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6 Big Data analytics use cases for healthcare IT

Brian Eastwood | April 24, 2013
Making use of the petabytes of patient data that healthcare organizations possess requires extracting it from legacy systems, normalizing it and then building applications that can make sense of it. That's a tall order, but the facilities that pull it off can learn a lot.

All data is available to the VA's 3,300 researchers and its hospital academic affiliates. The idea, he says, is to embed the clinical trial within VistA, the VA EHR system, with the data then used to augment clinical decision support.

3. Build Apps That Make EHR 'Smart'

A data warehouse is great, says John D'Amore, founder of clinical analytics software vendor Clinfometrics, but it's the healthcare equivalent of a battleship that's big and powerful but comes with a hefty price tag and isn't suitable for many types of battles. It's better to use lightweight drones-in this case, applications-which are easy to build in order to accomplish a specific task.

To accomplish this, you'll need records that adhere to the Continuity of Care Document (CCD) standard. A certified EHR must be able to generate a CCD file, and this is often done in the form of a patient care summary. In addition, D'Amore says, you'll need to use SNOMED CT as well as LOINC to standardize your terminology.

Echoing Halamka, co-presenter Dean Sittig, professor in the School of Biomedical Informatics at the University of Texas Health Science Center at Houston, acknowledges that this isn't easy. Stage 1 of meaningful use, the government incentive program that encourages EHR use, only makes the testing of care summary exchange optional, and at the moment fewer than 25 percent of hospitals are doing so.

The inability or EHR, health and wellness apps to communicate among themselves is a "significant limitation," Sittig says. This is something providers will learn the hard way when stage 2 of meaningful use begins in 2014, D'Amore adds.

That said, the data that's available in CCD files can be put to use in several ways, D'Amore says, ranging from predictive analytics that can reduce hospital readmissions to data mining rules that look at patient charts from previous visits to fill gaps in current charts. The latter scenario has been proven to nearly double the number of problems that get documented in the patient record, he adds.

4. 'Domesticate' Data for Better Public Health Reporting, Research

Stage 2 of meaningful use requires organizations to submit syndromic surveillance data, immunization registries and other information to public health agencies. This, says Brian Dixon, assistant professor of health informatics at Indiana University and research scientist with the Regenstrief Institute, offers a great opportunity to "normalize" raw patient data by mapping it to LOINC and SNOMED CT, as well as by performing real-time natural language processing and using tools such as the Notifiable Condition Detector to determine which conditions are worth reporting.

Dixon compares this process to the Neolithic Revolution that refers to the shift from hunter-gatherer to agrarian society approximately 12,000 years ago. Healthcare organizations no longer need to hunt for and gather data; now, he says, the challenge is to domesticate and tame the data for an informaticist's provision and control.


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