Hadoop Makes a Big Data Splash: Page 2

The rapid growth of Big Data adoption is driving corresponding growth in Hadoop.
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Phase one concluded in early 2013. Project coordinators found their predictive capabilities were 65 percent accurate at predicting suicide risk among a veteran control group. Phase two launched in July 2013. They are attempting to eventually get 100,000 veterans to opt in to the study. Participants who opt in receive a unique Facebook app and a mobile app designed to capture posts, Tweets, mobile uploads, and even location. Additional profile data is captured as well, including physician information and clinical notes.

Eventually, the project hopes to save lives by enabling professionals to intervene before a suicide takes place. Still in its initial phases, though, the Durkheim Project is authorized only to monitor and analyze data. While the project has delivered statistically valid results that accurately predict suicide risk in a control group of veterans, its critical research is restricted, at least for the time being, to a non-interventional protocol.

UC Irvine Health improves clinical operations and scientific research with Hadoop

The Clinical Informatics Group (CIG) at UC Irvine Health (UCIH) was founded in 2009 to provide high- quality data to support the work done by researchers and clinicians at UC Irvine. However, as with many organizations, much of UCIH’s data was scattered across multiple Excel spreadsheets. UCIH also had 9 million semi-structured records for 1.2 million patients over 22 years, none of which was searchable or retrievable. These semi-structured records included dictated radiology reports, pathology reports, and rounding notes – all very valuable, in aggregate. But it was not accessible in the aggregate.

The CIG first migrated data to an enterprise data warehouse with integrated clinical business intelligence tools. Then, they migrated again to their current Big Data architecture, which is built on Hortonworks Data Platform (HDP).

The single Hadoop “data lake” at UCIH serves two different constituents: The UC Irvine School of Medicine for medical research and the UC Irvine Medical Center (UCIMC) for the quality of its clinical practice. The medical school and the hospital have distinct Big Data use cases, but they are both able to use a unified data platform with HDP at its core.

“Hadoop is the only technology that allows healthcare to store data in its native form. If Hadoop didn’t exist we would still have to make decisions about what can come into our data warehouse or the electronic medical record (and what cannot). Now, we can bring everything into Hadoop, regardless of data format or speed of ingest. If I find a new data source, I can start storing it the day that I learn about it. We leave no data behind,” said Charles Boicey, who was previously an informatics solutions architect with UCIMC. (Boicey recently accepted a new position with Stony Brook Medical.)

“Now back to those 9 million semi-structured legacy records. They are now searchable and retrievable in the Hadoop Distributed File System. This allowed the UCIH team to turn off their legacy system that was used for view only, saving them more than $500,000,” Boicey added.

The CIG has already launched two new data-driven programs, one a pilot program that allows nurses to remotely monitor patient vitals in real-time and another that seeks to reduce patient re-admittance.

One of UCIH’s top goals is to predict the likelihood of hospital re-admittance within 30 days after discharge. Patients with congestive heart failure have a tendency to build up fluid, which causes them to gain weight. Rapid weight gain over a 1-2 day period is a sign that something is wrong and that the patient should see a doctor.

UCIH developed a program that sends those heart patients home with a scale and instructions to weigh themselves once daily. The weight data is wirelessly transmitted to Hadoop where an algorithm determines which weight changes indicate risk of re-admittance. The system notifies clinicians about only those cases. All home monitoring data will be viewable in the EMR via an API to Hadoop.

Photo courtesy of Shutterstock.

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Tags: Hadoop, big data, Data Analytics

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