A brief overview of some terms relating to data and health

Generative AI is a type of technology that can create things, like text, images, music, or even videos. It works by learning patterns from examples and then using that knowledge to make something new. Think of it like a super-advanced creative assistant. It doesn't "think" like a person, but it’s great at spotting patterns and mimicking them to generate content that feels human-made. For example: If you give it lots of pictures of cats, it can learn what cats look like and draw a completely new picture of a cat. If you feed it text, it can learn how sentences are structured and help you write an email, a story, or even answer questions.

Data can be considered as anonymous or anonymized, if it is not possible to “single-out” individuals with the data provided and when the applied mechanisms to anonymise are irreversible. Anonymous data is not identifiable data from the start, while anonymized data is data initially identifiable but for which a process has been put in place to make it not identifiable anymore (GDRP: art. 4(5)).

Big Data refers to really large amounts of information—so much that it’s too big and complex for regular computers or tools to handle easily. The idea of Big Data isn’t just about the size, though—it’s about what we can do with it. By analyzing this data, we can: Find patterns: For example, spotting trends in shopping habits or traffic flows. Make predictions: Like forecasting the weather or predicting what products someone might buy. Solve problems: Such as improving healthcare by analyzing medical data or optimizing city planning by studying population data. In short, Big Data processing helps us make sense of the huge amounts of information being created every second and turn it into useful insights.

A clinical trial is a type of research that tests whether a new medicine, treatment, or medical approach is safe and works well for people. It’s like a carefully controlled experiment with real patients to find better ways to treat or prevent diseases. Clinical trials help discover better ways to treat diseases and improve healthcare.

Data can also be described as measurements or values that, when processed, yield information. The term digital data, in turn, refers to those data that are stored or processed by digital means. The collection and analysis of data enable personalized services, predictive analytics and improved outcomes. As the volume of data generated continues to grow, its importance in shaping policies, enhancing business operations and fostering technological advancements becomes ever more critical.

Data aggregation is when you take a lot of small pieces of information and combine them into a bigger summary. It’s like collecting puzzle pieces and putting them together to see the bigger picture. For example: If you collect data about daily temperatures from different cities, you can aggregate it to find the average temperature for the whole country. Instead of looking at every single detail, you can focus on the overall trends or patterns. it alse saves time: It’s easier to analyze summarized data than thousands of individual points. In short, data aggregation turns lots of small pieces of data into something more useful and easy to understand.

Data altruism is defined as ‘individual people and companies can voluntarily make data available for the common good'. It is focusing on the safe reuse of public-sector data and establishing a level playing field in the data economy by promoting data sharing and reducing barriers to data accessibility.

Data collection is the process of gathering information so you can use it to learn something, make decisions, or solve problems. The goal of data collection is to gather accurate and relevant information that can be used for analysis, decision-making, or improvement. It can be done in many ways: surveys or interviews; observing and recording behaviors; sensors, like weather monitors or traffic cameras. In short, data collection is just about gathering the facts you need to answer a question or achieve a goal.

A data controller is a person, organization, or entity that determines the purpose and means of processing personal data. The data controller has the responsibility for ensuring that personal data is handled in compliance with data protection laws. It is the person who decides why and how personal data will be processed.

Data culture is about how people in an organization or community value and use data in their everyday work and decision-making. Building a data culture isn’t just about having the right tools or technology—it’s about encouraging people to trust and use data as a natural part of how they work. In healthcare, it might mean using patient data to improve treatments and outcomes.

Data ethics focuses on the moral obligations that all societal actors have (or should have) when collecting, generating, analysing and disseminating both structured and unstructured data, human-provided data as well as the leverage of existing databases, including decisions driven by automated/artificial intelligence (AI) in relation to data in general and personal data in particular. It relates to general principles on which our societies are built and is highly relevant to building trust and ensuring fairness. It is not only about protecting data privacy or security. It is also about protecting citizens, customers and users from data practices by both the public and the private sector that adversely impact people and society.

Data extraction in research involves collecting and retrieving relevant data from various sources for the purpose of analysis, interpretation and deriving conclusions. In healthcare, data extraction plays an increasingly important role in patient care and predictive medicine as well as in medical research.

Data governance refers to the overall management of the availability, usability, integrity and security of the data that is collected, used and reused. It involves the establishment of policies, procedures and standards to ensure that data are managed effectively throughout their lifecycle within organizations as well as within and between countries.

A data holder is an entity (like a person, organization, or system) that stores or manages data. A data holder keeps data safe and organized. It decides how to manage, share, or protect that data based on rules or laws. Examples include banks (holding financial data), schools (holding student records), or even your smartphone (holding your photos and contacts).

Data literacy refers to the ability to comprehend, interact with, analyze, and reason through data. It involves interpreting data in various forms—whether it’s charts, database fields, dashboards, or other formats. Additionally, it encompasses the skill of effectively working with data on a daily basis, using appropriate analytical methods to extract meaningful insights while approaching the information with critical thinking. This includes not only the ability to ask insightful questions and challenge the data but also the crucial ability to communicate findings and interpretations clearly and efficiently to others.

Data maturity refers to an organization’s ability to effectively manage, utilize, and derive value from its data. It measures how advanced an organization is in handling data as a strategic asset, including how it collects, stores, analyzes, governs, and uses data for decision-making.

Extracting patterns from large quantities of unstructured data is referred to as data mining or data analytics. Increasingly this is now done through methods such as artificial intelligence and machine learning. In healthcare, data extraction plays an increasingly important role in patient care and predictive medicine as well as in medical research. For example, the demand for reliable health information increased significantly during the COVID-19 pandemic. Many health systems could not, however, ensure the flow of necessary data and information between providers and public health agencies, making it difficult to detect patterns and interpret them to obtain actionable insights.

Data ownership means having control over a piece of data and the right to decide how it's used. If you "own" the data, you get to make choices about who can access it, how it can be shared, or whether it can be deleted. Data ownership gives you the power to make decisions about the data. It can involve legal rights, responsibilities, and sometimes accountability for how the data is used. We rather speak about rights and obligations for both data subjects and data controllers, rather than using the word data ownership.

A data permit is an official approval that allows a person or organization to use specific electronic health data for certain purposes. This permit is issued by a health data access body and outlines what data can be accessed, who can use it, and under what conditions.

Data processing is what happens when raw data is taken and turned into something useful or meaningful. It starts with raw data, like numbers, text, or images. Tools or systems organize, analyze, or change the data to make it easier to understand or use. The result is something useful, like a report, a graph, or a decision. It’s like cooking: you take raw ingredients (data), follow a recipe (a set of steps), and end up with a delicious dish (useful information). For example, when you deposit a check using a banking app, the app processes the image of the check to extract information like the amount and your account number.

A data processor is an individual, organization, or entity that processes personal data on behalf of a data controller. The data processor operates under the instructions of the controller and does not determine the purposes or means of processing the data. Processing includes actions such as collecting, storing, organizing, transferring, or deleting data.

A data provider is an entity (a person, company, or system) that supplies or shares data with others. Think of it as someone handing out information to people who need it. The data can be shared for free or as part of a paid service, depending on the situation. The data can be raw (like numbers or text) or processed (like reports or graphs). Examples include weather services providing forecasts, businesses sharing market data, or apps offering user statistics. For example, a company like Spotify could be a data provider if it gives music streaming data to artists.

Data quality refers to how good or reliable the data is for its intended purpose. High-quality data is accurate, complete, consistent, and up-to-date, making it useful for making decisions or solving problems. If the data is of poor quality, it might lead to mistakes or wrong conclusions. For example, if a customer’s address is wrong in a shipping database, the package might go to the wrong place (low data quality).

A data set is simply a collection of related data, usually organized in a way that makes it easy to look at or analyze. A data set is a group of data points about a topic. It’s usually structured, meaning it’s arranged in a table or similar format. You can think of it like a spreadsheet where rows and columns hold information about something specific. Each row might represent an individual thing (like a person, product, or event), and each column represents a specific type of information about those things (like names, prices, or dates). A weather report showing daily temperatures, humidity, and rainfall for a month is another example of a data set.

Data produced by people should be available to the people. Good healthcare, scientific research for better health, development of medication, health products and medical technologies, good practices, are based on the usage of shared data and knowledge. Health insurance is historically based on the fact that people put money in a box, a cash register, and people can take money from that cash register when they are ill. We have generalised this to our current health insurance, which is based on solidarity, people pay contributions and taxes, which can then be used by everyone when and where necessary. Actually, the same principles apply for data solidarity, meaning data produced by people should be available to the people. Just as all citizens contribute to the healthcare system through taxes, so too should data be shared for the common good. Data solidarity foregrounds the public value when it benefits people and communities without the risk of invading citizens’ direct privacy.

Data sovereignty is about the rules and systems that ensure data is stored, controlled, stored safely and used securely, and how it can be made easy to share and move between systems, while respecting key principles of digital independence. Data sovereignty is closely connected to the idea of digital self-determination, which means individuals have the right and ability to exercise autonomy over their digital presence, data and online activities. It also includes the idea of groups or communities having control over shared data.

A data space is like a shared environment or ecosystem where different organizations or people can safely share and use data. It’s built on rules and technologies that make sure the data is secure, easy to access, and used responsibly. The goal is to share data efficiently while keeping it safe and respecting privacy. In healthcare, a data space might let hospitals, researchers, and companies share patient data securely to improve treatments, without violating privacy rules.

Data standardization is the process of organizing data into a consistent format so it’s easier to understand, use, and share. It ensures that everyone who uses the data is on the same page, even if the data comes from different places or systems. Standardization makes data more reliable, compatible, and easier to analyze. If one system records "New York" as "NYC" and another as "New York City," standardizing them ensures all records are consistent, like always using "New York City." This helps avoid confusion, improves accuracy, and makes data integration smoother.

Data storage refers to how information is saved and kept for future use. Data storage is about finding a safe place to keep information, whether on your device, in the cloud, or on external hardware like a USB drive, or information stored in structured systems (databases) used by businesses for managing large amounts of data. It ensures the data is accessible, secure, and retrievable when required.

Data subjects are the people that share their data. A data subject is a person whose personal information (data) is being collected, stored, or processed. A data subject is the individual the data is about. They have rights over their data, such as knowing how it’s used, correcting it if it’s wrong, or asking for it to be deleted (depending on the law/regulation, like GDPR). When you shop online, you are the data subject for your order history, payment details, and shipping information.

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.

The General Data Protection Regulation (GDPR) is a regulation created by the European Union to protect people's personal data and privacy. It sets rules for how organizations collect, store, use, and share personal information and gives individuals more control over their data. Organizations must handle personal data responsibly and securely. They need clear and explicit consent to collect and process personal data. Companies must explain what data they collect, why, and how they use it. The word RGPD is the French translation of the GDPR.

Health data access bodies (HDAB) will monitor and contribute to a consistent application of the rules throughout the EU, including by advising the European Commission, while cooperating with other EU bodies and stakeholders (e.g. patient organisations). They also monitor, examine and supervise compliance by data users and data holders with the requirements in the EHDS Regulation. This body acts as a facilitator to help organizations securely access and share health data for specific purposes like research, policy-making, and innovation. They ensure that sensitive health data is handled with care, privacy, and security at the forefront. In Belgium, this responsibility is held by the Belgian Health Data Access Body (Belgian HDA). The Belgian HDA helps organizations like hospitals, universities, companies, and research institutions to access health data in a regulated way. It ensures that health data is only used for legal, ethical, and scientific purposes, with all necessary safeguards in place to protect patient privacy.

HeDERA stands for Health Data Enabled for Re-use Across Belgium. HeDERA supports the mission of the HDA to facilitate secondary use of health, healthcare and well-being data in a safe, uniform, and transparent environment. The project will especially support Belgium’s efforts to enlarge the capacity of the HDA, ensuring its connection, alignment and technical interoperability with the European Health Data Space and other European developments. HeDERA is coordinated by the Belgian Federal Public Service (FPS) Health, Food chain safety and Environment, in partnership with Sciensano.

Health Level Seven International (HL7) is a not-for-profit, ANSI-accredited standards developing organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing and retrieval of electronic health information.

ICD serves a broad range of uses globally and provides critical knowledge on the extent, causes and consequences of human disease and death worldwide via data that is reported and coded with the ICD. It allows the systematic recording, analysis, interpretation and comparison of mortality and morbidity data collected in different countries or regions and at different times. It also ensures semantic interoperability and reusability of recorded data for the different use cases beyond mere health statistics, including decision support, resource allocation, reimbursement, guidelines and more.

Data from different sources becomes compatible and combinable with other datasets and tools or technologies. This implies using standardized formats, languages and vocabularies.

Logical Observation Identifiers Names and Codes (LOINC) is a database and universal standard for identifying medical laboratory and clinical test results. LOINC applies universal code names and identifiers to medical terminology related to electronic health records. The purpose is to assist in the electronic exchange and gathering of clinical results (such as laboratory tests, clinical observations, outcomes management and research).

Machine learning (ML) is a type of technology that allows computers to learn and improve from experience without being explicitly programmed. Instead of giving the computer step-by-step instructions, we provide it with data and let it figure out patterns or solutions on its own. A machine learning system uses examples (data) to understand and predict outcomes or make decisions. An everyday example is recommendation systems like Netflix suggesting movies.

Mature data refers to data that is well-organized, reliable, and ready to be used effectively for decision-making or analysis. It means the data has been properly managed over time and meets key quality standards, making it trustworthy and valuable.

Metadata is "data about the data". In other words, metadata is information that describes other data, helping to explain what it is, how it can be used, and where to find it. Metadata provides valuable information but needs to be properly managed, to avoid losing its benefits. Metadata gives the ability to manage and use data effectively.

Open data describes any data that can be freely accessed, (re)used and shared by anyone without restrictions. It aims to make scientific knowledge openly available and accessible to everyone. Open source refers to any programme whose source code is made available for use or modification as users or other developers see fit. Open source software is computer software that is developed as an open public collaboration and made freely available to the public.

Opt-in means that you are giving your permission or agreeing to participate in something. Imagine you receive an invitation to join a club or a newsletter. If you decide to join, you are "opting in." It’s like saying, "Yes, I want to be part of this."

The EHDS imposes Member States to develop an opt-out possibility for secondary use of health data. Opt-out means choosing not to participate in something or saying "no" to a service, activity, or use of your data. For example, if a company sends you promotional emails, you can opt-out to stop receiving them. In health care, it refers to the ability to decline or stop certain actions, like having your data used for specific purposes. GDPR and EHDS legislation give people an easy way to opt-out, but there is a risk to it. For example, if a company sends you promotional emails, you can opt-out to stop receiving them. If fewer people share their data, researchers might not have enough information to study diseases, develop treatments, and improve healthcare practices in general. Moreover, if certain groups of people opt out (e.g., specific demographics or regions), the data may become biased. This can lead to medical findings that only apply to a narrow population and exclude underrepresented groups. Without enough data, it becomes harder for public health organizations to track disease outbreaks and to respond to public health crises effectively. People who opt out might miss benefits, such as early disease detection or being part of cutting-edge clinical trials. Someone's health condition might go unnoticed if their data isn’t included in population-wide screenings or analyses. While protecting privacy is critical, finding a balance between individual rights and collective benefits is essential for advancing healthcare and saving lives.

Outcomes data refers to information that measures the results or effects of specific actions, interventions, or processes. It’s used to understand whether goals were achieved and to evaluate the effectiveness of programs, treatments, or decisions. Outcomes data answers the question: "What happened as a result?" It shows the impact of what was done, helping people or organizations decide what’s working and what needs improvement.

Personal data is any information that can identify a specific person, either on its own or when combined with other details. It is about different pieces of information, which together can lead to the identification of a particular person, like your name, social security number, email, or phone number.

A Pod is essentially a container where you can store all of your personal data—photos, documents, preferences, health information, and much more. It's a personal data vault that only you control. The idea behind is to give individuals control over their own personal data in a way that is more private, secure, and interoperable compared to the current centralized systems we commonly use (like social media platforms or cloud storage). With a Pod, you have the ability to control who can access and use your data and under what conditions. The data can be encrypted, ensuring that only those with the correct permissions can access it. This decentralization contrasts with traditional services (like Facebook or Google), where the company holds and controls your data.

Portability refers to the ability to easily move or transfer something from one place, system, or service to another. Data portability means that you have the right to get a copy of your data in a usable format and transfer it to another service or platform. It gives you control over your information and allows you to switch services without losing access to your data. In healthcare it is moving your medical records from one doctor or hospital to another.

Primary data refers to data that you collect yourself, specifically for a particular research project or purpose. It’s original data that hasn’t been previously gathered or analyzed by someone else. You collect it directly from the source. Primary data is gathering first-hand information directly from the field, experiments, surveys, or observations. It’s new data created specifically to answer your research question or solve a specific problem. If you want to know how well a new medication works, you might conduct a clinical trial and collect data on the patients’ responses and health improvements. This data is primary because you’re gathering it directly for your specific research.

Primary use refers to the direct use of health data in providing healthcare services, such as a doctor using a patient's medical records to make a diagnosis or determine a treatment plan, and also allowing the doctors in any member state to access the patients data if necessary.

Pseudonymisation means processing research data in such a way that they can no longer be attributed to a specific person without the use of additional information like a coding key. This usually involves replacing identifiers from the data with a pseudonym (GDRP: art. 4(5)).

The public value of data refers to the benefits that data provides to society as a whole. It’s about using data to improve public services, solve social challenges, and create opportunities that positively impact communities, not just individuals or private organizations. Public value of data means using data for the common good—to make life better for everyone. This includes improving healthcare, education, transportation, and more through better decisions and innovations based on shared information.

Quantum is an European initiative that aims to create a common quality and utility label for Europe that will allow its use in all countries for scientific and health innovation purposes.

Real-time data is information that is collected, processed, and shared immediately, as it happens. Unlike data that is stored and analyzed later, real-time data allows users to see and respond to what’s going on right now. Real-time data is like getting a live update. It’s happening now, and you can use it right away to make quick decisions.

Real-world data (RWD) refers to information collected from real-life settings, outside of controlled experiments or clinical trials. It comes from everyday activities and interactions, such as visits to the doctor, use of devices, or behavior on social media. Real-world data is information about what happens in real life, not in a lab or under tightly controlled conditions. It’s like observing people in their day-to-day lives instead of studying them in an experiment. It is about patient records, insurance claims, or data from wearable devices like Fitbits. For example: tracking how patients use medications in their daily lives and the outcomes they experience.

Secondary data refers to data that was collected by someone else for a different purpose, but can be used for new research or analysis. It's not original or newly gathered data, but rather data that's been previously collected and is now being repurposed. Secondary data is like reusing an old report or dataset that someone else made instead of gathering all the new information yourself.

Secondary use involves (re-)using health data for purposes beyond individual care, such as research, public health monitoring, or policy-making. This might include using data to track disease outbreaks or conduct studies to find new treatments. 

Sensitive data refers to information that is particularly private and confidential. It’s more private than regular data and needs extra care when it’s collected, stored, or shared. In many cases, sensitive data is subject to stricter privacy laws and regulations to ensure it is handled securely.

SHAIPED is an European initiative that will support the synergetic implementation of the EHDS regulation with the AI regulation.

SNOMED it short for Systematized Nomenclature of Medicine - Clinical Terms.SNOMED CT is a structured clinical vocabulary for use in an electronic health record. It is the most comprehensive and precise clinical health terminology product in the world. SNOMED CT gives clinical IT systems a single shared language, which makes exchanging information between systems easier, safer and more accurate. It contains all the clinical terms needed, from procedures and symptoms through to clinical measurements, diagnoses and medications.

A secure processing environment (SPE) is a highly secure infrastructure and isolated environment. A SPE provides enhanced security compared to using a laptop when dealing with sensitive data. It prevents data from being transferred from its source to the data user. The data stays at its location where the research question is brought to the data in a secured environment and only the anonymous, aggregated results are transferred to the researcher.

Synthetic data is artificially generated information that mimics real-world data. It is created using algorithms, simulations, or models rather than being collected from actual real-world events or observations. Data is generated based on predefined rules or logic that reflects expected patterns. It avoids exposing sensitive or personally identifiable information since it isn't based on real people or situations.

The TEHDAS2 joint action from the European Union prepares the ground for the harmonised implementation of the secondary use of health data in the European Health Data Space – EHDS. TEHDAS2 is carried out by 29 European countries and co-ordinated by the Finnish Innovation Fund, Sitra. The project started in May 2024, and it will end in December 2026.

The Unified Medical Language System (UMLS) brings together many health and biomedical vocabularies and coding standards for drugs, disorders, procedures, lab tests, medical devices, organisms, anatomy, genes, and more. It enables interoperability between computer systems.