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

LBDW-diagram-platform

Many data warehouse architectures that have worked in industries such as retail or manufacturing force early data bindings to proprietary enterprise data models.   Unfortunately in healthcare, these have proven to be inflexible, one-size-fits-all architectures that force data from different systems into models enforcing a compromised, least common denominator warehouse. The Health Catalyst Late-Binding™ Architecture avoids the inherent weaknesses of early binding models.

The enterprise data model also requires massive transformations of data whereas late-binding retains its original, undiluted value by delaying data binding until the proper time and context in which it is is needed.

The following is are the principles supporting Catalyst’s approach to analytics. These principles have enabled data warehouses in manufacturing, the military, and healthcare that have been fully functional and adapting for over 20 years with an unmatched 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 analytic 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.
For a more detailed technical explanation of the Late-Binding™ Data Warehouse Approach Click Here »

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

The key components of our data warehouse platform are listed below or can be seen by browsing the right navigational bar

Metadata Engine

The Health Catalyst Metadata Engine powers the generation and automated loading of Source Marts into the Health Catalyst Platform.  Key components include Atlas, Auditing Dashboard, Data Security and Authorization, and the EDW Console.  You can browse in the right-hand navigation to explore details of any of these components.

Data Acquisition Engine

The Data Acquisition and Storage subsystem of the Health Catalyst Platform supports the extraction of data from source systems and stores those data in the Catalyst EDW for consumption by downstream applications.  Key components include source marts, source mart designer, source mart engine, and IDEA, our Instant Data Entry Application.  You can browse in the right-hand navigation bar to explore any of these components.

Data Bus Architecture

Catalyst’s Late-Binding™ Data Bus Architecture represents the core of our data warehousing approach for analytics in Healthcare.   As mentioned above, the Late-Binding™ approach provides the agility to meet healthcare’s demanding needs while avoiding the pitfalls of other early binding architectures.  Key components include master data management, content repositories, our subject area mart (SAM) engine and our SAM designer.  You can browse in the right-hand navigation bar to explore any of these components.

For more detailed technical explanation about the Late-Binding™ Architecture, click here.

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