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Author Bio

Michael McCuistion

Michael McCuistion joined Health Catalyst in December 2012 as a Technical Director. Prior to coming to Health Catalyst, he worked for PeaceHealth (a 9 hospital Catholic Healthcare System in the Pacific Northwest) for 17 years. At PeaceHealth, Michael served as the manager of an Enterprise Data Warehouse and Business Intelligence team for 9 years, the manager of the system-wide Help Desk for 6 years and as a desktop support person for 2 years. Michael has an undergraduate degree from Oregon State University in History and a graduate degree from Oregon State University in Business (MBA).

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Chris Rains
Michael McCuistion

Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for Quality Improvement Initiatives

Business intelligence may not sound like something that belongs in a healthcare setting. After all, what role can it possibly play in medical excellence and compassionate care? But federal mandates that require cost and care improvement and reporting on those improvement metrics, are driving the need for business intelligence tools. For healthcare, this means an enterprise data warehouse with the processing power and architecture to handle the vast volumes of data, analytics applications that will effectively unlock the data, and data visualization tools to easily illustrate areas of opportunity.

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Chris Rains
Michael McCuistion
Steve Barlow

BI Tools: 5 Reasons Why They Can’t Replace Your Healthcare Data Warehouse

An EDW is the only viable solution for driving healthcare analytics. This fact has resulted in many BI tools and visualization solutions being marketed as cloud data warehouses, promising quick, user-friendly answers. While they do a great job of visualizing data and exposing it to end users, these tools cannot replace an EDW for 5 reasons in particular:
i. BI tools don’t optimize healthcare data- optimizing data and exposing data-quality issues represents a significant chunk of the initial stages of an EDW project. BI tools just can’t offer this functionality.
ii. BI tools can’t handle large amounts of healthcare data- one patient encounter can general hundreds of rows of data, meaning that reports from BI tools will be slow to generate and inefficient.
iii. BI tools don’t work well with healthcare data at different levels of granularity- Some tools have difficulty displaying the one-to-many and many-to-many data relationships required in healthcare.
iv. BI tools can’t optimize healthcare data for multiple user types- Applying logic against the data so it is understandable at multiple levels for different audiences is something BI tools simply cannot do.
v. BI tools don’t provide for modularity, understandability, and code reuse

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Chris Rains
Michael McCuistion

How Healthcare Business Intelligence Drives Smarter Decisions

Business Intelligence is a loosely defined, but commonly used, term that means various things to different people. It seems to have become a catch-all phrase for three classes of technology:
1. Enterprise data warehouse (EDW) systems used to aggregate and standardize data across an organization
2. Reporting tools that visualize data (visualization tools), typically representing a snapshot of information captured at a particular point in time
3. Discovery tools that allow users to proactively drill down and through data sets, asking questions and uncovering information in real time about the performance of their organization

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