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    Linking and standardization

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    Just-in-Time Binding Gives Healthcare Providers Most Meaningful, Up-to-Date Data the Moment They Need It.

    Traditional data warehouses try to model the perfect database from the outset, determining in advance every possible business rule and vocabulary set needed to bring together data for analysis.

    This practice, called early binding, is a time-consuming, expensive undertaking. In healthcare, business rules and vocabularies change rapidly – and so do the use the cases that data linked across different source systems can serve.

    The Wisdom of Late Binding

    Health Catalyst’s Late-Binding™ Data Warehouse architecture avoids the consequences of linking data with volatile business rules or vocabularies too early. By waiting to bind data until it’s time to solve an actual clinical or business problem, analysts:

    • Don’t have to make lasting decisions about a data model up front when they can’t see what’s coming down the road in two, three, or five years
    • Quickly adapt to new questions and use cases
    • Have the data they need to perform timely, relevant advanced analytics

    EtL with a Lowercase t

    When data is extracted from source systems and loaded into the EDW, it undergoes almost no transformation. Aside from some minimal data conformance (for example, making sure the “patient name” field in one Source Mart is structured the same as the “patient name” field in another), the data is kept as raw as possible in a source system’s natural data models.

    Minimal transformation also helps analysts easily track and catalog data lineage through a single enterprise metadata dictionary.

    Just-in-Time Answers

    When it’s time to analyze a specific use case, source mart data is bound more tightly in a Subject Area Mart. At this point, we perform some transformation of the data, but only when necessary.

    The Six Binding Points

    Data in Health Catalyst’s Late-Binding™ Data Warehouse can be bound at six points:

    Six Points to Bind Data



    Binding Defined

    Binding is the process of mapping the data in the data warehouse from source systems to standardized vocabularies (e.g., SNOMED and RxNorm) and business rules (e.g., length-of-stay definitions, ADT rules) so it can be used together for analysis.

    Health Catalyst’s late-binding architecture avoids wasted time and effort by waiting to bind data until a business case drives it.