Learn more about Drew Cardon

Author Bio

Drew Cardon joined Health Catalyst in November 2011 as a data architect. Prior to this, he worked for nine years in the state tax and revenue industry as a project manager and implementation consultant with Accenture, and later with Fast Enterprises. He was involved in the installation of large information technology systems for the State Tax Commissions in Arizona, Utah, and Oklahoma. He holds a Bachelor’s degree in business from Brigham Young University and an MBA from the University of Notre Dame.

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Drew Cardon

From Installed to Stalled: Why Sustaining Outcomes Improvement Requires More than Technology

The big first step toward building an outcomes improvement program is installing the analytics platform. But it’s certainly not the only step. Sustaining healthcare outcomes improvement is a triathlon, and the three legs are:

Installing an analytics platform
Gaining adoption
Implementing best practices

The program requires buy-in, enthusiasm, even evangelizing of analytics and its tools throughout the organization. It also requires that learnings from analysis translate into best practices, otherwise the program fails to produce results and will eventually fade away. Equally important is that top-level leadership across the organization, not just IT, supports and promotes the program ongoing. We explore each of the elements and how they come together to create successful and sustainable outcomes improvement that defines leading healthcare organizations.

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Drew Cardon

Healthcare Databases: Purpose, Strengths, Weaknesses

Most healthcare data is now electronic and with that comes electronic healthcare databases. A healthcare database replaces the old paper documents, file folders, and filing cabinets. They take the form of EHRs, practice management systems, costing systems, patient satisfaction programs, and more. The benefits of databases are equal to the benefits that run on them. They can be stored externally and backed up in a secure place. However, healthcare databases have two big problems: There is an overwhelming amount of raw data with little ability to gain targeted, actionable knowledge from that data. And databases are siloed preventing the across-the-organization insight that is needed in today’s world of value-based care. The answer is a data warehouse, which sits on top of all the other databases providing sophisticated analysis.

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Drew Cardon

Database vs Data Warehouse: A Comparative Review

What are the differences between a database and a data warehouse?
A database is any collection of data organized for storage, accessibility, and retrieval.
A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use.
The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.

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