Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes

Abstract There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach...

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Autores principales:	Beau Norgeot, Kathleen Muenzen, Thomas A. Peterson, Xuancheng Fan, Benjamin S. Glicksberg, Gundolf Schenk, Eugenia Rutenberg, Boris Oskotsky, Marina Sirota, Jinoos Yazdany, Gabriela Schmajuk, Dana Ludwig, Theodore Goldstein, Atul J. Butte
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/65f57d47a8274a3a8bf988b25258315d
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spelling	oai:doaj.org-article:65f57d47a8274a3a8bf988b25258315d2021-12-02T15:51:17ZProtected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes10.1038/s41746-020-0258-y2398-6352https://doaj.org/article/65f57d47a8274a3a8bf988b25258315d2020-04-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0258-yhttps://doaj.org/toc/2398-6352Abstract There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter (“Protected Health Information filter”). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods.Beau NorgeotKathleen MuenzenThomas A. PetersonXuancheng FanBenjamin S. GlicksbergGundolf SchenkEugenia RutenbergBoris OskotskyMarina SirotaJinoos YazdanyGabriela SchmajukDana LudwigTheodore GoldsteinAtul J. ButteNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Beau Norgeot Kathleen Muenzen Thomas A. Peterson Xuancheng Fan Benjamin S. Glicksberg Gundolf Schenk Eugenia Rutenberg Boris Oskotsky Marina Sirota Jinoos Yazdany Gabriela Schmajuk Dana Ludwig Theodore Goldstein Atul J. Butte Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
description	Abstract There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter (“Protected Health Information filter”). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods.
format	article
author	Beau Norgeot Kathleen Muenzen Thomas A. Peterson Xuancheng Fan Benjamin S. Glicksberg Gundolf Schenk Eugenia Rutenberg Boris Oskotsky Marina Sirota Jinoos Yazdany Gabriela Schmajuk Dana Ludwig Theodore Goldstein Atul J. Butte
author_facet	Beau Norgeot Kathleen Muenzen Thomas A. Peterson Xuancheng Fan Benjamin S. Glicksberg Gundolf Schenk Eugenia Rutenberg Boris Oskotsky Marina Sirota Jinoos Yazdany Gabriela Schmajuk Dana Ludwig Theodore Goldstein Atul J. Butte
author_sort	Beau Norgeot
title	Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
title_short	Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
title_full	Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
title_fullStr	Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
title_full_unstemmed	Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
title_sort	protected health information filter (philter): accurately and securely de-identifying free-text clinical notes
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/65f57d47a8274a3a8bf988b25258315d
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Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes

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