AI and Machine Learning in De-identifying Healthcare Data: Future Trends and Applications

June 11, 2024

The healthcare industry faces the highest number of cyber-attacks due to the large amount of sensitive data it possesses and the critical nature of its operations. In fact, in 2023, data compromises in the healthcare sector reached their all-time high. As of May 2024, healthcare data breaches have affected thousands of individuals. 

Annual number of data compromises in the US healthcare sector (2005-2023) ~ Statista

With the increasing cyber-attacks, protecting patient information in healthcare is critical to maintaining trust and ensuring compliance with privacy regulations. Medical data de-identification is one such strategy that helps protect patient privacy by removing personal identifiers from healthcare data.

Moreover, the use of artificial intelligence (AI) and machine learning (ML) to enhance privacy and security is becoming prevalent in the healthcare sector. For instance, AI-powered platforms help healthcare organizations improve patient security while maintaining ethical standards by enabling real-time monitoring to detect and prevent breaches. 

But how is AI being used in the de-identification process? What are the trends?

Let’s find it out.

The Role of AI and Machine Learning in Healthcare Data Privacy 

AI and ML are transforming healthcare data protection by offering various applications that enhance privacy and security. For instance, AI techniques like biometrics and continuous authentication bolster access controls by verifying user identities repeatedly throughout sessions.

Moreover, AI apps monitor traffic patterns to detect emerging anomalies or attacks on Internet of Things (IoT) devices. This allows for quick isolation of compromised devices to contain the threats and prevent them from spreading further. Another use case is where AI/ML systems can recognize patterns preceding ransomware attacks, enabling early blocking of attacks containing malicious files. 

Several privacy-enhancing technologies (PETs) leverage AI and ML to further improve healthcare data protection. 

There are three different types of PETs:

  • Algorithmic PETs: These PETs modify how data appears or is structured, making it more difficult to identify individuals. They use tools like encryption and summary statistics to provide mathematical rigor and usability to the analyzed data. 
  • Architectural PETs: These PETs focus on the data structure or computation environments. They help exchange information confidentially without sharing underlying data. 
  • Augmentation PETs: These PETs use historical data distributions to generate realistic synthetic datasets. These can enhance existing data sources, such as improving a small dataset or generating entirely synthetic datasets.

Examples of Advanced Privacy Enhancing Technologies

Recently, various AI techniques have been proposed to enhance privacy in healthcare. Some examples of these privacy-enhancing technologies (PETs) include the following:

1. Federated Learning

This innovative approach allows multiple parties to train machine learning models collaboratively without sharing sensitive data. Each participating entity trains its model locally on its own data, and only model updates, not raw data, are shared with a central server or aggregator. 

While federated learning reduces the need for data sharing and protects some privacy by keeping raw data siloed, there is a risk of reconstruction. Therefore, federated learning is often combined with other PETs, like differential privacy, to enhance privacy protection.

2. Differential Privacy

Differential privacy is a mathematical definition of privacy that quantifies privacy leakage during data analysis using an epsilon (ε) value. Ideally, with an ε of 0, analysis results remain unchanged whether an individual is in the database. Higher ε values indicate more privacy leakage. Academia suggests ε values below 1 for strong anonymization, but setting the right ε value is challenging in practice.

This method ensures the results of queries are almost the same whether or not a particular entry is included by introducing noise to the results. ​

3. Synthetic Data Generation

Synthetic data generation involves creating artificial datasets that mimic the statistical properties of real data while ensuring individual privacy. The AI-driven generators are trained on real data and, once trained, can produce datasets that are statistically similar but vary in size. Because the synthetic data points do not correspond directly to the original data, re-identification of individuals is not possible.

Synthetic data has common applications in data anonymization, advanced analytics, and training AI and machine learning models. However, it may not fully capture the complex relationships in real-world data. 

4. Homomorphic Encryption

Homomorphic encryption enables computation on encrypted data without the need for decryption, preserving data privacy throughout processing. This technology allows for secure data analysis and sharing while maintaining confidentiality. By performing computations directly on encrypted data, homomorphic encryption minimizes the risk of exposing sensitive information during data processing and transmission.

However, since homomorphic encryption is extremely compute-intensive and still in the development phase, it has limited functionality. Some query types cannot be performed on encrypted data. 

Other AI Techniques for Data Privacy in Healthcare

Healthcare organizations are increasingly leveraging AI to enhance data privacy and security. Here are a few techniques that help protect healthcare data.

  • Anomaly Detection: ​​AI-driven anomaly detection systems monitor data access and usage patterns to identify unusual or suspicious activities. These systems can detect potential data breaches or unauthorized access in real time by recognizing deviations from normal behavior. This enables prompt intervention and mitigates risks to patient privacy.
  • AI-Driven Access Control: Traditional access control mechanisms can be augmented with AI to manage permissions dynamically based on real-time analysis. AI models can assess the context and behavior of users to adjust access rights, ensuring that only authorized personnel can access sensitive patient data. 
  • Predictive Analytics for Risk Management: AI-powered predictive analytics can forecast potential privacy risks by analyzing patterns and trends in data usage. This allows healthcare organizations to spot vulnerabilities early and take steps to prevent data breaches.

How is AI Being Used for Data De-identification?

Over the past few years, AI has transformed the de-identification process. AI-powered de-identification systems help protect privacy and enable healthcare organizations to use data correctly while protecting patients’ private data. 

The following is a list of several persuasive examples demonstrating how AI can be used for de-identification across the healthcare industry. 

1. De-Identification of Clinical Document Images Using Deep Learning

In 2023, researchers utilized AI to de-identify clinical notes, autonomously removing Protected Health Information (PHI) from Scanned Clinical Document Images. They proposed an end-to-end framework involving dataset annotation, Yolov3-DLA model training, and document layout analysis, achieving an impressive F1 score of 97.21%.

2. Clinical NLP APIs to De-identify EHR Data

Natural Language Processing (NLP) technologies have transformed healthcare data management. Among these advancements, the AutoICD Clinical NLP APIs stand out. This solution integrates AI and clinical NLP to provide tools tailored for de-identifying Electronic Health Record (EHR) data.

3. Synthetic Training Data for De-identification

Researchers addressed the challenge of sparse annotated datasets for NLP in Electronic Health Records (EHRs). They proposed using neural language models (LSTM and GPT-2) to generate artificial EHR text with named entity annotations. Through experiments, they demonstrated superior de-identification performance compared to rule-based methods. Combining real and synthetic data enhanced method recall without manual annotation.

4. Medical Text De-Identification using GPT-4

In response to concerns about confidentiality in digitized healthcare, researchers developed a novel de-identification framework called “DeID-GPT.” Leveraging large language models (LLMs) like ChatGPT and GPT-4, this framework automatically identifies and removes identifying information from medical text data. Compared to existing methods, DeID-GPT demonstrated superior accuracy and reliability in masking private information while maintaining text structure and meaning.

Future Trends in Data De-Identification 

As data privacy continues to be a significant concern, several trends are emerging in data de-identification. Let’s talk about some of the trends below. 

  • Increased Adoption of AI and ML: The widespread and growing adoption of AI and ML technologies is expected to play an essential role in data de-identification processes. AI and ML algorithms can enhance the efficiency and accuracy of de-identification techniques, allowing for more effective anonymization of sensitive information.
  • Growth in the Use of De-identification Software: With the growing demand for stringent privacy protection, the use of specialized de-identification software is anticipated to increase. These tools will help de-identify data at a large scale. 
  • Development of more Privacy-Preserving Technologies: Advancements in privacy-preserving AI will allow organizations to train machine learning models on sensitive data without disclosing the data. This will be useful especially in healthcare research and other use cases such as fraud detection. 
  • Blockchain-based Anonymization: Integrating blockchain technology into data de-identification processes holds promise for enhancing security and transparency. Blockchain-based anonymization solutions offer immutable data access and transaction records, ensuring the integrity and traceability of de-identified data sets.

AI-Powered Data De-identification for Healthcare

iMerit offers an AI-powered de-identification solution for sensitive healthcare information. Its purpose-built application uses pre-trained NLP models to detect and protect PHI. Healthcare providers can also add an optional verification layer with human-in-the-loop (HiTL) teams for additional compliance and confidentiality.

Key features of iMerit’s medical data de-identification include:

  • Automated Workflow: Streamline your data pipeline with a robust platform that seamlessly converts raw files to de-identified data.
  • Scalable & Customizable: Tailor iMerit to meet the evolving needs of your project.
  • Enhanced Quality Control: Leverage a fully automated or human-in-the-loop approach for optimal results.
  • HIPAA Compliance: Ensure safe and compliant radiology data across all 18 HIPAA-protected health identifiers.
  • Seamless Integration: iMerit’s intuitive import and export plugins simplify the data exchange process.
  • Analytics & Reporting: Monitor quality and track progress to ensure project milestones are achieved.

Experience a hybrid de-identification approach that combines automation with expert verification to meet diverse client needs. Try iMerit today!

Are you looking for data annotation to advance your project? Contact us today.