IBM, Georgia Tech Partner on Children's Health Program
EUC with HCI: Why It Matters
IBM and the Georgia Institute of Technology have launched a program called One Million Healthy Children to use data modeling to analyze the health information of children suffering from asthma, autism and diabetes.
The goal of 1MHC is to analyze the health data of 1 million children in the state of Georgia, according to IBM.
1MHC will analyze models of fee-for-service health care, in which patients pay providers for specific services. They'll also study how transportation, health services, socio-economic status, education history and food resources affect the health of children.
For the project, IBM is contributing financial support as well as the data storage capabilities of DB2 database software and data analytics from SPSS and Cognos.
The tools will allow Georgia Tech to plug in various factors from unstructured data to see what the outcomes might be, IBM reports.
"We've developed some extraordinary ways of taking data from a given field and parsing that data and building models around it using intelligence," Bernard Meyerson, IBM fellow and vice president of innovation, told eWEEK.
IBM and Georgia Tech announced the big data initiative on Oct. 27.
To adhere to privacy measures, the data will be "anonymized" and untraceable back to patients. "We're extremely careful of this aspect," Meyerson said. "We don't want any possible way to work backwards."
Researchers will study data from more than 16,000 health records of Emory University employees' children in the first stage of the project.
In later stages, analytical models will incorporate data from Children's Healthcare of Atlanta, Georgia Cancer Coalition and the Georgia Department of Community Health.
Researchers aim to use the analytic models to enable health care providers to save time and money and better understand differences in pediatric health care expenditures and outcomes among various populations.
Using a multi-level model, Georgia Tech's researchers will show visualizations of a medical history across the provider, patient and payee. Data models will incorporate information on the medical and financial aspects and run "correlation analyses" from multiple angles to see if outcomes may occur that doctors wouldn't anticipate, Meyerson noted.
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