Abstract. Evolving technology has enabled large-scale collaboration for neuroimaging data. For high resolution structural neuroimages, these data are inherently identifiable and must be given the same privacy considerations as facial photographs. To preserve privacy, identifiable metadata should be removed or replaced, and the voxel data de-identified to remove facial features by applying skull stripping or a defacing algorithm. The Quickshear Defacing method uses a convex hull to identify a plane that divides the volume into two parts, one containing facial features and another the brain volume, and removes the voxels on the facial features side. This method is an effective alternative to existing solutions and can provide reductions in running time.
Radio frequency identification (RFID) is a form of wireless communication that is used to identify assets and people. RFID has significant benefits to the medical environment. However, serious security threats are present in RFID systems that must be addressed in a medical environment. Of particular interest are threats to patient privacy and safety based on interception of messages, interruption of communication, modification of data, and fabrication of messages and devices. This paper presents an overview of these security threats present in RFID systems in a medical environment and provides guidance on potential solutions to these threats. This paper provides a roadmap for researchers and implementers to address the security issues facing RFID in the medical space.
Electronic Health Record (EHR) systems are a powerful tool for healthcare providers and patients. Both groups benefit from unified, easily accessible record management; however, EHR systems also bring new threats to patient privacy. The reach of electronic patient data extends far beyond the healthcare realm. Patients are managing their own health records through personal health record (PHR) service providers, and businesses outside of the healthcare industry are finding themselves increasingly linked to medical data. The Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule and other regulatory measures establish baseline standards for protecting patient privacy, but the inclusion of medical images in patient records presents unique challenges. Medical images often require specialized management tools, and some medical images may reveal a patient’s identity or medical condition through re-linkage or inherent identifiability. After exploring EHR systems in-depth and reviewing health information policy, the chapter explores how privacy challenges associated with EHR systems and medical images can be mitigated through the combined efforts of technology, policy, and legislation designed to reduce the risk of re-identification.
In neuroscience, collaboration and data sharing are undermined by concerns over the management of protected health information (PHI) and personal identifying information (PII) in neuroimage datasets. The HIPAA Privacy Rule mandates measures for the preservation of subject privacy in neuroimaging studies. Unfortunately for the researcher, the management of information privacy is a burdensome task. Wide scale data sharing of neuroimages is challenging for three primary reasons: (i) A dearth of tools to systematically expunge PHI/PII from neuroimage data sets, (ii) a facility for tracking patient identities in redacted datasets has not been produced, and (iii) a sanitization workflow remains conspicuously absent. This article describes the XNAT Redaction Toolkit—an integrated redaction workflow which extends a popular neuroimage data management toolkit to remove PHI/PII from neuroimages. Quickshear defacing is also presented as a complementary technique for deidentifying the image data itself. Together, these tools improve subject privacy through systematic removal of PII/PHI.
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