6 Big Data Analytics Use Cases for Healthcare IT

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.

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Tue, April 23, 2013
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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.

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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.

The benefits of this process—in addition to meeting regulatory requirements—include research that takes into account demographic information as well as corollary tests related to specific treatments. This eliminates gaps in records that public health agencies often must fill with phone calls to already burdened healthcare organizations, Dixon notes. In return, the community data that physicians receive from public health agencies will be robust enough to offer what Dixon dubs "population health decision support."

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Dr. Mark Dente, managing director and chief medical officer for MBS Services, recommends that healthcare organizations "aggregate clinical data at whatever level you can afford to do it," then normalize that data (as others explain above). This capability to normalize data sets in part explains the growth and success of providers such as Kaiser Permanente and Intermountain Healthcare, he says.

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To do this, you need to create modules and apps such as the ones D'Amore describes. This often requires linking contemporary data sets to legacy IT architecture. The MUMPS programming language, originally designed in 1966, has served healthcare's data processing needs well, but data extraction is difficult, Dente says.

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