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

Oleh:

Morrison, Frances P. ; Li, Li ; Lai, Albert M. ; Hripcsak, George

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: JAMIA ( Journal Of the American Medical Informatics Association ) vol. 16 no. 1 (Jan. 2009), page 37.

Ketersediaan

Perpustakaan FK Nomor Panggil: J43.K.2009.02 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

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.

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