Saving lives with big data analytics that predict patient outcomes

Cerner’s Enterprise Data Hub allows data to be brought together from an almost unlimited number of sources, and that data can be used to build a far more complete picture of any patient, condition or trend.

Insights derived from data can help healthcare providers understand health outcomes not just for individuals but for entire groups of individuals or populations. They can identify and predict high risk segments within a population and help take preventive action, creating long term benefits for patients, hospitals, governments and society at large.

To unlock the true potential of data for population health, data from a range of disparate sources, including clinics, hospitals, pharmacies, fitness centres and even homes and employment places, would have to be brought together and analysed. However, traditional healthcare IT solutions tended to be limited in scope and restricted to a particular source of data

This was the challenge being faced by Cerner Corporation (Cerner), a leader in the healthcare IT space, whose solutions are used in over 35 countries at more than 27,000 provider facilities, such as hospitals, integrated delivery networks, ambulatory offices, and physicians’ offices.

Cerner was expanding its historical focus on electronic medical records (EMR) to help improve health and care across the board. To do so, it aimed to assimilate and normalise the world’s healthcare data in order to reduce cost and increase efficiency of delivering healthcare, while improving patient outcomes.

Mr David Edwards, Vice President and Fellow at Cerner explained, “Our vision is to bring all of this information into a common platform and then make sense of it — and it turns out, this is actually a very challenging problem.”

The firm accomplished this by building a comprehensive view of population health on a Big Data platform that’s powered by a Cloudera enterprise data hub (EDH). Management tooling, scalability, performance, price, security, partner integration, training, and support options were key criteria for the selection of a partner.

Today, the EDH contains more than two petabytes (PB) of data in a multi-tenant environment, supporting several hundred clients. It brings together data from an almost unlimited number of sources, and that data can be used to build a far more complete picture of any patient, condition, or trend. The end-result is better use of health resources.

The platform ingests multiple different Electronic Medical Records (EMRs), Health Level Seven International (HL7[1]) feeds, Health Information Exchange information, claims data, and custom extracts from a variety of proprietary or client-owned data sources,

It uses Apache Kafka, a high-throughput, low-latency open-source software platform to ingest real-time data streams. The data is then pushed back to the appropriate data storage, HDFS (Hadoop Distributed File System) cluster or HBase (a noSQL database which enables random, real-time read/write access to data).

A blog post by Micah Whitacre, a senior software architect on Cerner Corp.’s Big Data Platforms team, explains how Apache Kafka helped Cerner overcome challenges related to scalability for the near real-time streaming system and in streamlining data ingestion from multiple sources, including ones outside Cerner’s data centres.