Groundhogs Aren't the Only Ones Who Can Predict the Future: Using Analytics to Improve Performance
This session will share the results of a predictive analytics study conducted jointly between Catholic Health Initiatives and the University of Denver's Daniels College of Business. You will learn the difference between data and information as well as the challenges and opportunities of using HR information. Karlzen will help you understand the difference between correlation and causation and will share the predictive metrics that were discovered during this study that could improve your organization's performance.
Paul Karlzen
Paul Karlzen (JD, MBA, and PMP) serves as the Director, HR Information & Analytics at Catholic Health Initiatives (CHI). One of the nation’s largest health systems, Englewood, Colorado-based CHI operates in 19 states and comprises 105 hospitals, including four academic health centers and major teaching hospitals and 30 critical-access facilities; community health-services organizations; accredited nursing colleges; home-health agencies; and other facilities that span the inpatient and outpatient continuum of care. Paul is responsible for HR Information metrics, service level agreements, information standardization and researching/implementing leading information practices including predictive analytics, text analysis, and big data.
Previously, Paul held various leadership roles at The Coca-Cola Company, American Airlines, Ball Corporation and Accenture. Paul is licensed to practice law in Colorado, Georgia and Texas and obtained concurrent Juris Doctorate (J.D.) and Masters in Business Administration (M.B.A.) degrees from Southern Illinois University, magna cum laude.
Paul is a voting member of SHRM’s National HR information standards taskforces and has been an active member of the Colorado Bar Association’s Communications & Technology section. He is a first time presenter at SHRM’s Annual Conference & Exposition and has previously presented at the Project Management Institute’s Global Congress.