As healthcare organisations continue to digitise their operations, the need to protect sensitive patient data becomes more pressing. The two common methods for data protection are data masking and data de-identification. While both techniques aim to protect sensitive data, they differ in their approach and effectiveness. In this blog, we will explore the differences between data masking and data de-identification and their relevance in healthcare.

What is Data masking?

Data masking is a technique that involves obscuring sensitive data, sometimes replacing it with fictitious data to protect someone’s identity. For instance, replacing a patient’s name with a pseudonym or replacing their identifiers such as medical record number or Social Security Number with hashed or other random numbers. 

The purpose of data masking is to suppress i.e. masking most of the directly identifying attributes. Data masking is commonly used by organisations such as finances to reduce the risk of data breaches. 

However, data masking has limitations when it comes to the protection of sensitive healthcare data, as it does not provide complete anonymity. This is why data-processed masking techniques often require strict access control and data sharing may not be granted without consent.

What is Data De-identification?

Data de-identification on the other hand involves not only removing all identifiable information but also transforming other indirectly identifiable information such as postal code, ethnicity or rare diseases to protect minor grouped individuals’ privacy. Once all identifiers are removed, the data is considered de-identified and can be used for research, analysis, and other purposes without the need for patient consent.

De-identified data can be used for research, analysis, and other purposes without the risk of exposing patient information. The purpose of de-identification is to ensure that data is not re-identifiable, even when combined with other information.

Masking and de-identification in healthcare

While both techniques have their place in healthcare, understanding the difference between the two is crucial for choosing the right approach in any given situation.

Let’s look at a few examples:

Data masking might be the preferred approach when:

  • Sharing research data when zero analytic data loss is required
  • Testing software with limited scope
  • Individual patient consent can be obtained easily

Data de-identification might be the preferred approach when:

  • A hospital needs to share electronic health records with researchers without violating patient privacy
  • Data needs to be shared and analytical data can be transformed to protect patient privacy 
  • Testing software with a wider scope
  • The need to comply with regulations such as HIPAA and GDPR

So what’s better?

If you’re looking for a high level of anonymity and protection from re-identification, data de-identification is superior as it removes all direct and indirect identifiers from sensitive data. Data masking can be used to mask directly identifying information about individuals but it does not provide complete anonymity as the individual can be identified with enough effort and resources. 

While both masking and de-identification have their place, it’s useful to know when to use each approach for safeguarding data privacy, all while maintaining data usefulness. 

Orion Health’s Orchestral De-identify

Orion Health’s De-identify can help you extract insights while maintaining the security and privacy of your patient data. It is specifically designed for the healthcare industry, with a focus on patient privacy and compliance with regulatory requirements.

Why pick Orion Health’s De-identify?

  • Use the latest, sophisticated de-identification techniques to safely de-identify data
  • Can handle multiple data types including databases, data sets, free text, messages, and more
  • Our software helps you assess the risk of sharing de-identified data with specific individuals or groups based on sensitivity and context
  • Guide users through the entire de-identification process, from raw to the desired output, trading off the strength of de-identification with information loss and bias
  • Others are generalists, but we specialise in healthcare
  • Free text de-identification allows you to extract valuable insights from unstructured data

Interested in finding out more about how Orion Health’s De-identify can help you protect patient privacy, ensure compliance and save time and resources?