Cessily Johnson

Vice President of Terminology & Master Data Management

Cessily Johnson joined Health Catalyst in January 2013 as Director of Terminology Services. She started working in healthcare during college and graduated with a Bachelor’s degree in Medical Laboratory Science from the University of Utah. She then worked in the Lab for Intermountain Healthcare while earning an MBA with an emphasis in IT. During her MBA program she moved to the Intermountain IT department working in Clinical Modeling and Terminology, becoming the team lead and then the manager of that department. She then moved on to work for Lantana Consulting Group doing terminology consulting for vendors and healthcare provider organizations before joining Health Catalyst. The focus of her career has been application of Standard Terminology in Healthcare Information Systems.

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Find the Right Term for Your Goals: How to Choose Healthcare Terminology Standards

With an overwhelming number of healthcare terminology standards, how do industry professionals determine which ones they need to know? Terminology users can start by matching their purpose with the correct standard. Because different healthcare terminology standards fulfill distinct purposes, matching purpose to standard generally leads users to the right term for their goals. 

Terminology users can match their purpose with the correct standard by first identifying the standard’s purpose. Purposes encompass billing, clinical, laboratory, and pharmacy terminology standards:

1. Healthcare billing terminology.
2. Clinical terminology.
3. Clinical and laboratory terminology.
4. Pharmacy terminology.

Self-Service Data Tools Unlock Healthcare’s Most Valuable Asset

Data is increasingly critical to the delivery of healthcare. However, due to its complexity and scope, frontline clinicians and other end users can’t always access the data they need when they need it. In addition, expectations for data at the point of care unduly burden data analysts, keeping them from advancing more sophisticated organizational analytics goals.

In response to data productivity and efficiency challenges, self-service data solutions models only the high-value data, versus all available data, giving analysts and nontechnical users immediate and direct access to the data. These reusable models address three key challenges healthcare analytics programs face:

1. Cost—avoid additional expense and labor of producing single-use models.
2. Efficiency—save times associated with routinely producing new models.
3. Maintenance—allow updates across the organization’s models, versus separate updates.