IBM, Georgia Tech Launch Children's Health Data Modeling Project
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.
By analyzing the data, researchers hope to see how to fix the pediatric health system systemically rather than at one level at a time, according to William B. Rouse, executive director of the Tennenbaum Institute at Georgia Tech.
"We're looking at very large data sets and need to portray relationships in those data sets," Rouse told eWEEK. "We're trying to use the computational power to provide that broader view."
For a bone fracture, researchers might run models to see how a patient would fare based on location, speed of treatment and whether they took calcium supplements or not, Meyerson explained.
"If you believe that the healing time of a fracture is related to some unforeseen medical issue such as the presence of diabetes, how important is it to tightly control sugar levels?" Meyerson asked. "It should show you when children have well-controlled diabetes."
For conditions such as asthma, autism and diabetes, researchers will create models based on claims and diagnoses as well as drugs administered, Rouse said.
"Think about health analytics as treatment protocols involving seven, eight, nine different things that would yield an outcome you wouldn't find unless you've looked at it together," Meyerson said.
The data analytics work will reveal data incompatibilities in IT systems that need to be fixed, according to Rouse.
"We have the basic machinery to do this well, but we have to get behind problems of incompatibility of patients' data sources and all the complicated factors of industrial systems," Rouse said.