Hadoop: The Definitive Guide, 4th Edition

Chapter 22. Composable Data at Cerner

Ryan Brush

Micah Whitacre

Healthcare information technology is often a story of automating existing processes. This is changing. Demands to improve care quality and control its costs are growing, creating a need for better systems to support those goals. Here we look at how Cerner is using the Hadoop ecosystem to make sense of healthcare and—building on that knowledge—to help solve such problems.

From CPUs to Semantic Integration

Cerner has long been focused on applying technology to healthcare, with much of our history emphasizing electronic medical records. However, new problems required a broader approach, which led us to look into Hadoop.

In 2009, we needed to create better search indexes of medical records. This led to processing needs not easily solved with other architectures. The search indexes required expensive processing of clinical documentation: extracting terms from the documentation and resolving their relationships with other terms. For instance, if a user typed “heart disease,” we wanted documents discussing a myocardial infarction to be returned. This processing was quite expensive—it can take several seconds of CPU time for larger documents—and we wanted to apply it to many millions of documents. In short, we needed to throw a lot of CPUs at the problem, and be cost effective in the process.

Among other options, we considered a staged event-driven architecture (SEDA) approach to ingest documents at scale. But Hadoop stood out for ...