Dicom Systems offers a proven and scalable de-identification of medical images solution that unlocks valuable imaging studies for areas such as research, policy assessment, and comparative effectiveness studies. Dicom Systems Unifier platform can de-identify DICOM, XML, TIFF, JPEG, PDF, and other image formats complying with HIPPA safe harbor de-identification of Protected Health Information (PHI) requirements. Images and data are received and translated into a standardized format that can then be transferred to or accessed by referring physicians, radiologists, PACS/MIMPS, RIS or to any radiology workstation, regardless of its physical location.

What Is De-Identification?

HIPAA safe harbor de-identification is the process that removes specific identifiers of the patient, and of the patient’s relatives, household members, and employers. The requirements of the HIPAA safe harbor de-identification process become fully satisfied if, and only if, after the removal of the specific identifiers, the covered entity has no actual knowledge that the remaining information could be used to identify the patient.

HIPAA safe harbor de-identification methodology requires 18 PHI identifiers to be masked or removed—making data preparation a complex undertaking.

De-Identification Benefits & Features

  • Adherence to the HIPAA Privacy Rule and Safe Harbor auditee by third parties.
  • Full customization of processes and output
  • Robust enough for large scale de-identification with no impact to clinical workflow.
  • Supports full DICOM, DICOMwebFHIR and  HL7 interoperability with compatible devices
  • Removes pixel, mask-based, metadata, text removal from images referencing an industry database of modalities based on vendor and model to find exact coordinates of where text was burnt into the medical images. Optical Character recognition (OCR ) software capability available for certain use cases.
  • Best price-to-performance technology trusted by top healthcare enterprises, government agencies, and imaging partners.
  • When deployed in conjunction with Dicom Systems Enterprise Imaging Unifier VNA, it leverages a robust framework for imaging lifecycle management and archiving when deployed with Unifier.
  • Bi-directional dynamic tag morphing makes changes on input and output.
  • Advanced pixel-level de-identification to avoid accidental corruption or truncation of the image file.
  • Complex DICOM tag substitutions, removals or morphing are automated by designing transformations into the LUA script framework.
  • Full customization of de-identification processes and output.

De-Identification As On-Ramp to AI and Machine Learning Innovation

High quality data is an essential contribution to better machine learning algorithms, unleashing tremendous potential for AI solutions that benefit patient care. For AI algorithms to properly “consume” patient data, it must be standardized and HIPAA-compliant. To that end, Unifier’s de-identification functionality seamlessly delivers de-identified data in the right format and quality, for use in any relevant AI algorithm.