Michael Thompson has over 30 years of using analytics to unleash hidden stories within data. The quest has led him to use a variety of data warehousing, visualization, statistical, data mining methodologies, and analytic modeling creations. His team’s work has been shared in the Wall Street journal, industry symposiums, and publications over the years. With a personal goal to help others on their journey to find opportunities hidden in their data, Mike has shared his adventures as a speaker at industry events (national and international) on computational health topics and as a lecturer on data and analytic topics at Georgia Tech University, Mercer University, Emory University, and UCLA. After two and half decades of torturing data with various levels of sophistication, Mike updated his skills by earning a Master of Predictive Analytics/Data Science degree from Northwestern University. Within the private sector, Mike has led analytic teams across large financial and healthcare organizations. Currently, Mike leads a multi-disciplined team (physicians, nurses, statisticians, data scientists, data analysts, data engineers, and data warehouse developers) to fulfill the insatiable financial, operational, clinical, quality, and population health analytic needs of the organization as the Executive Director of Enterprise Data Intelligence at Cedars-Sinai in Los Angeles, CA.
Improving hospital-wide patient flow requires an appreciation of the hospital as an interconnected, interdependent system of care. Learn how supervised machine learning was used to create predictive models for LOS, ED arrival, ED admissions, aggregate discharges, and total bed census to reduce patient wait times, reduce staff overtime, improve patient outcomes, and improve patient and clinician satisfaction.