Extracting Surveillance Data from Templated Sections of an Electronic Medical Note: Challenges and Opportunities

Adi Gundlapalli, Guy Divita, Marjorie Carter, Shuying Shen, Miland Palmer, Tyler Forbush, Brett South, Andrew Redd, Brian Sauer, Matthew Samore


Apart from the traditional structured data elements used for surveillance, the free text of the medical note provides a rich source of epidemiological information. Many electronic notes use boiler-plate templates from EMR pull-downs to document information on the patient in the form of checklists, check boxes, yes/no and free text responses to questions. There is a dearth of literature on the use of natural language processing in extracting data from templates in the EMR. This study was undertaken to highlight the challenges and opportunities of addressing templates while developing NLP algorithms for surveillance using the free text of electronic notes.

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DOI: https://doi.org/10.5210/ojphi.v5i1.4535

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org