The healthcare industry thrives on data, from patient diagnosis to treatment plans, and this information fuels medical research and contributes to shaping better health initiatives. The digital repositories in the healthcare industry contain large volumes of patient data, which is confidential and has sensitive patient information. Under HIPAA regulations, this data needs to be safeguarded.
So, how can we carefully handle this data to ensure patient privacy? That is where data de-identification comes in. Let’s explore more about the de-identification of healthcare data.
What is Medical Data De-identification
Medical documents and reports contain sensitive information linked to patients. Data de-identification is a technique that alters healthcare data by breaking links between data and the individual it is associated with. It removes personal information from datasets (Medical records, reports, or media) containing patients’ Protected Health Information (PHI).
Medical Data De-identification allows data sharing for secondary uses — research and analysis while safeguarding patient privacy.
Is Data De-identification the Need of the Hour
De-identification is crucial for several reasons. It helps balance an individual’s privacy with the utility of data.
- Privacy Protection: It safeguards patient’s sensitive information and ensures their data is secure within the organization. An individual’s privacy remains confidential by removing or masking personal identifiers.
- Compliance and Regulations: De-identification helps organizations comply with privacy laws and regulations, mandating personal data protection. With de-identification, protecting personal data becomes possible.
- Risk Management: Reduces the risk associated with data breaches. De-identified data, if exposed, cannot be used to harm individuals, as it reduces the ethical and financial implications of a data breach.
- Collaborative Research and Analysis: De-identified data can be easily shared for collaborative research and analysis.
Medical Data De-identification Methods
Medical data de-identification, governed under HIPAA, has two main approaches to de-identify data.
Safe Harbor Method
This HIPAA-approved technique is focused on removing 18 identifiers from the checklist, such as names, dates (excluding the year), phone numbers, and medical record numbers, with other similar information linked to the patients. These identifiers can classify health information as Protected Health Information (PHI), which limits its use and disclosure. Data de-identification tools can detect such sensitive information and mask them. After applying this method, the data is considered de-identified and no longer subject to HIPAA’s privacy regulation.
Statistical Methods
These methods go beyond removing direct identifiers and minimize the risk of re-identification. There are several statistical methods for de-identifying data, and each method is suitable for different situations and requirements:
- Differential Privacy – Analyze data patterns without exposing identifiers.
- Generalization – This technique involves replacing specific values with broader categories, like using the birth year instead of the exact date of birth.
- Suppression – This entirely removes data points if there is a risk of re-identification or substitutes specific data points with generalized information.
- Redaction – This technique erases or masks identifiers using pixelation in all data records, including images or audio.
- Omission – Removes names and other direct identifiers from datasets.
- Hashing – Encrypts identifiers irreversibly, eliminating the possibility of decryption.
- Data Perturbation – This technique adds noise to data, making it difficult to pinpoint individuals.
- Tokenization – Replaces identifiers with unique tokens to preserve data structure while removing direct identities.
- Anonymization – Completely removes identifiers
- Pseudonymization – Replaces identifiers with unique, temporary codes.
Best Practices for De-identifying Healthcare Data
- Define the Purpose and Use
Define the intended use and function of the de-identified data with precision. It facilitates the selection of the most suitable methodologies and guarantees that the data will serve its intended purposes.
- Risk Assessment
Data is not identical. To examine the privacy risks associated with the data you are handling, you must perform an exhaustive risk assessment. Strictness of the data dictates the degree of de-identification required.
- Data Integrity
It is essential to achieve a balance. De-identified data should remain viable for research and analysis despite the importance of privacy protection. Ensure that the de-identification procedure substantially increases the utility of the data.
- Data Security and Governance
Embrace resilient data governance and security measures throughout the data lifecycle. Regular monitoring, access controls, and secure storage can prevent unauthorized access or breaches.
- Data Sharing Compliances
Establish transparent agreements for the sharing of de-identified data with external parties. In addition to stipulating obligations regarding data eradication and confidentiality, these agreements delineate provisions concerning re-identification risks, authorized data uses, and data security measures.
iMerit’s Medical Data De-identification Tool
Purpose-built for data de-identification and protected health information (PHI) removal, iMerit’s application leverages pre-trained NLP models to detect and protect sensitive patient information. It also has the option to add a verification and review layer with human expert teams in the loop for regulatory compliance and medical confidentiality.
The data de-identification solution by iMerit is fully automated, meets HIPAA regulations, and easily integrates and simplifies data sharing. This application includes:
- Automated workflow that streamlines data pipeline
- A completely customizable solution to meet project requirements
- Enhanced quality control for optimal results
- Monitor quality and project progress for reports and analytics
Learn more about it here.
