Physician notes with annotated PHI

Question

I am looking for a data set of physician notes with annotated PHI (protected health information) as defined in Health Insurance Portability and Accountability Act (HIPAA)'s de-identification guide (US regulations). I.e. the following information are considered as PHI:

(i) Names of patients and family members
(ii) Addresses and their components
(iii) Dates (month and day parts, unless the inclusion of the year part identities an individual to be older than 90 years old)
(iv) Explicit mention of ages over 89 years old
(v) Telephone and fax numbers
(vi) Social Security numbers
(vii) Medical record numbers
(viii) Health plan beneficiary numbers
(ix) Account numbers
(x) Certificate or license numbers
(xi) Vehicle identifiers and serial numbers
(xii) Device identifers and serial numbers
(xiii) Electronic mail addresses
(xiv) Web universal resource locators (URLs)
(xv) Internet protocol (IP) addresses
(xvi) Biometric identifiers
(xvii) Full face photographic images
(xviii) Employers
(xix) Any other unique identifying number, characteristic or code

Answer 1

1) i2b2 2006 Deidentification and Smoking Challenge's data set:

NLP Data Set #1B: 889 de-identified discharge summaries with de-identification challenge annotations, training and test sets and ground truth.

Please cite as:

Uzuner Ö., Juo Y, Szolovits P. "Evaluating the state-of-the-art in automatic de-identification". J Am Med Inform Assoc. 2007, 14(5):550-63. http://www.jamia.org/cgi/content/abstract/14/5/550 .

2) i2b2 NLP Data Set #7a: De-identification Challenge Data Set:

Please cite as:

• Stubbs A, Uzuner Ö. (2015). "Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus". J Biomed Inform. 2015 Aug 28. pii: S1532-0464(15)00182-3. doi: 10.1016/j.jbi.2015.07.020. [Epub ahead of print]. http://www.ncbi.nlm.nih.gov/pubmed/26319540.

• Stubbs A, Kotfila C, Uzuner Ö. (2015). "Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1". J Biomed Inform. 2015 Jul 28. pii: S1532-0464(15)00117-3. doi: 10.1016/j.jbi.2015.06.007. [Epub ahead of print]. http://www.ncbi.nlm.nih.gov/pubmed/26225918.

3) i2b2 2016 (mirror) CEGS N-GRID challenge; Track 1: De-identification.

4) https://www.physionet.org/physiotools/deid/

The deid software package was developed and tested using a gold standard corpus of 2,434 nursing notes that have been thoroughly deidentified by a multi-pass process that included meticulous reviews by three or more experts working independently, as well as by a variety of automated methods. All detected instances of PHI in these nursing notes have been replaced by realistic surrogate data in the gold standard corpus. Although the deid software, as noted above, may be redistributed under the terms of the GPL, the gold standard corpus, because of the very small possibility that it may contain one or more instances of undetected PHI, is currently available only under terms that do not permit it to be redistributed.

• Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and Physionet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13).
• Neamatullah I, Douglass M, Lehman LH, Reisner A, Villarroel M, Long WJ, Szolovits P, Moody GB, Mark RG, Clifford GD. Automated De-Identification of Free-Text Medical Records. BMC Medical Informatics and Decision Making, 2008, 8:32. doi:10.1186/1472-6947-8-32

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In case anyone is interested, we presented an overview of the state-of-the art results on these two datasets in:

• Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, Peter Szolovits. De-identification of Patient Notes with Recurrent Neural Networks. Journal of the American Medical Informatics Association (JAMIA). 2016.
• Ji Young Lee, Franck Dernoncourt, Ozlem Uzuner, Peter Szolovits. De-Feature-Augmented Neural Networks for Patient Note De-identification. International Conference on Computational Linguistics (COLING) Clinical NLP. 2016.