Saturday, December 3, 2016

IHE: Analysis of Optimal De-Identification Algorithms for Family Planning Data Elements

This is a use of the IHE published De-Identification Handbook against a use-case. The conclusion we came to is an important lesson, that sometimes the use-case needs can't be met with de-identification to a a level of 'public access'.  That is that the 'needs' of the 'use-case' required so much data to be left in the resulting-dataset, that the resulting-dataset could not be considered publicly accessible. This conclusion was not much of a problem in this case as the resulting-dataset was not anticipated to be publicly accessible.

The de-identification recommended was still useful as it did reduce risk, just not fully. That is that the data was rather close to fully de-identified; just not quite. The reduced risk is still helpful.

Alternative use-case segmentation could have been done. That is we could have created two sets of use-cases, that each targeted different elements while also not enabling linking between the two resulting-datasets. However this was seen as too hard to manage, vs the additional risk reduction.

Further articles on De-Identification


IHE IT Infrastructure White Paper Published

The IHE IT Infrastructure Technical Committee has published the following white paper as of December 2, 2016:
  • Analysis of Optimal De-Identification Algorithms for Family Planning Data Elements  
The document is available for download at Comments on all documents are invited at any time and can be submitted at ITI Public Comments.