Biomedical informatics experts at Ohio State’s College of Medicine call this novel approach “information fusion” – a method which involves processing computer data stored in uncoded, narrative text fields, extracting 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," says Albert Lai, research assistant professor at the College of Medicine and also principal investigator.
Researchers believe creating such a method of information fusion will 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.
“With an LHR formed through information fusion for screening patients for clinical trials eligibility, we will be able to not only reduce the amount of staff effort required to recruit a patient into a clinical trial, but also accelerate the pace at which clinical trials can be conducted,” adds Lai.
Contact: Sherri Kirk, College of Medicine Public Relations, 614.366.3277, or