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Repurposing the Clinical Record: Can an Existing Natural Language Processing System De-identify Clinical Notes?

Columbia University Department of Biomedical Informatics, New York, NY

^_* Correspondence: Frances Morrison, 622 West 168th Street, Vanderbilt Clinic, 5th Floor, New York, New York 10032 (Email: frances.morrison{at}dbmi.columbia.edu).

Received for publication: 05/16/08; accepted for publication: 09/30/08.

Electronic clinical documentation can be useful for activities such as public health surveillance, quality improvement, and research, but existing methods of de-identification may not provide sufficient protection of patient data. The general-purpose natural language processor MedLEE retains medical concepts while excluding the remaining text so, in addition to processing text into structured data, it may be able provide a secondary benefit of de-identification. Without modifying the system, the authors tested the ability of MedLEE to remove protected health information (PHI) by comparing 100 outpatient clinical notes with the corresponding XML-tagged output. Of 809 instances of PHI, 26 (3.2%) were detected in output as a result of processing and identification errors. However, PHI in the output was highly transformed, much appearing as normalized terms for medical concepts, potentially making re-identification more difficult. The MedLEE processor may be a good enhancement to other de-identification systems, both removing PHI and providing coded data from clinical text.