Up to three out of every hundred hospital patients in the United States who undergo surgery will develop surgical site infections, according to the CDC. These infections can be difficult to manage and rack up costs for hospitals and are often linked to decisions made during surgeries in the operating room.
Iowa City-based University of Iowa Hospitals and Clinics has developed an approach that uses predictive analytics to influence the factors in the OR that could result in greater risk of SSIs.
"Many factors determine whether or not a patient gets a surgical site infection, including characteristics of the patient, their medical history and the illness they're being treated for," John Cromwell, MD, associate chief medical officer and director of surgical quality and safety at the hospital, told Computer World.
Deploying predictive analysis in the OR is a unique route, as many healthcare organizations will generally focus on data generated from non-surgical populations and non-surgical settings, according to Dr. Cromwell.
By focusing surgery-specific datasets, like blood loss, duration of surgery or patient biomarkers, the organization has been able to combine data from previous operations with information from those taking place in real time to better predict which patients are at greater risk of developing an SSI and why.