What is De-Identification, and Why is it Essential for Safeguarding Healthcare Imaging Data?

Healthcare organizations possess vast amounts of personal data, including hospital records, clinical examination results, and medical images. This data is often shared with other healthcare systems or providers to maintain the care continuum or provide relevant patient medical history. Medical images are a foundational tool that enables clinicians to visualize critical information about a patient, helping them diagnose and treat. The digitization of medical images has enhanced the ability to reliably store, share, view, search, and curate these images, thereby assisting medical professionals. The global medical imaging market size was valued at USD 41.6 billion in 2024 and is projected to reach USD 55.4 billion by 2030, growing at a CAGR of 4.95% from 2025 to 2030, reflecting the increasing demand for scalable, compliant, and secure sharing solutions.

What is medical image sharing, and how is it different from sharing data?

When medical images are shared, they are presented in DICOM format. Sharing can occur among physicians and departments within the same healthcare facility, as well as with consultants or patients outside the facility.

DICOM images are unlike other data types and cannot be shared or viewed in the same manner. DICOM image files are large due to their high resolution and quality, limiting their ability to be shared. They also require a specific viewer. A DICOM router capable of recognizing and processing these high-quality images, along with a viewer for viewing them, is necessary.

Sharing medical information is a crucial aspect of healthcare operations and clinical research; however, ensuring that all identifiable patient information is de-identified is critical, particularly when medical images are involved.

What is data de-identification?

Data de-identification is the process of preventing the disclosure of personal identity. Data de-identification involves removing or transforming personal identifiers. Once personal identifiers are removed or de-identified, it is much easier to reuse and safely share the data with third parties.

When applied to metadata or general data, the process is also known as data anonymization. Common strategies include deleting or masking personal identifiers, such as personal names, and suppressing or generalizing quasi-identifiers, such as date of birth. The reverse process of using de-identified data to identify individuals is known as data re-identification.

Original de-identified medical data. Source: ResearchGate

Which Industries Use De-identification?

HIPAA governs data de-identification and is most often applied to medical data. For example, data generated in human-subjects research may be de-identified to protect the privacy of research participants. Biological data may be de-identified to comply with HIPAA regulations, which define and govern patient privacy.

However, data de-identification is also vital for businesses or agencies that want or need to mask identities under other frameworks, such as CCPA and CPRA, GDPR, or in fields of communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio or video surveillance.

Requirements for California Consumer Privacy Act (CCPA), Colorado Privacy Act (CPRA), Virginia Consumer Data Protection Act (VCDPA), and Consumer Privacy Act (CPA). Source: GreenbergTrauig

Another example is that, in surveys such as a census, information is collected from a specific group of people. To encourage participation and protect the privacy of survey respondents, the researchers designed the survey so that no participant’s individual response(s) can be matched to published data.

Re-identification, the process of comparing two images of an object to determine whether they represent the same identity, has applications in object tracking. One example is that, during a soccer match, if a player needs to be identified, the goal (pun intended) is to recognize that player while also identifying each player on the team. The most accurate way to do this is by referencing a catalog to determine which identity corresponds to each image (player). If a catalog does not exist, re-identification algorithms are used to distinguish all players during the soccer match, without determining their true identities.

De-Identification of PHI In Medical Imaging

The process of de-identification, by which identifiers are removed from health information, protects patient privacy while enabling the use of imaging data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors. De-identification in healthcare removes all direct identifiers from patient data, enabling organizations to share it without risking HIPAA violations.

Direct identifiers, known as Protected Health Information, can include a patient’s name, address, and medical record information, and convey a patient’s physical or mental health condition, any healthcare services rendered to that individual, as well as financial data related to healthcare. For example, medical records, hospital bills, and lab results are all PHI.

The increasing adoption of health information technologies highlights their potential to facilitate beneficial research that combines large, complex datasets from multiple sources. Since large sets of health data can support clinical research and benefit the medical community, the HIPAA Privacy Rule permits a covered entity or business associate to de-identify data in accordance with specific standards and specifications.

Medical image before de-identification. Source: Dicom Systems

Why Is De-Identification Important?

De-identification protects the privacy of individuals. Once a dataset has been de-identified and no longer contains personal information, its use or disclosure cannot violate individuals’ privacy.

De-identification in the healthcare industry has multiple use cases, including:

De-identification preserves patient confidentiality without compromising the values and information that may be needed for various research purposes, and protects specific health information that could identify living or deceased individuals.

The HIPAA De-Identification Standard

Data de-identification is expressly governed under HIPAA. There are two methods for de-identifying data in accordance with HIPAA. The first is Safe Harbor, which involves the explicit and implicit removal of all 18 identifiers. The second is Expert Determination, in which a qualified subject-matter expert determines that the risk of re-identifying an individual from the dataset is minimal. Additionally, the expert must thoroughly document their analysis to ensure compliance. The Expert Determination method enables individuals to extract key data points while maintaining patient privacy, but it has limitations.

Two methods to achieve de-identification in accordance with the HIPAA Privacy Rule. Source hhs.gov

Regardless of the method used to de-identify, the Privacy Rule does not restrict the use or disclosure of de-identified health information, as it is no longer considered protected health information.

The Safe Harbor method of de-identification requires removing 18 types of identifiers, so that residual information cannot be used for identification:

Once all of those elements are removed from the data, HIPAA protections no longer apply. Any derivatives of any of the listed identifiers cannot be used under the Safe Harbor method. For example, a document containing the last four digits of a Social Security number would not meet the de-identification requirement.

Lack Of Proper De-Identification Runs The Risk of HIPAA Violation

Data breaches in healthcare are becoming increasingly frequent, with news articles appearing almost daily. The reality is that more frequent breaches could become the norm, requiring greater diligence in security measures and ensuring that patient data is protected. HIPAA regulations were established to protect patient privacy. Still, penalties for non-compliance can include steep fines for organizations that experience data breaches, as well as reputational damage, which can lead to public distrust.

The most common HIPAA violations that have resulted in financial penalties are the failure to perform an organization-wide risk analysis to identify risks to the confidentiality, integrity, and availability of protected health information (PHI); the failure to enter into a HIPAA-compliant business associate agreement; impermissible disclosures of PHI; delayed breach notifications; and the failure to safeguard PHI.

The failure to conduct an organization-wide risk analysis is among the most common HIPAA violations and can result in a financial penalty. If risk analysis is not performed regularly, organizations will be unable to determine whether any vulnerabilities exist in the confidentiality, integrity, or availability of PHI. Risks are therefore likely to remain unaddressed, leaving the door wide open to hackers. HIPAA settlements with covered entities for the failure to conduct an organization-wide risk assessment include:

De-Identification vs. Anonymization

It is essential to distinguish between data de-identification and anonymization. It is not uncommon for researchers to use the terms ‘de-identified’ and ‘anonymous’ interchangeably; however, the distinction between them can mean the difference between a study that must comply with federal regulations governing human subjects research and one that does not.

Both methods remove personal identifiers from a data set. However, after anonymization, the dataset contains no identifiable information, and there is no way to link it back to its source. In contrast, de-identified data can be re-identified.

It is also essential to distinguish between datasets that must comply with the Health Insurance Portability and the Health Insurance Portability and Accountability Act (HIPAA), and those that do not.

Under HIPAA, a dataset is considered de-identified if all 18 identifiers listed at 45 CFR 164.514(b)(2) are removed. If the dataset is not subject to HIPAA, it is considered anonymous if the identity of the human subjects cannot be readily ascertained. Identity is considered readily ascertainable if the information is publicly available or could be determined from publicly available information.

It is crucial to distinguish between anonymous and de-identified data because research involving anonymous data is not considered human subjects research and therefore does not need to comply with federal regulations governing human subjects research.

How Dicom Systems Ensures Effective De-Identification and Compliance

To support healthcare organizations seeking comprehensive, compliant, and scalable de-identification solutions, Dicom Systems offers a solution designed to meet rigorous privacy and compliance requirements while maintaining the usability of medical imaging data.

The Dicom Systems Unifier platform can de-identify DICOM, XML, TIFF, JPEG, PDF, and other image formats, thereby complying with HIPAA safe harbor de-identification requirements for Protected Health Information (PHI). Images and data are received and converted into a standardized format, which can then be transferred to or accessed by referring physicians, radiologists, PACS/MIMPS, RIS, or any radiology workstation, regardless of their physical location.

Dicom Systems Unifier Deidentification Features and Benefits:

Want to learn more about de-identification with the Dicom Systems Unifier platform? Meet with one of our enterprise imaging workflow experts.