Dan LeSueur

Senior Vice President of Professional Services

Dan has been developing and implementing the core products and services of Health Catalyst since February of 2011. He started as a data architect, moved into a technical director role and is now a Senior Vice President of Professional Services. Prior to joining Health Catalyst, Dan owned and operated a management consultancy for five years that assisted ambulatory practices in the implementation of electronic health records and data-driven management methodologies. In this venture he served as data architect, business-intelligence developer, and strategic advisor to physicians and practice owners in the strategic management and growth of their practices. Dan holds Master’s degrees in Business Administration and Health-Sector Management from Arizona State University and a Bachelor of Arts degree in Economics from Brigham Young University.

See content from Dan LeSueur

How to Run Your Healthcare Analytics Operation Like a Business

A robust data analytics operation is necessary for healthcare systems’ survival. Just like any business, the analytics enterprise needs to be well managed using the principles of successful business operations.

This article walks through how to run an analytics operation like a business using the following five-question framework:

1. Who does the analytics team serve and what are those customers trying to do?
2. What services does the analytics team provide to help customers accomplish their goals?
3. How does the analytics team know they’re doing a great job and how do they communicate that effectively to the leadership team?
4. What is the most efficient way to provide analytics services?
5. What is the most effective way to organize?

5 Reasons Healthcare Data Is Unique and Difficult to Measure

Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:

1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day.
4. The data is complex.
5. Changing regulatory requirements.

The answer for this unpredictability and complexity is the agility of a Late-Binding™ Data Warehouse.