Identifiable Health Data

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Identifiable Health Data (or Personally identifiable Health Data) refers to any information that can be used, either alone or in combination with other information, to uniquely identify, contact, or locate a single person.


Data are considered “individually identifiable” if they include any of the 18 types of identifiers specified by the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule:

  • Name
  • Address (all geographic subdivisions smaller than state, including street address, city, county, ZIP code)
  • All elements (except years) of dates related to an individual (including birth date, admission date, discharge date, date of death and exact age if over 89)
  • Telephone numbers
  • FAX number
  • E-mail address
  • Social Security number
  • Medical record number
  • Health plan beneficiary number
  • Account number
  • Certificate/license number
  • Any vehicle or other device serial number
  • Device identifiers or serial numbers
  • Web URL
  • Internet Protocol (IP) address numbers
  • Finger or voice prints
  • Photographic images
  • Any other characteristic that could uniquely identify the individual


De-identified patient data

De-identified patient data is patient data that has been removed of important identifiers such as birth date, gender, address, and age.

De-identified patient data is often used for research. The Health Insurance Portability and Accountability Act (HIPAA) allows the use of such de-identified data without requiring special authorization, and its use or disclosure without restrictions

Information is de-identified when it is not possible to 'reasonable ascertain' the identity of a person from that data.

The definition of Irreversible de-identification of data is context driven. The capacity of re-identify de-identified data may depend critically on particular resources( Intellectual, Information Technology, Access to multiple data sets).(1) Efforts are being made to automate the anonymization of health information by developing de-identifications models that can successfully remove personal health information. (2)

  1. Australian Government. Office of the Privacy Commissioner.
  2. State-of-the-art Anonymization of Medical Records Using an Iterative Machine Learning Framework; György Szarvas, Richárd Farkas, Róbert Busa-Fekete b J Am Med Inform Assoc. 2007 Sep–Oct; 14(5): 574–580.


Friedlin, F. J., McDonald, C. J. A Software Tool for Removing Patient Identifying Information from Clinical Documents. (2008) JAMIA, 15 (5); 601 – 610. PMCID: PMC2528047