TOPIC 427 - AUTOMATED DEIDENTIFICATION OF PATHOLOGY AND RADIOLOGY DATA

This proposal has three main objectives. Firstly, to identify actual and potential PHI/PII locations in DICOM and WSI data - both tags in the header data and coordinates in the image data, including manufacturer specific data - and to deliver a detailed, broad-based landscape analysis report. Secondly, to deliver a robust, validatable software solution that facilitates the development, management, chaining, and execution of medical image data de-identification algorithms. For Phase I, we will implement data de-identification pipelines using as a baseline the data elements identified in the landscape analysis report. Thirdly, to develop, on the same software platform, a set of deep learning-based algorithms to perform image data deidentification. We will facilitate the execution of these algorithms against data both local and remote, in order to obviate any security concerns regarding having to move data to the cloud for de-identification - i.e., we will ""bring the algorithms to the data"". Our cloud-based solution, EICON REACH (Remote Execution of Algorithms for Clinical Health), provides the underpinning for this proposal. We will enhance its capabilities to address directly the objectives listed. At the end of Phase I, we will provide these capabilities in a fully tested, fully documented, validated solution with full audit trail of user actions and data transformations. EICON REACH can also serve as the basis for a Phase II proposal wherein we would undertake a broader and deeper effort to address the more complex issues related to identification, review and redaction of PHI in the image data.