Wrong conclusions in your analytics can cause waste and disillusionment, not to mention suboptimal outcomes that may take months or even years to recover from. But analytic analysis isn’t about perfection—it’s about getting to the right answer by quickly getting to the wrong one.
In this interactive webinar, Jason Jones, chief data scientist at Health Catalyst, walks through scenarios that illustrate how commonly used analytic methods can lead analysts and leaders to the wrong conclusions, and shares how to course correct if this happens to you. In health and healthcare, leaders drive change by understanding and supporting better approaches, and analytics provide the best foundation for informed change management. Let’s work together to shift towards a better use of AI in healthcare.
View this webinar to learn:
- How analysis of the same data set can result in different conclusions.
- Tools and techniques to get your organization back on track after a misstep.
- Lessons from two case studies that will help you drive better analytics in your own organization.
Getting to the Wrong Answer Faster with Your Analytics: Shifting to a Better Use of AI in Healthcare
1. Getting to the Wrong Answer Faster
with Your Analytics: Shifting to a Better
Use of AI in Healthcare
August 14, 2019
Jason Jones, PhD
Chief Data Scientist, Health Catalyst
2. Learning
Objectives
• Describe how the same data can
result in different conclusions.
• Identify tools and techniques to put
your organization back on track.
• Describe two cases to drive better
analytics.