Use Cases

Any software application or framework that can connect to Amazon Redshift can connect to our Bulk Data API and interact with our large, enriched healthcare data sets. Since the portfolio of compatible tools and frameworks is very large, it is critical to carefully consider each intended use case and select an application that is well-suited to the data requirements of the use case.

Here are a few examples of common applications organized by use case:

Analytics: Build Interactive Dashboards and Visualizations

Tableau, Microsoft Power BI, ArcGIS, and Knime are examples of industry-standard applications that support native connections to Amazon Redshift and provide powerful dashboarding and visualization capabilities. These tools are well-suited for interactive reporting use cases with well defined data specifications. CareEvolution has a library of “getting started” reports to illustrate how to use these tools with our Bulk Data API in order to understand populations or study results, trend key performance metrics, and describe opportunities to drive these metrics prospectively.

In order to build performant interactive visualizations, dashboarding applications generally require a simplified, denormalized data structure. The data model that underlies our Bulk Data API has been developed to provide these applications with enriched data that has been transformed into readily-usable tables that minimize complex joins and unions.

In addition to these third party tools, CareEvolution’s Galileo is a web-based dashboarding tool that is tightly integrated with other CareEvolution applications and has built-in support for many population health workflows.

Ad-hoc Query: Explore Enriched Data Sets

If you wish to explore data with open ended queries to pursue novel questions of interest, consider a flexible query application such as DBeaver or Aginity. These applications permit any analysis that can be composed in SQL.

Visual query editors such as DataRow offer a simplified user interface to assemble SQL-based queries that may be ideal for users without a SQL background.

Redshift is designed to optimize the performance of complex queries on large datasets. Several constructs within our data model are purpose-built for query-oriented use cases.

Data Science: Analyze Data and Develop Models

Support for statistical analysis or predictive modeling is available from many different data science tools and frameworks. Jupyter notebooks using Python data science libraries are a common toolset for these use cases. AWS Sagemaker is Amazon’s managed data science framework that has deep Redshift integration and advanced tooling for machine learning.

Application Development or Data Warehouse

If you wish to develop a custom application or acquire our enriched data into your own data warehouse, you may wish to consider a programming framework that supports fast access to Redshift such as Java, C# or Python.