Abstract: Batch de-identification of unstructured health care documents includes performing optical character recognition (OCR) upon a form-based document so as to produce an initial set of terms. Amongst the initial set of terms, initial specific terms are identified which contain protected information. Each of the identified initial specific terms are then replaced with synthetically generated corresponding terms. Subsequently, additional OCR is performed upon the form-based document so as to produce a new set of terms and new specific terms are identified amongst the new set of terms which are determined to contain protected information. Finally, the new specific terms are compared to the initial specific terms and the form-based document is then added to a repository of de-identified documents only if none of the new specific terms are equivalent to corresponding ones of the initial specific terms. But otherwise, the form-based document is flagged in error.

Abstract: A synthetic generation of healthcare documents for use in training a classifier is described herein. Initially, a multiplicity of electronic forms are received in memory of a host computing system and data extracted from a specific common field located in each of the forms. A statistical metric is then computed for the specific common field a value synthetically generated for the specific common field according to the computed statistical metric. Finally, the synthetically generated value is inserted into the specific common field of a training version of the electronic forms and the training version of the electronic forms persisted as part of a training data set for a classifier adapted to classify the multiplicity of electronic forms.