About the Session

The U.S. healthcare industry faces several implementation challenges in the transition from fee-for-service to value-based care. Achieving a quality-driven healthcare system where payments are based on performance has increased the importance of population health management. Data-driven approaches have also become essential in reaching population-wide quality goals. Given the complexity of data sources and multitude of data elements available, it becomes important to filter analytical outputs in a meaningful and customized way. This session will describe the key characteristics of clinical analytical platforms and address the analytics and population health management needs of provider groups, employers and health plans. The presenters will describe current population health solutions supported by analytical platforms, including best practices in patient engagement and development of community support and employer solutions. You will hear about the potential benefits of machine learning for care quality improvement and review several case studies of successful population health interventions.

Learning Objectives:

  • Describe the opportunities and challenges involved with leveraging clinical data for population health interventions.
  • Apply concepts from proven population health programs to other health systems and understand the cost-of-care implications of implementing such programs.