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
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.
- 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.
- Delay binding to rules and vocabulary as long as possible until a clear use case requires it.
- Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to lock down for consistent analytics.
- Late binding in the visualization layer is appropriate for what-if scenario analysis.
- 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 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:
Catalyst Analytics Platform
- Atlas metadata management system
- EDW Console management and monitoring applications
- A data security and authorization framework and associated tools
- Patient identity and reference data management tools
- A Late-Binding™ engine for creating data marts scoped to a particular analytic need
- Visual frameworks that support meta-data selection, configuration and sharing between multiple visualization applications.
Data Acquisition and Storage
- A data storage and indexing engine based on Microsoft SQL Server
- A data acquisition framework and library of vendor-specific adapters
- An application designer and runtime engine that enables the creation of simple data entry applications that populate data warehouse tables (IDEA)
Advanced Analytics Data Marts
- An application designer and runtime that enables the creation of subject area marts scoped for specific analytic purposes.
- Content Repositories