Researchers from the University of Michigan have developed a predictive analytics algorithm that uses basic EHR data to flag patients at high risk of developing complications from the hepatitis C virus (HCV), according to a study published in the most recent issue of Hepatology. As payers seek to control the costs of extremely expensive but highly effective new HCV treatments like Sovaldi, the use of clinical analytics to pinpoint the most meaningful course of treatment for patients may help to avoid unnecessary spending.
The predictive analytics model was developed using a dataset derived from the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis (HALT-C) trial conducted by the National Institutes of Health. The team used machine learning techniques to process clinical information such as lab results, age, body mass index, and details of the virus type to create a risk score for patients. The score is more accurate than previous attempts because the algorithm uses more lab values than other models and analyzes how the values change over time…(Read More Here)