Slideshow 6 Reasons You Should Use Dirty Data

Published
  • July 20 2015, 9:34am EDT
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6 Reasons You Should Use Dirty Data

Healthcare analytics are used to best advantage when aimed at treating patients, and to best support that mission, don’t waste time and money cleaning your data before analyzing it—just use it. That’s the message John Showalter, M.D., chief health information officer at the University of Mississippi Medical Center, brought to HDM’s recent Healthcare Analytics Symposium. (Photo: Fotolia)

Population Health

Analytics enables a provider to identify individuals at risk, and to work with those individuals to change risky behaviors and overcome barriers, Showalter said. Focusing on particular individuals helps identify barriers, such as the fact that there’s no pharmacy on an individual’s bus line. A provider can improve the health of a population by improving the health of one individual. So, stop cleaning data. Sure, it’s dirty, but how do you know the codes in progress reports that have been turned into data are accurate and really reflect the patient? You don’t, he said. (Photo: Fotolia)(Photo: Fotolia)

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Lost Intelligence

Cleaning data is expensive and doesn’t improve analytics, Showalter contended. When cleaning, there is a lot of data thrown out that doesn’t have to be. That’s intelligence you’ll never analyze. Use of neural networks and machine learning technology can help create many associations out of the data. The bottom line is simple for Showalter: “Twitter and Facebook have horribly dirty data. They don’t clean it, they just use it.” It works for them, and healthcare should follow suit. (Photo: Fotolia)

Know Your Community

Current analytics work at UMMC includes sending every clinical note through natural language processing technology to convert SNOMED concepts and sent them out for analysis. This can speed up decisions to take action. Showalter also advocates using geo-coding, as it is cheap and effective. UMMC took 2 million addresses from its data warehouse and geo-coded them in about three months. Now, physicians and researchers can pull up maps and look at diabetics, healthcare disparities and other metrics by ZIP code. And the software to do this cost less than $10,000. It’s not predictive analytics, but it shows where the community stands. (Photo: Fotolia)

Find Friends

Don’t do analytics alone. Find partners, such as a vendor, hospital or clinic. Don’t reinvent everything. Stop creating silos. Stop trying to shut down competitors and cooperate with them on lowering readmission rates and other initiatives that meet patient needs, Showalter said. “All ships rise with the tide.” (Photo: Fotolia)

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Leverage the EHR

Don’t put analytics results in a portal; put results in the electronic health record. Just like lab tests flow to the EHR, so should analytics. “They are a lab test of your data,” Showalter explained. (Photo: Fotolia)

Get Results

UMMC contracts with an outside vendor to conduct its predictive analytics and has had some stunning results. For instance, 70 percent of an analyzed high-risk group was predicted to have a heart attack within three to six months. “Stop cleaning data, integrate the results and stop trying to be perfect,” Showalter said. (Photo: Fotolia)