Reducing Risk with Predictive Analytics

It is imperative for physician anesthesiologists to perform risk assessments with their patients both before and after operations. The challenges associated with these assessments are the unknown variables each patient innately possesses. But recent developments in technology are making the task easier. Predictive analytics software is now being applied to identify the variables that put certain patients at greater risk. This software enables anesthesiologists to better record, track and analyze patient data to make real-time predictions about patient risk factors and take precautionary measures.

For example, infections from surgical wounds are one of the most common reasons for patients to be readmitted to the hospital. To combat this trend, medical professionals have begun using predictive analytics software to recognize which surgical patients are most at risk for postoperative infections and implement a strategy that reduces that risk.

“The logic behind predictive analytics is to compare a patient’s history, medical condition, and in-surgery vital signs and complications against a model that associates specific factors with levels of risk for developing specific medical conditions,” says John Cromwell, MD, associate chief medical officer and director of surgical quality and safety at the University of Iowa Hospitals and Clinics.

The potential for predictive analytics to reduce post-op infections and readmissions could lead to many benefits, including:
Fewer complications
Better health outcomes
More efficient use of provider resources
Reduced risk of malpractice suits
Less stress on patient and family
Lower patient medical expenses
Decreased patient time away from work

With technological advancements like predictive analytics software, physician anesthesiologists are able to meet the challenges associated with risk assessment with greater confidence and, more importantly, better results.

Learn more about the development and leveraging of predictive analytics: