It all starts with a data warehouse

An enterprise data warehouse (EDW) forms the foundation for healthcare analytics.  Most large healthcare organizations have hundreds of different technology solutions from various vendors.  Without bringing all of this data into a single source of organizational truth, it is impossible to provide reliable and repeatable reporting and analysis.  From the foundation of the EDW, organizations can progress to registries and reporting, population health, and clinical and financial risk modeling.

Health Catalyst’s Late-Binding™ Data Warehouse is our architectural model for analytics in healthcare. The Late-Binding™ architecture avoids the pitfalls of early binding architectures espoused by Inmon, Kimball, and others.

Here is a diagram representing the Health Catalyst 2.0 Platform and Application stack.  Below is a description of the key components of the Late-Binding ™ Data Warehouse platform

late-binding data platform

Catalyst’s Late-Binding™ Data Architecture is Health Catalyst’s architectural model for analytics in Healthcare. The Late-Binding™ Data Model avoids the pitfalls of early binding architectures espoused by Inmon, Kimball, and others.Previous warehouse architectures forced early data bindings to proprietary enterprise data models that have proven to be inflexible, one-size-fits-all architectures that force data from disparate systems into models enforcing a compromised, least common denominator warehouse. The Catalyst Late-Binding™ Architecture avoids the inherent limitations of early binding models.

Massive transformations of the data are also required to fit the enterprise data model. By delaying data binding until the proper time and context in which it is to be used, data retains its original, undiluted value.

Below is a summary of the principles that underlie Catalyst’s approach to analytics. These principles enabled data warehouses in the military, manufacturing, and healthcare that have been operating and adapting for over 20 years with an unparalleled track record for proven results.

    1. Minimize remodeling data in the data warehouse until the analytic use case requires it. Leverage the natural data models of the source systems by reflecting much of the same data modeling in the data warehouse.
    2. Delay binding to rules and vocabulary as long as possible until a clear use case requires it.
    3. Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to lock down for consistent analytics.
    4. Late binding in the visualization layer is appropriate for what-if scenario analysis.
    5. Retain a record of the changes to vocabulary and rule bindings in the data models of the data warehouse. This will provide a self-contained configuration control history that can be invaluable for conducting retrospective analysis that feeds forecasting and predictive analytics.
    6. For more details about the Late-Binding™ Architecture, click here.

Key Components of the Health Catalyst Late-Binding™ Data Warehouse Platform

The Health Catalyst™ Late-Binding™ Data Warehouse Platform is the core of our product portfolio and is deployed at every one of our customers’ organizations. This platform includes the following key components: (click on links for detailed descriptions)

Catalyst Analytics Platform

Data Acquisition and Storage

  • A subsystem supporting the optimized extraction, transformation, and loading (aka storage) of data from a number of source systems into Source Marts.
  • Source Marts which are created from a specific source systems, like an electronic medical record system (e.g., Epic EMR) which are then automatically extracted, minimally transformed, and loaded into its corresponding Source Marts of the Catalyst EDW.
  • Source Mart Designer which allows customers to rapidly design, develop, deploy and maintain Source Marts.
  • Instant Data Entry Application (IDEA), a tool for designing web-based data collection forms that allow users to enter data into the EDW for use in Subject Area Marts (SAMs) or other applications.

Advanced Analytics Data Marts

  • A subsystem of the Health Catalyst Platform that supports the creation of Subject Area Marts (SAM) which are a key component of an Advanced Application designed to measure specific metrics related to a specific cost or quality improvement process.
  • Content Repositories that organize and store common metric definitions, algorithms, predictive models, hierarchies, cohort rules, and knowledge assets
  • SAM (Subject Area Mart) Designer, a tool that simplifies the creation and modification of Subject Area Mart (SAM) definitions which feed Health Catalyst Applications

Read More About Data Warehousing in Healthcare

Why Do You Need a Data Warehouse?

New Gartner Report Covers Major Shift From EMRs to Data Warehousing and Analytics
Gartner

Why Your Healthcare Business Intelligence Strategy Can’t Win without a Data Warehouse
Paul Horstmeier, Senior Vice President

Using Clinical Data Repositories Versus a Data Warehouse: Which Do You Need?
Tim Campbell, Vice President Technology

6 Surprising Benefits of Healthcare Data Warehouses: Getting More Than You Expected
Mike Doyle, Vice President

5 Myths You Won’t Need to Worry About When Adopting a Clinical Data Warehouse
Mike Doyle, Vice President

Why Are Data Warehousing Needs Unique in Healthcare?

5 Reasons Healthcare Data Is Unique from Other Industries
Dan LeSueur, Vice President Technology

Why Knowing When to Bind Your Healthcare Data is So Critical in Healthcare
Steve Barlow, Senior Vice President Operations and Co-Founder

What Are the Different Approaches for Data Warehousing in Healthcare?

The Late-Binding Data Warehouse Explained (white paper)
Dale Sanders, Senior Vice President, Strategy

What Is the Best Healthcare Data Warehouse Model? Comparing Enterprise Data Models, Independent Data Marts, and Late-Binding Solutions
Steve Barlow, Senior Vice President and Co-Founder

Comparing Star Schema vs. Late-Binding Approaches in Healthcare Data Warehousing
Steve Barlow, Senior Vice President and Co-Founder

Comparing EMR-Based Models vs. Late Binding Approaches in Healthcare Data Warehousing 
Eric Just, Vice President, Technology

Build vs. Buy a Healthcare Data Warehouse: An Honest Comparison of the Options
Mike Doyle, Vice President

Late-Binding Data Warehousing: An Update on the Fastest Growing Trend in Healthcare Analytics
a webinar by Dale Sanders, Senior Vice President (including slides and transcripts)

A Health Catalyst Overview: Building a Data Warehousing and Analytics Strategy (a webinar)
Eric Just (Vice President, Technology), and Mike Doyle (Vice President)

Key Considerations Once You Select a Data Warehouse Approach

6 Reasons Why Healthcare Data Warehouses Fail
Steve Barlow, Senior Vice President and Co-Founder

I Already Have a Data Warehouse. Can I Use Health Catalyst Applications With It?
Health Catalyst

Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse
Doug Adamson, Chief Technology Officer

EDW Cloud Hosting: Is It Right for Your Health System?
Nate Arnold, Director, Infrastructure Systems

What Does a Data Warehouse Cost? How to Get a Return on Your Investment
Dan Burton, Chief Executive Officer

 

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