Narrative clinical reports contain a rich set of clinical knowledge that could be invaluable for clinical research. However, they may also contain personally identifiable information (PII) that make those clinical reports classified as PHI, which is associated with use restrictions and risks to privacy. Computational de-identification seeks to remove all instances of PII in such narrative text in order to produce de-identified documents, which would no longer be classified as PHI and can be used in research with fewer constraints and with almost no risk to privacy. Computational de-identification uses pattern recognition and computational linguistic methods to recognize words and other alphanumeric tokens denoting PII (e.g., names, addresses, and telephone and social security numbers) in the text, and redacts them. In this way, patient privacy is protected and clinical knowledge is preserved. After exploring existing de-identification tools, the U.S. National Library of Medicine (NLM) began developing a new application software called NLM Scrubber, which is capable of de-identifying many types of clinical reports with high accuracy. The software design is based on both deterministic and probabilistic pattern recognition and computational linguistic methods utilizing large dictionaries of personal names, addresses, and organizations. The application accepts narrative reports in plain text or in HL7 format. When the input reports are formatted as HL7 messages, the application software leverages patient information embedded in HL7 segments to find such information in the text portion of the HL7 message. In November 2014, we released the first beta version of NLM Scrubber, which is freely downloadable from NLM Scrubber performs quite well on detecting words and other alphanumeric tokens containing PII found on dictated reports. Our focus is on extending our work to further improve NLM Scrubbers de-identification performance across a large spectrum of identifiers and additional report types. Work is underway to use the NLM Scrubber for de-identification of narrative reports within the NIH Biomedical Translational Research Information System (BTRIS) and in the NCI Surveillance, Epidemiology, and End Results (SEER) databases.

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Kayaalp, Mehmet (2018) Patient Privacy in the Era of Big Data. Balkan Med J 35:8-17
Kayaalp, Mehmet; Browne, Allen C; Sagan, Pamela et al. (2015) Challenges and Insights in Using HIPAA Privacy Rule for Clinical Text Annotation. AMIA Annu Symp Proc 2015:707-16
Browne, Allen C; Kayaalp, Mehmet; Dodd, Zeyno A et al. (2014) The Challenges of Creating a Gold Standard for De-identification Research. AMIA Annu Symp Proc 2014:353-8
Huser, Vojtech; Kayaalp, Mehmet; Dodd, Zeyno A et al. (2014) Piloting a deceased subject integrated data repository and protecting privacy of relatives. AMIA Annu Symp Proc 2014:719-28
Kayaalp, Mehmet; Browne, Allen C; Dodd, Zeyno A et al. (2014) De-identification of Address, Date, and Alphanumeric Identifiers in Narrative Clinical Reports. AMIA Annu Symp Proc 2014:767-76
Kang, Yanna Shen; Kayaalp, Mehmet (2013) Extracting laboratory test information from biomedical text. J Pathol Inform 4:23
Kayaalp, Mehmet; Browne, Allen C; Callaghan, Fiona M et al. (2013) The pattern of name tokens in narrative clinical text and a comparison of five systems for redacting them. J Am Med Inform Assoc :
Fung, Kin Wah; Kayaalp, Mehmet; Callaghan, Fiona et al. (2013) Comparison of electronic pharmacy prescription records with manually collected medication histories in an emergency department. Ann Emerg Med 62:205-11