Databases Reference
In-Depth Information
Overview: giving practitioners new insights to guide patient care
An integrated nonprofit medical provider in the midwest, Aurora Health Care serves communities
throughout eastern Wisconsin and northern Illinois, meeting the needs of 1.2 million people and coor-
dinating the operations of more than 30,000 caregivers. Given strict regulatory compliance issues,
Aurora deals with significant responsibilities to keep patient data private. But there's an enormous
incentive to use its data more effectively to improve the health and well-being of its patients.
One way to improve patient health outcomes is to identify the top treatment patterns that lead to
better outcomes, and put those insights into the hands of medical professionals to make better real-
time decisions about the treatments they recommend to their patients. This became a priority for
Aurora Health Care as the organization began a major initiative to evaluate the critical technologies
that could meet this objective quickly and more efficiently. However, as one of the nation's largest
healthcare organizations, Aurora deals with huge amounts of data from multiple sources and varying
formats—it has 18 data feeds, ranging from financial information and procedures, to pharmacy, to
laboratory services, and many other operational areas.
Challenges: blending traditional data warehouse ecosystems with Big Data
Aurora's challenges include:
●
Multiple sources of siloed data that need to be synchronized.
●
Lack of an analytics and discovery platform that could scale and generate the insights needed.
●
Gigabytes of clickstream data stored in flat files, resulting in extremely slow queries.
Dave Brown, senior director of enterprise business intelligence, explained:
The challenges Big Data brings to our existing data integration and analytics platforms are
…
huge. The shared business imperative is to reduce IT expenses, while simultaneously maintaining
that the BI service quality is ever-present. However, the “new” business value of analyzing Big
Data must be pursued to remain competitive.
Most Big Data implementations are external, or run alongside existing EDW platforms. Because
this can add more people, more processes, and more cost, Aurora decided to adopt a new model that
blends the technologies of a traditional EDW ecosystem with that of a Big Data discovery platform.
One initiative that Aurora began was to enhance the functionality of its nearly $900 million sup-
ply chain. With new technology in place, Aurora will manage supplies from the purchase order all the
way through to the inventory process, and then follow up to monitor actual utilization within a proce-
dure to give doctors a peer-to-peer comparison on outcomes and cost savings.
Solution: getting ready for Big Data analytics
To meet its goals, Aurora embarked on a strategic and comprehensive program to recreate a com-
pletely new enterprise business intelligence platform from scratch. This took the form of a hybrid
business intelligence ecosystem that combines a RDBMS for traditional business intelligence report-
ing with a Big Data discovery platform for next-generation Big Data analytics. The ecosystem
includes a message-centric ETL methodology, and leverages an RDBMS to perform all dimension
and fact table processing. This, in turn, integrates with a big analytics and discovery platform that
