A brief overview of some terms relating to data and health

Data transfer is the process of moving data from one place to another. This could mean sending data between devices, systems, or locations. Think of it like delivering a package—it’s about getting information from point A to point B. It’s how data travels over networks, like when you send an email, upload a file, or access a website. A cross-border transfer is also possible. It is transferring data between countries, often subject to laws and regulations to protect privacy and security.

A data user is a person, organization, or system that accesses and works with data. A data user is anyone who interacts with data for a specific purpose. They use the data to analyze, make decisions, or perform tasks. They could be reading, editing, analyzing, or sharing the data. A student using online research data for a project is a data user. Data users have responsibilities, like handling data responsibly and respecting privacy laws or guidelines.

DCAT-AP stands for Data Catalog Vocabulary Application Profile. It is developed and maintained by the European Commission for an Interoperable Europe. It is a standardized approach for describing public sector data sets, making it possible for data from diverse sources to be easily located, accessed and reused by various applications and stakeholders. It provides a common basis for standardized description of metadata and dataset within Europe to improve interoperability and make it easier to exchange data across borders and domains.

De-identification is the process of removing or masking personal information from a dataset so that individuals can no longer be easily identified. It’s a way to protect privacy while still allowing the data to be useful for analysis or sharing. It’s like blurring someone’s face in a photo—you can see the picture, but you can’t tell who the person is. A hospital might de-identify patient data by removing names and medical record numbers before sharing it with researchers.

The general objective of the European Health Data Space (EHDS) is to have easier and more secure rules, structures, and processes across the EU Member States to access and share electronic health data across borders. The EHDS regulation provides clear rules how health data can be accessed, how transparency should be created about the usage goals, and about citizens’ rights to clear information and opt out for certain usage goals. The EHDS also provides a clear legal framework essential for efficient health data exchange across borders, by ensuring that health data are standardised and interoperable at the EU level. By making health data more accessible at the EU level, the EHDS removes obstacles to data sharing across borders, allowing researchers to conduct transnational studies. These are important for rare diseases where data is limited, and shared data from across the EU would be greatly beneficial to support research in this area. It also secures secondary data use, ensuring that researchers, policymakers and patient organisations can tap into EU health data for scientific research with full respect for a person’s privacy.

An Electronic Health Record (EHR) is an electronic version of a patients medical history, and may include all of the key administrative clinical data relevant to that persons care under a particular provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports. EHRs are designed to be used by multiple care providers and healthcare organizations. This facilitates sharing accurate data over time. Without EHRs, patients and/or care providers would still bear the administrative burden of arranging the transfer of the medical records to another care provider or multiple care providers.

Data FAIRification refers to the process of making data compliant with the FAIR principles, which aim to enhance the usability of data by making it Findable, Accessible, Interoperable, and Reusable. Findability of data means that it becomes easier for data users (both humans and machines) to locate the data they need. Increasing the accessibility of data means that once data users have located the data they need, they also know how to access it. Increasing the interoperability of data means that data from different sources becomes compatible and combinable with other datasets and tools or technologies. Increasing the reusability of the data means that the data can be used in future different research.

Federated data analysis is a method of analyzing data stored in different locations or systems without moving or centralizing the data. Instead of bringing all the data to one place, the analysis is performed where the data is stored, and only the results are combined. This approach is often used to maintain data privacy and security. This way, the data stays in its original location, and sensitive information is protected. Imagine you have puzzle pieces spread across different rooms. Instead of collecting all the pieces into one room, you go to each room, analyze the pieces, and then bring back just the conclusions to see the full picture. In healthcare, hospitals can use federated data analysis to study patient trends without sharing sensitive patient records. Each hospital analyzes its own data locally, and only the aggregated findings are shared.

A federated data platform is a system or architecture that enables decentralized data management and collaboration across multiple locations, systems, or organizations while maintaining data ownership, security, and privacy at the source. Instead of centralizing all the data in a single repository, a federated platform allows decentralized data storage, meaning data remains distributed at different organizations or devices, but makes it accessible and usable across the participating entities. In essence, a federated data platform is a powerful way to enable collaboration and analysis across distributed data ecosystems without compromising privacy, security, or ownership.

FHIR stands for Fast Healthcare Interoperability Resources. It is an interoperability standard developed by HL7 (the Health Level 7 standards organization) designed to enable the exchange of healthcare data electronically between different computer systems in the healthcare industry, regardless of how it is stored in those systems. It is a set of rules and specifications for the secure exchange of electronic health care data. It is designed to be flexible and adaptable, so that it can be used in a wide range of settings and with different health care information systems.