Jason Jones, PhD

Chief Data Scientist, Health Catalyst

Jason Jones is passionate about achieving the Quadruple Aim through better and easier use of data in healthcare, including helping organizations to find analytic focus; helping providers feel that the systems they work for have their backs, and helping people to understand and have their goals and preferences respected for the hopefully brief periods during which they are “patients.”

Previously, Jones served as Vice President, Information Support for Care Transformation, at Kaiser Permanente (KP). In that capacity, he brought together and co-led the national Hospital and Healthplan Quality and Finance analytic functions and led development of national quality strategy and care delivery IT investments.  Prior to that, he was KP’s Executive Director of Clinical Intelligence and Decision Support and a Research Scientist in KP’s Southern California region.

Before joining KP, Jones was a Senior Medical Informaticist for Intermountain Healthcare. He also held analytic and marketing positions at Bayer Healthcare in Wayne, N.J., and Ingenix (now Optum) Pharmaceutical Information Products in Salt Lake City, where he developed a model for converting United Healthcare data into a saleable asset for external customers conducting outcomes research.

Throughout his career, Jones has taught graduate courses in statistics to medical informaticists at the University of Southern California and at the University of Utah. He has published dozens of peer-reviewed papers in medicine, predictive modeling, and outcomes improvement.

Speaker Sessions

27. Introducing the Data Science Adoption Model™: Realizing the Value of Your Investment

(Analytics, Innovative Data and Analytics Transformation, Machine Learning/AI — Course Level: Intermediate)

COVID-19 compelled organizations to quickly advance the use of data science, swiftly embracing predictive models to improve the effectiveness of their response plans. Analytics maturity models are widely used to illustrate the stages a company travels through to reach the next level of analytics maturity. Some critics believe the linear nature of the analytics maturity models may inadvertently limit the effectiveness of data science. In this presentation, Jason Jones, Chief Data Scientist at Health Catalyst, will discuss the Data Science Adoption Model and will share how the model can be used to help data science practitioners and leaders direct investments and deliver real value.

22. Capacity Planning and Leadership Response: A COVID-19 Silver Lining

(Analytics, Innovative Data and Analytics Transformation, Machine Learning/AI — Course Level: Intermediate)

The COVID-19 pandemic demonstrated how quickly the healthcare industry can move when it needs to and has aligned leadership. One area where leaders and organizations have made rapid progress is capacity planning—including beds, mechanical ventilators, personal protective equipment, and staffing—even across traditional organizational boundaries. During this session, you will discover:

  • How to identify different types of decision windows.
  • Review leadership tools for forecasting and alerting in the context of what we’ve learned so far.
  • Evaluate tool options for short term outlier detection.
  • Applications of what we may encounter this Fall, Winter, and beyond.

4. Machine Learning, Social Determinants, and Data Selection for Population Health

(Analytics, Innovative Data and Analytics Transformation, Machine Learning/AI, Population Health — Course Level: Intermediate)

Ninety percent of the $3.3T spent in the U.S. annually for healthcare is for people with chronic and mental health conditions. It can be overwhelming to determine which data sources are best for segmenting the population to identify the patients that could benefit the most from population health interventions—critical for ensuring health during a pandemic. Are EMR data components, social determinants of health, claims data, clinical data, or other data the most relevant? This session will describe the use of various data sets to drive a machine learning platform, identify the relative contributions of the data, and discuss which sources are the most important for accurate predictive modeling.

Thank You for a Great Virtual Summit! See You Next Year for HAS 21.

Replay HAS 20 Virtual

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