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Author Bio

Brian Eliason, MIS

Brian Eliason brings more than 10 years of Healthcare IT experience to Health Catalyst, specializing in data warehousing and data architecture. His work has been presented at HDWA and AMIA. Prior to coming to Health Catalyst, Mr. Eliason was the technical lead at The Children's Hospital at Denver with experience using I2B2. Previously, he was a senior data architect for Intermountain Healthcare, working closely with the disease management and care management groups. Additionally, he helped Intermountain bridge clinical programs with the payer-arm, Select Health. Mr. Eliason holds an MS in business information systems from Utah State University and a BS from Utah Valley University.

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Amanda Rich
Brian Eliason, MIS

Against the Odds: How this Small Community Hospital Used Six Strategies to Succeed in Value-Based Care

The constant thread weaving through every healthcare organizational strategy should be adherence to the Triple Aim. But with uncertainty generated by the changes at the federal level, healthcare organizations may be tempted to put their value-based care plans on hold. This article explains why that’s not necessary and lists six strategies for thriving under a fee-for-value model:
1.) Use Leadership and Team Structure to Support Improvement
2.) Drive Down Costs
3.) Reduce Unnecessary Waste
4.) Encourage the Learning Organization
5.) Prioritize Patient Education
6.) Track Data and Outcomes
This blog cites one small medical center with odds stacked against it, and how it is managing to not only weather the changes, but also distinguish itself by staying true to the values of the Triple Aim.

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Brian Eliason, MIS

9 Ways Your Outcomes Improvement Program Could Be Delayed

A health system’s outcomes improvement program is an expensive undertaking. It’s worth the results, but there’s no need to make it even more expensive through unforeseen and unnecessary delays. We outline the three phases of managing outcomes improvement programs, from hardware and software acquisition and configuration to resource management to sustaining and scaling the gains. We also examine the nine potential pitfalls that can undermine success in each of these phases:

Hardware and software acquisition delays
Environment readiness
Source system access
Lack of resource capacity
Lack of analytic and technical skills
Data quality paralysis
Lack of clinical or operational engagement
Punitive culture: data used as a weapon
No CEO, no go

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Brian Eliason, MIS
Neil Andersen

Why the Data Steward’s Role Is Critical to Sustained Outcomes Improvement in Healthcare

The data steward is critical to sustained outcomes improvement, yet they tend to be underappreciated members of the healthcare analytics family. Combining the invaluable technical expertise of a data analyst with the vital clinical knowledge of an experienced caregiver, the data steward’s skills and proficiency at both positions brings value beyond measure to any outcomes improvement project. Unfortunately, all too often, their role is non-existent even though potential candidates for the job are located in multiple data sources throughout the organization. Among other responsibilities, the data steward:

Reinforces the global data governance principles.
Helps develop and refine details of local data governance practices.
Is the eyes and ears of the organization with respect to data governance and the governance committee.
Provides direction to peers regarding appropriate data definitions, usage, and access.
Anticipates local consequences of global changes

For innovative health system leaders who have specifically recognized this emerging role, the ROI of data stewards who help achieve improved outcomes is very worthwhile.

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Brian Eliason, MIS
Roopa Foulger

A Guide to Governing Healthcare Claims Data Successfully: Lessons from OSF HealthCare

OSF HealthCare has committed that 75 percent of its primary care patient will be part of a value-based program by 2020. The organization’s leaders knew that success depended on how well they managed their data and decided to build a data warehouse in-house. They recognized that beneficiary claims data was essential to understanding their population better. To get that claims data, however, was no easy task. This required patient matching through master data management and getting buy-in from leaders and physicians throughout the health system. Then, they prioritize where to start efforts using the 80/20 rule and using that as a guide, loaded the claims data.

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Brian Eliason, MIS
Roopa Foulger

Outsourced vs. In-house Healthcare Analytics: Pros and Cons

Healthcare analytics are essential for organizations to thrive in the new healthcare environment. Using analytics, systems can evaluate efficiency, effectiveness, and find improvement opportunities. There are two principal approaches: outsourcing the analytics function to benchmarking companies and providers of software-as-a-service; and doing analytics in-house with a system’s own data warehouse. The pros of outsourcing include gaining benchmarking access to how health system peers are performing. The cons to outsourcing include focusing too much high-level outcomes with no insight in how to effect change. The pros of in-house analytics include having quick access to fine-grained details of the data and being able to include clinicians in the implementation and development of the analytics process. A con is that in-house analytics can require significant resources – an investment in the right personnel and right technology.
 

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Brian Eliason, MIS
Kristi Mousel

Self-Service Hospital Reporting Possibilities: Enabling Clinicians to Make Faster and More Informed Decisions

Self-service capabilities are growing in our society and it’s starting to make its way into hospital reporting. Traditionally, analytics and reports have fallen under the purview of the IT department. However, this approach takes more time and is ineffective when trying to make care improvements. With self-service analytics tools, clinicians and other users can access and analyze data on their own, leaving IT to do the more complex analytical tasks and function at the top of their education.

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Brian Eliason, MIS
Jason Burke
Pete Hess

Master Data Management in Healthcare: 3 Approaches

Master data management is key for healthcare organizations looks to integrate different systems. The two types of master data are identity data and reference data. Master data management is the process of linking identity data and reference data. MDM is important for mergers and acquisitions and health information exchanges. The three approaches for MDM are: IT system consolidation, Upstream MDM implementation, and Downstream master data reconciliation in an enterprise data warehouse.

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Brian Eliason, MIS
David Crockett

What Is Data Mining in Healthcare?

This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the best practice system (and systematically applying evidence-based best practices to care delivery), and the adoption system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.

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