Jason Jones, Chief Data Scientist, Health Catalyst
Summary of Presentation
Technology advances in AI have allowed us to focus on the change management aspects. We develop AI to help clients achieve the Quadruple Aim and though not all change is an improvement, all improvement requires change. How can we support leaders lead through change? Three critical questions need to be answered in AI deployment: functional, contextual, and operational. Does the model make sense? How does it fit into the workflow? How do we balance potential benefit with risks or resource constraints?
Health Catalyst has built a standard process and tools to address these questions. Health Catalyst showed a hypothetical example for deciding which patients to reach out to for preventing Coronary Heart Disease (CHD). The aims were to identify half the people who would develop CHD (sensitivity) and ensure half the people who received intervention would otherwise have developed CHD (positive predictive value). The model was not able to achieve both aims, but the tool was able to create low (do nothing), medium (wait another year), and high (intervene now) to achieve acceptable balance.
Coupling AI with quantitative change management tools can enhance leadership clarity, expectations, and results.
About The Presenter
Jason Jones is Chief Data Scientist at Health Catalyst–an organization that delivers software, data, and services to improve Quadruple Aim outcomes. Prior to Health Catalyst, Jason served at Kaiser Permanente and Intermountain Healthcare in various leadership positions related to quality, operations, research, and informatics. Jason received his PhD from the University of Southern California in biostatistics.