New research published in the INFORMS journal Operations Research shows that healthcare systems can substantially reduce overtime, idle time and overall staffing costs by adopting a multilocation, dynamic staff-planning model for anesthesiologists. The study is based on the University of Pittsburgh Medical Center (UPMC), which cut daily overtime and idle time across 11 hospitals, generating over $800,000 in annual cost savings.

Cardiovascular diseases account for approximately 80% of all deaths caused by known medical conditions, making them the leading cause of mortality worldwide. The present study investigates the use of electrocardiogram (ECG) non-linear features and different topological medical features (heart rate, anthropometry, blood, glucose, and lipid profile, and heart rate variability) to discriminate between different Framingham Cardiovascular Risk Scale status groups in adult obesity using machine learning.