The Potential of Synthetic Data in Healthcare
The healthcare industry is undoubtedly one of the most complex and sensitive sectors when it comes to data access and processing.
The main reason why health data access and processing is so important is due to the extreme delicacy of the information that makes up this data. It contains elements such as illnesses individuals suffer or have suffered, the consequences thereof, the types of tests they have undergone, or the medicines administered to them.
For this reason, incorporating synthetic data generation into the lifecycle of healthcare data is considered a key element. it helps data scientists to create solutions capable of detecting and predicting diseases, or even collaborating in obtaining new cures and treatments without the legal and ethical bottlenecks of real-world data.
Data challenges in the Healthcare industry
The application of data-driven decisions involves a number of challenges when it comes to accessing private and sensitive data. Within the healthcare sector specifically, there are three main challenges:
1. Privacy and data regulations
Probably no sector demands more privacy than the health sector. The information that medical centres and companies deal with encompasses extremely intimate details. Key regulations include:
- General Data Protection Regulation (GDPR): In Europe, the GDPR sets out the guidelines for the collection, storage, use, and protection of patient information. It applies to all organizations requiring access to personal data for specific health activities.
- The Digital Care Act (DVG): This German regulation outlines that digital health applications must comply with strict data protection and security requirements, allowing doctors to prescribe digital health apps.
- The Health Insurance Portability and Accountability Act (HIPAA): This rule establishes national standards in the U.S. to ensure medical information is protected, setting checklists for data transmission, access, integrity, and auditing.
2. Healthcare data comes from different sources
A wide range of sources contribute to data collection, including hospital records, medical records, examinations, and wearable devices. Having such a variety of sources, the format in which the data is presented varies; some provide structured data, while others present unstructured data (like medical images).
Interpreting and collecting this data is vital for patient care. However, even with explicit consent, it takes significant effort to purge and structure this data. Complying with privacy requirements does not always guarantee that scientists have enough quality data for building prevention models.
3. Health data is unstructured
An estimated 80% of data within healthcare organizations is unstructured. It is critical to identify, extract, and anonymize this sensitive information wherever it exists—in systems, messages, or documents.
New AI and machine learning techniques are now able to identify code words by understanding the context around a document or image, including the clinical structure of the patients involved.
The Synthetic Data Solution
In recent years, synthetic data has received considerable attention as a method of protecting patient privacy and augmenting clinical research. It carries the ability to create fake patient records and medical imaging that are truly non-identifiable because the data does not relate to any real individual.
By using AI to automate the process of accessing patient data, healthcare organizations can save both time and money, while avoiding the massive risks associated with potential data breaches. The potential benefits for innovation in this industry are huge, and companies are just beginning to realize the possibilities.
