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
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
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