Nowadays, healthcare conducts its operations on a digital landscape. As a result, the urge to protect sensitive patient data becomes more pressing. The primary methods used to protect this critical information are through data masking and de-identification.
In this article, we’ll delve deeper into the workings of data masking. At the same time, we’ll see how this method enables and preserves data privacy.
Data Protection’s Demand Rises for Healthcare Providers
Healthcare providers have been using electronic health records these days. These EHRs have provided better access to their patients’ data. However, it does bring a new concern to the table in the form of data security. Lots of organizations have expressed concerns in regard to data breaches in their patient database. At the same time, patients expressed worry about the privacy of their medical information.
Data Masking: An Overview
On the bright side, there are solutions to data security concerns. One of them is Data Masking, but here’s the question – what is data masking? Data masking is the process where sensitive data is obscured by false information. What happens here is that the true data is masked by fictitious data. For example, a patient’s name can be replaced with a pseudonym. Also, their medical record numbers may be replaced with random numbers. Aside from those details, their date of birth may be shifted by a few or many days.
The goal here is to allow authorized staff access without exposing the true details of the patient. While it has those potent perks, data masking has some limited applications in healthcare. For one, it doesn’t guarantee complete anonymity. It also requires you to configure access controls and patient consent. Also, there’s still the risk of inference attacks – an example is a masked date of birth that can reveal the patient’s estimated age.
Data De-Identification’s Role in Patient Data Privacy
Compared to data masking, data de-identification removes all identifiable information but also transforms other data as well. There are two de-identification methods, which are:
- Safe Harbor Method. This de-identification method requires the removal of 18 common identifiers.
- Statistical Method. It assesses the re-identification risks and limits them as much as possible.
Once the patient data is de-identified, it can also be used for secondary purposes such as research and improving care. The best part about data de-identification is that it doesn’t need the patient’s consent. That’s what makes de-identified data a more versatile approach to protecting patient data.
Selecting the Right Data Protection Approach
Both methods of protecting your patient’s data have their place in healthcare data privacy efforts. However, the choice depends on the specific uses that your organization does. Regardless, here are a couple of ways to select the right data protection approach.
1. Data Masking Is Acceptable on Systems With No Data Losses
If your database doesn’t suffer from data losses then data masking is an acceptable data protection approach. It is suitable for test systems that contain the patient’s information. It is also useful for sharing lightly redacted records with third parties – provided that there is proper consent.
2. Use De-Identification for EHRs
Data de-identification is great if you’re using electronic health records (EHRs) for secondary purposes. Examples would be research work or creating healthcare datasets. It offers robust anonymity without distorting the data’s utility.
Keep in mind that your healthcare organization must make careful and informed choices based on these factors:
- Downstream data usage
- Legal obligations
- Patient expectations
Be aware that in the realm of healthcare, patient data requires a lot of care as it is valuable. You must balance accessibility and privacy. Both methods offer unique advantages that ensure no unauthorized access can reach them.
Limitations of Data Masking
As mentioned earlier, data masking has some limitations in the healthcare niche. Identifying the limitations of this method can help you come up with ideas to shore up those gaps. Here are a few caveats when using data masking:
1. Anonymity Is Not 100%
Data masking doesn’t guarantee that the data is 100% anonymous. As stated earlier, it necessitates strict access controls if data sharing is needed. There’s also the risk of inference attacks where hackers can guess the patient’s age due to the birth year.
2. Data Can Get Distorted
Other masking techniques such as shuffling, substitution, and generalization can distort statistics. It can also affect data relationships. Both of them can limit the use of masked data for analytics.
3. Referential Integrity Must Be Preserved
You also need to maintain referential integrity in relational healthcare data. This is the case when dealing with complex data masking situations. A patient’s masked ID must show consistency across different interacting databases. For example – prescriptions and lab tests.
4. Lacks Diversity to Test Boundary Conditions
One more limitation that data masking has is that it’s not ideal for testing boundary conditions. It can miss issues or key details when the data is applied to large patient populations.
FAQs
1. What are the key differences between data masking and de-identification?
Data masking obscures personal identifiers in the patient’s data while retaining the format and utility. De-identification on the other hand transforms data. It removes all identifiable information and prevents re-identification.
2. How can healthcare organizations ensure that de-identified data stays anonymous?
Healthcare organizations must use best practices such as Safe Harbor or HIPAA. That way, they can remove all possible identifiers present in the data.
3. What regulations apply to data privacy and security for healthcare organizations?
Regulations such as HIPAA and GDPR apply here. They emphasize patient consent, data minimization, and data safeguarding.
4. What is the other use of De-Identified data for Healthcare Providers?
De-identified data is used by healthcare providers for a myriad of purposes. Often, it’s used for research purposes, but it’s also used for the following:
- Retrospective research studies that span many care sites.
- Compiles regional datasets for public health analytics. This focuses on disease patterns and the efficacy of available treatments.
- Population health management initiatives
- Forming machine learning models that help in improving diagnosis.
Conceal your Patient’s Data through Data Masking
As healthcare starts to embrace digitization, data protection is becoming essential. Through data masking and de-identification, they can maximize patient data safety. Healthcare organizations must understand the ins and outs of each method. That way, they can create robust and compliant data protection programs.
