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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

数据对于提供医疗保健越来越重要。然而,由于其复杂性和范围,一线临床医生和其他终端用户不能总是在他们需要的时候访问他们需要的数据。此外,在护理点对数据的期望会给数据分析师带来过多的负担,使他们无法推进更复杂的组织分析目标。

为了应对数据生产力和效率方面的挑战,自助服务数据解决方案只对高价值数据建模,而不是对所有可用数据建模,使分析师和非技术用户能够立即直接访问数据。2022卡塔尔世界杯赛程表时间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.

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