Using Analytics to Reduce Perioperative Clinical Variance

The bulk of identified waste in US health care spending is attributed to hospital-related care variance and inefficiencies from high-cost care delivery practices. Surgical procedures account for 70% to 80% of variance cost. Using a suite of artificial intelligence approaches, advanced health care analytics can process complex patient care data to identify hidden costs and variability from daily activities in the surgical services department. Electronic health record data that have been cohorted for similar surgical traits can improve efficiency and effectiveness to drive actionable changes to reduce unwarranted clinical variation and improve perioperative care quality while reducing costs. This article discusses the types of advanced health care analytics used in the surgical cohorting process and provides a clinical example that produced cost savings by reducing product variability.