In the hands of the right individuals, healthcare data can be of immense value. Place it in the wrong hands, however, and it can also be a significant privacy risk. PHI or Protected Health Information can contain many details that directly identify a person. These can be names, addresses, financial data, medical histories, etc.; personal identifiers that can point to specific people.
Identity theft, penal consequences, and the risk of serious fraud are some of the dire consequences that can be inflicted on a violation of such sensitive information. Any healthcare organization that works with such information therefore has to be extremely cautious in ensuring data privacy and adhering to all relevant laws such as HIPAA and GDPR.
For healthcare companies to properly use healthcare data without violation of patient privacy, de-identification of PHI is essential.
This works by either altering or entirely removing identifiable elements from the data, allowing the data to then become safe for use for healthcare providers, researchers, insurance companies, and developers.
While effectively safeguarding sensitive information, de-identifying PHI is also great for data-driven innovation in the field of healthcare, medical research, and public health initiatives.
What is PHI and Why Does It Need De-Identification?
PHI, or Protected Health Information, is a class of data that can, either directly or indirectly, be used to identify a specific patient. Typical examples of PHI include things like prescriptions, medical histories, biometric data, healthcare spending details etc.
Data breaches involving PHI can be catastrophic, resulting in loss of patient trust and integrity, loss of funds, and ensuing legal troubles. PHI handling laws differ based on the geolocation of the healthcare organization, i.e., HIPAA and GDPR, where PHI concealment is deemed to be an intrinsic compliance requirement.
The central logic to de-identify patient data is to safeguard individual patients’ identities but at the same time enable data to be used for medical research and analysis. It should also be noted that an imperfect de-identification leaves an open door to matching the data against a particular individual later with advanced re-identification techniques.
Because of these facts, it becomes very important for healthcare systems to carry out proper de-identification of PHI.
Interested Read: De-identification of PHI (Protected Health Information) Under HIPAA Privacy
Key Methods for PHI De-Identification
Expert Determination Method
In this process, experts like privacy specialists and data scientists use their skills and experience to determine whether a particular dataset can be re-identified. Usually, they use resources like statistical models to estimate possible risks, and apply appropriate de-identification techniques according to their findings. This method can often help organizations to strike an outstanding balance between compliance and data utility, and is therefore often the method of choice for healthcare providers.
While highly versatile, the Expert Determination Method is specialized and may be costly. The success of this method is based on the skills of the evaluators and the advanced capabilities of risk analysis software. Despite these drawbacks, it is still a favored method for sophisticated datasets that call for individualized de-identification techniques.
Safe Harbor Method
This standardized process removes eighteen different HIPAA identifiers to ensure PHI cannot be connected to any one person. By methodically removing identifiable elements, organizations create datasets that meet compliance criteria. This is a popular de-identification method for many healthcare providers.
In spite of its upside, there are cons to the Safe Harbor method as well. Sometimes, it can make the data less usable. Also, despite the rigorous framework, data might still need further anonymization for some applications. Therefore, companies need to analyze their data requirements and then determine if this method is appropriate for their use case.
Read Case Study: Protecting PHI in Unstructured Medical Text
Key Data De-Identification Techniques
Data Masking
This is a simple process where actual data can be replaced with altered data values, taking care not to compromise the utility and structural integrity of the overall dataset. With data masking, there is very little compromise in terms of usability when de-identifying PHI. The simplicity and effectiveness of this method makes this one of the most widely used techniques of de-identification in the field of healthcare.
Pseudonymization
This approach replaces direct identifiers with reversible codes. Pseudonymization of PHI for the purpose of de-identification can be a great help for companies to keep data utility intact while preserving patient confidentiality. Since identifiers can be reversed using a key, pseudonymization is useful for longitudinal studies and training of AI.
Anonymization
In contrast to pseudonymization, anonymization removes identifiers permanently, and re-identification is not possible. This procedure is needed for publicly distributed or multi-organization data. Anonymization increases security but can reduce data usability for applications where fine-grained tracking is necessary.
Data Tokenization
Tokenization replaces sensitive data with values that are generated randomly, and therefore PHI isn’t revealed but it can be examined. Tokenized data won’t be readable without a reference database, as opposed to encryption. Tokenization-based data de-identification tools provide an additional layer of security for health records.
Generalization & Suppression
These methods reduce re-identification risks by limiting data specificity. Generalization replaces exact values with more general classes, e.g., exact ages to age ranges. Suppression removes exact data points, and both are helpful in de-identification of PHI without loss of research utility.
Interested Read: Advanced Techniques for De-Identifying PII and Healthcare Data
Challenges in PHI De-Identification
One of the main challenges in PHI de-identification is ensuring that your data still maintains its accuracy and usability after removing all relevant identifiers. This is where organizations need to strike a balance between data usability and de-identification, especially in an era where AI-driven data re-identification techniques have greatly evolved. Strategies of de-identification need constant refinement to stay effective while maintaining the integrity of datasets.
Another layer of complexity gets added to the process when you consider different regulations, like GDPR, HIPAA, and regional laws, and the different standards of compliance each regulation may require.
Healthcare organizations need to stay alert and aware of these requirements, with a constant eye out for changes and amendments. Any sharing of patient data across different organizations also needs to be appropriately scrutinized and validated in order to prevent risks associated with re-identification, and consequent compliance issues.
Best Practices for Effective PHI De-Identification
The best way to de-identify PHI data effectively is to analyze your requirements and choose a combination of techniques to satisfy those requirements. Depending on your analytical requirements, you can choose a mix of techniques like data masking, tokenization, pseudonymization etc, to make sure that data retains its value while aiding in compliance.
It is possible to automate a lot of these tasks to make the process more efficient. However, it is also essential to refine and tune your strategies periodically to protect against emerging risks and remain compliant in the face of changing regulations.
Further, you can put to good use techniques like multi-factor security protocols, data encryption, and audits to stay on top of data security requirements. User access can be restricted based on different levels of authorization, while audit logs can help find loopholes in the system. This way, you can stay compliant with relevant regulations like HIPAA, avoid data re-identification, and get maximum value for medical research.
Tools for PHI De-Identification
There are many data de-identification tools that organizations can use to great effect to aid their privacy and compliance efforts. In many cases, it makes sense to use automated software to scale up de-identification of PHI for efficiency and to reduce the possibility of human error. Recently, AI-driven tools have emerged as ideal solutions for this task, using machine learning to analyze risks of re-identification and providing much-needed help in compliance.
With both commercial and open-source software solutions available, healthcare providers can explore their options. Often, commercial software brings excellent support and advanced compliance features to the table. In contrast, open-source alternatives can be more customizable to fit a particular use case.
Conclusion
Effective de-identification of PHI is critical in an effort to ensure patient data privacy and regulatory compliance. Organizations must employ data de-identification techniques in a bid to prevent re-identification attacks while enabling valuable research and AI applications.
Multi-layered protection and automated solutions must be used to de-identify patient data and protect it. Keeping up-to-date with HIPAA de-identification guidelines and employing best practices will allow organizations to have compliant and secure data environments. Protecto provides top-of-the-line de-identification solutions for protected health information, ensuring security as well as regulatory compliance.
