Investigators with the Mayo Clinic in Rochester, Minn., are close to completing a suite of computing tools that can be used to sort and identify digital health information from all types of electronic medical records (EMRs)--regardless of the file formats or types of data organizations.
The announcement was made following a two-day conference at the University of Minnesota highlighting milestones of the $60 million Strategic Health IT Advanced Research Projects (SHARP) initiative, funded by Office of the National Coordinator for Health IT. The SHARP project is seeking to safely convert stores of EMR data to support research--while maintaining privacy and security of that data.
To date, the investigators have used natural language processing tools to isolate health information from about 30 EMRs of patients with diabetes. When run through computing systems developed in partnership with IBM's Watson Research Center, those 30 patient records have expanded into 134 billion individual pieces of information that can be organized and stored.
"There is a huge ocean of information that has the potential to significantly improve delivery of care," said Wil Yu, the federal agency's SHARP program coordinator, in a statement.
Currently, different hospitals and healthcare organizations and health IT vendors tag and store health information in different formats--many of them proprietary. Investigators at the Mayo Clinic and three other research institutions--also funded by SHARP--are working on software to mine the data for best practices and statistical trends.
The other SHARP grant projects include: the University of Illinois at Urbana-Champaign, on security of health information; the University of Texas Health Science Center at Houston, on patient-centered cognitive support for clinicians; and Harvard University, on new healthcare applications and network-platform architecture.
For more information:
- view the Mayo Clinic announcement
- see the Government Health IT article