Machine Learning, EHR Data Predict High-Risk Surgical Patients

Utilizing machine learning tools that leverage electronic health record (EHR) data from a single organization could help providers predict patients at high risk of surgical complications more accurately than traditional approaches, a study published in PLOS Medicine found. Complications arise in 15 percent of all US surgical procedures performed, the researchers noted, with high-risk surgeries resulting in complications up to 50 percent of the time. The expenses related to these events quickly add up: the total cost of surgical complications in the US is approximately $31.35 billion per year. Organizations across the country have increased their efforts to identify high-risk patients and reduce surgical complications. However, if providers don’t have timely access to important patient data, or if organizations lack strong predictive models, it can be difficult to detect high-risk individuals.