Editor's Note
Using machine learning on electronic health record (EHR) postoperative data linked to the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) outcomes data, researchers developed a model with 163 predictors of postoperative complications at the University of Colorado Hospital.
Of 6,840 patients analyzed with the model, 13.5% had at least one of the 18 complications tracked by ACS NSQIP. The model had 88% specificity, 83% sensitivity, and an area under the curve of 0.93.
This model may be useful for electronic surveillance of postoperative complications, the authors say.
Read More >>Takeaways Providers are generally seeking to reduce use of travelers…
Human trafficking (HT) is a hidden-in-plain-sight crime—victims walk among the…
Every year, OR Manager shines a light on staffing issues…