Ohio State Gets Grant for EHR Data Integration Initiative

Sept. 18, 2012
The Ohio State University College of Medicine’s Department of Biomedical Informatics announced that the National Library of Medicine has given it a $1.3 million grant for an EHR data integration initiative. The initiative will aim to integrate information within a patient’s EHR to generate a "longitudinal medical history that can help accelerate recruitment of patients into clinical trials," the college says.

The Ohio State University College of Medicine’s Department of Biomedical Informatics announced that the National Library of Medicine has given it a $1.3 million grant for an EHR data integration initiative. The initiative will aim to integrate information within a patient’s EHR to generate a "longitudinal medical history that can help accelerate recruitment of patients into clinical trials," the college says.

The approach, according to the Ohio State biomedical informatics team, is called “information fusion.” It involves processing computer data stored in uncoded, narrative text fields, extracting the structured data from unstructured data, merging multiple data sets, and binding episodic events together to create a medical portrait for individual patients.

"Electronic health records are composed of multiple data sources that are often redundant or inconsistent, stored in uncoordinated and unstructured clinical narratives and structured data. These characteristics make EHRs difficult to use for matching patients against the complex event and temporal criteria of clinical trials protocols. This research proposes that an improved longitudinal health record (LHR), which contains a comprehensive clinical summary of a patient, can improve patient screening, and therefore expedite recruitment of patients into clinical trials," Albert Lai, research assistant professor at the College of Medicine and also principal investigator, said in a statement.

The researchers say this approach could address both the meaning and temporal nature of data contained within patients’ EHRs, resulting in more accurate information than if these same data sources were analyzed individually.

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