to reduce the risk of infection.

Surgical infection in the US is the number one hospital infection and carries the most morbidity,” he said. “It is also the most expensive type of hospital infection to treat.

(The most recent, 2011, statistics available from the US Center for Disease Control put the annual number of surgical site infections at 157,000 out of a total of 722,000 hospital-acquired infections).

Cromwell explained that a number of factors contributed to the risk of surgical infection: “The patient might be malnourished, or morbidly obese, or they might be on medications that supress the immune system. The duration of the operation influences the risk, and whether you keep the patient warm throughout the entire operation makes a difference.

We designed a real time tool that uses the medical record data plus the real time data from the operating room to provide some decision support to the surgeon at the time of the operations to change the wound management strategy.” He said surgeons had basically two options at the time of surgery that could be applied to mitigate the risk of infection: Leave the wound open or use a technique known as negative pressure wound therapy where the wound is sealed and a vacuum applied to it.”Negative pressure wound therapy can reduce infections significantly if it is applied to the right patients, but it is not inexpensive so we wanted to be selective. There are different hypotheses as to why it works. “We used the analytic tools to determine which patients should get negative pressure therapy and within two years we had reduced the surgical infections by 58 per cent and in three years by 74 per cent. “We were very surprised. The result was far better than antibiotics. … And we probably saved the hospital between [US]$1.2 million and $2 million per year.”