Glossary

Adressing service

Generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), which indicates which parties participate in which functions and how to address them

Anonymize

Anonymized data are personal data that is processed so that the identifying features are irreversibly removed/hidden so that it is no longer possible to trace it back to a person

API

An API (Application Programming Interface) is used by software components to exchange data in a formalized way

Application overview

Generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), which provides an overview of who has done what with personal health data.

Authorative source

An authorative source is a record of data that is considered the primary source of that information.

Catalogue

The roles article refers to the Data Guide Role. This refers to the Catalogue, as implemented in the Health-RI hub as a National Health Data Catalogue. This role performs services that allow a data user to find existing health data that can be used for research with the functions

• Collect decentralised metadata; with the Find and discover existing health data action, the catalogue requests the latest status from all FAIR data points that are registered in the FAIR data point Register.

• Publish metadata

• Make metadata discoverable

Central data provider review commitee

Generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), responsible for:

• Creating and managing generic terms of use

• Testing whether a analysis to be performed meets the conditions of use of the requested dataset(s)

• Testing (cross-source) data request for traceability of personal data

• Keeping track of case law

Citizen

In the context of the Health-RI ecosystem, a citizen is an individual who controls his or her data in the context of research and innovation.

Related roles: client, patient, subject

More: https://health-ri.atlassian.net/wiki/spaces/HWV4/pages/793618095/Roles#Citizen

Clinical data

Clinical data is data that has to do with the health or treatment of people. They may come from clinical research, where treatments or other medical interventions are tested on human subjects, or from clinical practice, where Healthcare Professionals or patients collect data about their patients or in the case patients themselves.

Clinical (patient) research

In this research, an attempt is made to learn more about the condition by conducting research with patients. Examples of clinical research are the collection of clinical data to gain a good insight into the course of a condition. Also comparing the most efficient epilepsy or sleeping medication or identifying the best communication technique are examples of clinical research.

Source: https://encore-expertisecentrum.nl/

Cohort

A group of people who share certain characteristics or traits and are the subject of a study or dataset.

Data-archiver

A data archiver archives data in order to guarantee continuity of data provision in the event of the potential termination of original data source of a data producer.

The data archiver role is one of the roles that must be done by the data retention role. The data holder is the controller of the data that the data archiver archives.

Datacentric approach

A way of looking at where the data is central and not the purpose for which it was collected or used.

Data cleansing

The process of detecting and correcting or removing corrupt, duplicate, incomplete, incorrect, or irrelevant data from a dataset, table or database.

Data dictionary

A data dictionary is a codebook or code list: an agreed set of values for a metadata field.

Data Governance Committee

The Data Governance Committee is preferably one authorized coordinating party that draws up a standard data dictionary for a relevant domain, together with domain experts.

The data governance committee determines and manages:

• Standardized treatment process and protocols

• Minimal dataset with associated metadata set

• Unity of language for relevant dataset (coding and modeling); In which the relevant data governance committee is the mouthpiece to the (international) standard holders to implement possible harmonization adjustments, in order to prevent loss of quality in data transformation as much as possible.

• FAIR metadata templates

• Mapping definitions between different target groups within unity of language.

The Data Governance Committee is related to the generic features

More https://health-ri.atlassian.net/wiki/spaces/HWV4/pages/793618095/Roles#Generic-features

Data guide

The data guide provides services that allow a user to find existing health data that can be used for the research with the functions

• Acquire decentralized metadata; under the find and discover existing health data action, the data guide requests the latest state of affairs from all FAIR data points that are registered in the addressing service.

• Publish metadata

• Making metadata traceable

Also known as: catalog

Data holder

A data holder is a person or organization responsible for the management and storage of health data within the Health-RI ecosystem for research and innovation. This can be, for example, a (collection of) hospital(s), a healthcare institution, a government agency, or a research organization that manages and stores health data.

The data holder is a collection of roles that correctly make original health data available to the Health-RI ecosystem:

• Local review committee data-offering

• Data producer

• Data Preparator

• Data archiver

• Data Provider

• Federated analysis

More https://health-ri.atlassian.net/wiki/spaces/HWV4/pages/793618095/Roles#Data-holder

In GDPR the term 'data controller' is used as well for the data holder

Data management plan

A Data Management Plan (DMP) is a formal document developed at the beginning of your research project that describes all aspects of data management, both during and after the project. It contains, among other things:

• what data will be collected (type, size)

• where the data is stored, who has access to it and how access is arranged

• how often the data is backed up

• how the data is documented

• the version control strategy and the folder structure that will be used

• whether there are privacy and ownership issues

• whether and how the data will be archived and shared.

source: uu.nl

Data minimisation

Data minimisation means that a dataset does not contain more data than is necessary for the purpose for which the dataset was requested.

Data Preparator

Based on the data-centric principle, the data preparator separates the original health data from the original application and prepares the original health data, where necessary, for multiple use (including for use for research and innovation) in a persistent data platform.

The data preparator role is one of the roles that must be filled by the data holder role. The data holder is the controller of the data that the data preparator transforms.

Meer Roles | Datapreparator

Data processor

Within the Health-RI ecosystem for research and innovation, there are two forms of "Data Processing":

• Making data suitable for multiple use at the data holder: Preparing data

• Analyzing data at the data user: data analysis processor

Data producer

The data producer produces, creates and stores the original health data, preferably with

• Context information

• Terms of Use

• Coding and modeling according to unity of language

• Quality and usability characteristics

• Version control

For original data sources, where the data user does not have the basis to know personally sensitive information (researchers data sources), the data producer will store the original health data in a pseudonymized or anonymized manner.

The data producer role is one of the roles that must be done by the data holder role The data holder is the controller of the data that the data producer produces.

Dataprovider

The dataprovider sets up the delivery process of the dataset based on the terms of use of the data preparator and the target transformation.

The dataprovider role is one of the roles that must be done by the data holder. The data holder is the controller of the data made available by the data provider.

More Roles | Dataprovider

Data-request register

Data request and issue register, in which the reseearch data request service manages the requests.

Dataset

A dataset is a collection of similar data relating to a group of data subjects. The collection has a certain uniformity, such as the presence of certain data items or data types, and similar data acquisition and processing techniques, so that it makes sense to view the dataset as a group that can be drawn upon for reuse.
A dataset can be static, which means that the dataset no longer changes after delivery. On the other hand, a dataset can also be dynamic: the dataset is then subject to change and/or can be supplemented. In that case, the list of data subjects described by the dataset may also change

Datasubject

A datasubject is an identifiable natural person, whose health data is collected, processed, stored or shared within the Health-RI ecosystem. This could be, for example, a patient whose medical data is stored and shared between different healthcare professionals, researchers or government agencies.

Data user

A data user is a natural person who has access to health data within the Health-RI ecosystem for research and innovation, for the performance of specific tasks or purposes. For example, a data user may be a researcher who needs data for scientific research, a public authority that needs data for monitoring public health or a healthcare professional who needs access to health data for diagnosis or treatment.

Data user institute

A data user institute is the organization to which the data user belongs. The institute is legally responsible/liable for what happens to the data and also the contracting party for any agreements.

Data visiting

A form of data processing on one (existing) dataset at one location (e.g. Plugin)

(De-) identification service

Generic feature which pseudonymizes or anonymizes data.

Distributed processing

In the case of distributed processing, the data user instructs from a central point to perform processing on one or more environments that are set up under the control of each of the data holder(s) concerned.

Economy of scale

Economies of scale refers to the phenomenon whereby average costs per unit decrease as the scale of production increases.

FAIR Data Point

FAIR Data Points are used to describe your data sets in a FAIR way, using standard metadata and make them available through simple web protocols.
More info: https://www.fairdatapoint.org/

FAIR principles

FAIR is an acronym used in the context of data management. FAIR stands for Findable, Accessible, Interoperable and Reusable.
More info: What is Fair?

Feasibility study

A feasibility study is an assessment that determines how likely it is that a dataset will provide added value to a study. If a study concerns, for example, the elderly and they are not included in the dataset or are only included to a limited extent, the researcher may decide not to request the dataset in question.

Federated analysis coordinator

The federated analysis coordinator provides the tooling to submit the analysis question (in the case of a federated analysis) and ensures that the analysis question becomes available to the federated analysis implementers and that the results of the federated analysis are collected.

Federated analysis performer

The Federated analysis performer ensures that an applicant's assignment federated analysis is executed locally at a data holder, by

• Request action on (meta)data from data provider

• Receive data from data provider within environment data holder

• Calculate data using computing power within environment data holder

• Return results calculation to applicant federated analysis

Federated processing

Federated processing is a way of processing that does not depend on a centrally coordinated structure, so has no single point of failure or single point of power.

Generic Features

The nodes from the Health-RI ecosystem use generic features. Preferably, these generic features are also used in healthcare itself (primary use). This promotes transparency and consistency of the application of these services and avoids unnecessary multiple costs for implementing and managing the solutions.

The following generic features can be provided to the nodes of the Health-RI ecosystem:

• Identity registers

• System manager Health-RI

• Data Governance Committee

• Data inverter

• (De-)identification service

• Linking service

• Terms of use service

• Application overview

• Addressing service

• Central review committee data-offering

• Research data request service

• MDR certification provider

Health data

Health data is healthcare data + research data + all other data applicable to health (IoT data and citizen-generated data).

Health data access body (HDAB)

A legal entity designated by a Member State of the European Union to manage access to health data for research purposes. The role of an HDAB is to facilitate access to health data while ensuring the protection of personal data and privacy rights.

source: EHDS Regulation

Healthcare data

Healthcare data is data that is used to support the care processes and/or is recorded during the care processes Healthcare data is a form of health data.

Healthcare Organisation

A healthcare organization is a provider and/or recipient of data and services in the field of research and innovation.

Healthcare Professional

A Healthcare Professional is a natural person who provides medical or health-related services to patients or clients, whose purpose is to promote the health of patients or clients, to prevent, treat and cure diseases, and to support them in maintaining or restoring their functional skills and quality of life.

Health-RI ecosystem

The integrated health data infrastructure for research and innovation that complies with the Health-RI Architecture.

The Integrated Health Data Infrastructure for Research and Innovation refers to the totality of all parties involved and elements in scientific research and how they interact with each other. This includes not only the technological resources and facilities, but also the social, cultural, economic and political environment in scientific research. Within the ecosystem, involved parties can exchange health data with each other in a secure way.

Health-RI foundation node

The Health-RI Foundation Node is a Nationwide node, which initially provides for the data user role. In time, this node will also provide the data-holding role for parties who cannot, may and/or do not want to make their original health data available through existing nodes.

Horizontal partitioning of data

With horizontally partitioned health data, each source contains data from different patients/ citizens/ participants (the rows) and each source contains the same characteristics (the columns)).

IAA

IAA stands for Identification, Authentication and Authorization. These are steps in the access control process.

See the articles Identification and Authentication Service and Authorization Service for more information.

Identity repositories

Different target groups use the Health-RI ecosystem. These different target groups each have specific roles and attributes. The NEN standard is currently looking at identification and authentication of healthcare professionals, which also faces the challenge that not all different groups of healthcare professionals fall under the UZI identification register. The Health-RI Foundation advocates a national approach or standardization of identity registers, in which the design of roles and attributes is looked at across domains and internationally. This also makes it easier to combine socio-economic information from ODISSEI with health data from the Health-RI ecosystem for prevention purposes.

The target groups that we distinguish for the Health-RI ecosystem are:

• Healthcare professionals

• Researchers

• Policy makers

• Citizens

Identity registers fall under the Generic features

Infrastructure

Infrastructure is the set of facilities (organization, process, information, application and technology) for processing, storing, securing, managing and transporting digital data.

Innovator

The Innovator is a special researcher who produces reusable research results and algorithms for data users who want to have simplified access to health data, such as healthcare professionals, policy makers and citizens. Tasks that an innovator performs are

• Research;

• Certify MDR on Algorithms

• Publish Algorithms;

Linking service

Generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), to be able to combine data of a person over various sources (data linking) based on identifiers or keys.

Local review committee data providing

The local review committee data providing

• draw up in advance terms of use for the provision of the original data on data, preferably recorded with the data producer, or else with the data preparator.

• Performs review of a data research request for the original data source

Local review committee research

The organization to which the researcher or the data is affiliated has a review committee that determines whether studies that affiliated researchers want to start can be assessed on ethical and legal grounds.

Make data FAIR

This process involves assigning unique and persistent identifiers to the data, enriching metadata, and registering or indexing this data in searchable resources.

Making data comply with FAIR principles

This refers to the process of assessing existing data against the FAIR principles and making any necessary adjustments to meet these principles.

This may involve adding missing metadata, improving the discoverability of data, standardizing formats, and so on.

The goal is to transform the data so that it fully meets the FAIR criteria.

Mapping service

The mapping service is a generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), which ensures that data is consistently translated between the different target groups of data holders and data users. In case of change in coding and modeling of standards within the unit of language, it is implemented once here.

MDR Notified Body

Generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), which grants Medical Device Registry (MDR) certification

Medical Research Ethics Committee (MREC)

A Medical Research Ethics Committee (MREC) assesses medical research with test subjects under the Medical Research Involving Human Subjects Act (WMO). The purpose of this assessment is to safeguard the rights, safety and well-being of the participants. A study can only be conducted after an approved MREC as issued a positive recommendation.

See also: TZW, CCMO

Metadata

Metadata is data about the data. It gives people and systems information about the context of the data. This context is needed to make the data easier to find, understand, and relevant. It also helps to get a better grip on the data.

Metadata provides people and systems with information about the context of the data. That context is necessary to make the data more findable, understandable and relevant. It also helps to get a better grip on the data.

Metadata can concern both datasets and data points.
Dataset metadata describes, for example, how much of which type of data is contained in a dataset. See Minimal (meta)dataset for further description.

Metadata about data points describes the context about a specific data point, for example a blood pressure measurement or image recording.

The term metadata can have different meanings in different processes. Something that is 'metadata' in the primary care process can be 'data' in secondary use, for example the time or the measuring device that is recorded during a blood pressure measurement in the primary care process.

Multiple use

Use both within healthcare (so-called primary use) and outside it, such as research and innovation (so-called secondary use). The responsibility for making data suitable for multiple use is shared between the data holder and the data user (in the role of data holder).

Alternate term: further use

National contact point for secondary use of electronic health data

An organizational and technical gateway enabling the cross-border secondary use of electronic health data, under the responsibility of a Member State of the European Union. The NCP-2nd-use is also responsible for ensuring interoperability and compatibility and for providing guidance and support.

source: EHDS Regulation

National node

A National node provides the role of connecting and/or applying nationally oriented specific data sources from the Health-RI ecosystem. The Health-RI Foundation is currently exploring a national categorical hub specifically for oncological health data.

Node

A node forms the access to the Health-RI Ecosystem of the role of Data holder and/or Data user. There are several types of nodes:

• Regional nodes

• National nodes

• Health-RI foundation node

More https://health-ri.atlassian.net/wiki/spaces/HWV4/pages/793618173

Onboarding

The term onboarding is used in the context of metadata and involves registering the metadata of a dataset with a FAIR Data point.

Ontology

Terminology file characterized by the presence of specified relationships between the concepts; There are not only synonym relationships and hierarchical relationships such as in a thesaurus, but also all types of relationships are broken down

Open standards

Open standards are standards that are publicly available, developed and approved through a process based on collaboration and consensus with the community. Open standards facilitate interoperability and data exchange.

Payment service

A service that can charge a user an amount depending on the services/data sets purchased

Persistent dataplatform

A persistent data platform is a central and durable storage environment for data that must remain available and consistent for a long time. Research data is stored within this environment and this data is made available from this environment

Primary use

Use of health data on behalf of direct patient treatment.

Processing centrally

With Processing centrally, the data from different data holders is processed in one central place. The administrator of the environment is not necessarily affiliated with the same institution as the data user. In general, the processing environment will be a virtual environment that will be set up specifically for the analysis or the directly encompassing project and will no longer be accessible when the analysis or project is complete.

In the environment, the data user is given the opportunity to analyze the data from various sources together (by downloads or remote mounts or a combination). The data user can also collaborate with colleagues in the environment. In the central processing environment, the data user can select and use processing tools, or add their own processing tools. Think of sectoral facilities, (commercial) cloud facilities (such as myDRE of anDREa, SURF research cloud, a SURF HPC cluster, or commercial platforms such as Amazon AWS, Google Cloud or Microsoft Azure provided that they meet security requirements).

Processing data

Within the Health-RI ecosystem for research and innovation, there are two forms of “Processing data”:

1. Making data suitable for multiple use at the data holder: Data preparation

2. Analyzing data at the data user: data analysis processor

Processing environment

A processing environment is an environment where research data can be processed. The digital processing environments exist with different characteristics. For example, you can have a local processing environment, a central or a distributed one.

Processing facility

A processing facility is an appliciation component in the central analysis processing environment (e.g. a virtual machine or some software)

Pseudonymization

Pseudonymization is the reversible encryption, replacement and/or deletion of (directly) identifiable personal data in a dataset to protect the privacy of the data subject

Regional node

A regional hub provides the role of connecting the region, by performing roles on behalf of regional parties from economy of scale, as a shared service center. Currently, the Health-RI ecosystem has eight regional nodes, which perform the role of Data Holder for the benefit of their region and also different Data User roles per regional node.

More https://health-ri.atlassian.net/wiki/spaces/HWV4/pages/793618173/Nodes#Regional-hub

Registry Holder

A Registry Holder is a holder of an authorative source.

Requester federated analysis

Together with the applicant for federated analysis, the researcher can instruct the federated analysis executor(s) to perform a local analysis calculation at the data holder.

Research data

Research data is data that serves as input for a study, which is generated during a study and/or that is the result of a study. Care data, if suitable for use for research, can serve as research data after conducting a target transformation.

Researchdata request service

Generic feature, both for healthcare itself (primary use) and for research & innovation (secondary use), responsible for:

• Determining the identity and authenticity of the researcher

• Determining permission to start research

• Recording data research request and environment

• Sending data research request

• Administering data research request

Researcher

Researchers are natural persons engaged in conducting scientific research in the field of health care. This can be, for example, research into new treatments or therapies for diseases, discovering the causes of diseases or improving the quality of care for patients. Their work includes designing research protocols, conducting experiments, and analyzing data.

More Roles | Reseacher

Research Organisation

A research organization is a legal entity, a provider and/or recipient of data and services in the field of research and innovation. The data and services are primarily used in the investigation process. In addition, it is the ambition of the Health-RI foundation that this data from the learning healthcare system will also be part of the healthcare itself.

Research register

A register that records which studies exist and what condition the studies are in (such as PaNaMa)

Review

Medical-scientific research using data or bodily materials that come from people is often tested, especially in the areas of ethics and privacy, before implementation. The testing of health research that is not subject to the WMO is not required by law. This is in contrast to medical-scientific research involving humans, which falls under the WMO (Medical-Scientific Research Involving Humans Act). This last category is mandatory tested by an METC (Medical-Ethical Review Committee).

Which studies from the first category, non-WMO-required research (nWMO research), are tested currently differs between institutions. The composition of the committee and the assessment criteria also differ between institutions. To reduce these differences, VWS has supported a project in which field parties have developed an assessment framework for nWMO research. Health-RI has taken over and is coordinating the follow-up process, in which implementation steps will be taken.

Review committee

A review committee (also known as a Data Access Committee or DAC) has the responsibility to review and approve or reject requests for access to data. Examples are:

• A local review committee is responsible for assessing data applications for one data holder.

• A central review committee assesses applications that relate to multiple data holders.

• A national review committee assesses applications from outside the Netherlands. The national review committee is part of the Health Data Access Body (HDAB).

Secondary use

Use of healthdata outside the primary healthcare process, for example for research, innovation, quality

Secure processing environment

A secure processing environment, which meets the requirements of the Health-RI ecosystem and provides the following services:

• Processing facility

• Storage capacity associated with processing

• Tools facility

• Data linkage

• Process analysis calculation

In general, the secure processing environment will be a virtual environment that is specially set up for the analysis or the directly encompassing project, and will no longer be accessible when the analysis or project is complete.

In this environment, the data user is given the opportunity to analyze data from different sources together (e.g. via downloads or remote mounts or a combination). The Data User can also collaborate with colleagues in the environment for this purpose. In the secure processing environment, the data user can select and use processing tools, or add their own processing tools.

A secure processing environment includes sectoral facilities, (commercial) cloud facilities (such as the myDRE from anDREa, SURF research cloud, a SURF HPC cluster), or commercial platforms such as Amazon AWS, Google Cloud or Microsoft Azure, provided they comply with privacy requirements. and safety requirements.

Shared service center

A Shared Service Center (SSC) is a central department within an organization that provides specialized services and support to various internal departments or business units. The purpose of an SSC is to promote efficiency, save costs and improve the quality of service through centralized and standardized processes.

Standardise

Choose by design for generic services and standards as components in stead of specific solutions to enable a node to join the Health-RI Ecosystem

Storyline

A Storyline is a short, simple description of an application of the Health-RI ecosystem for research and innovation of an end user in outline. A storyline is not a functional description, but makes it clear what an end user wants, or needs and also why that is necessary.

System Administrator Health-RI

The System Administrator Health-RI is a Health-RI ecosystem specific Generic feature that provides services such as:

• Manage contract template repository; To prevent consistency and reinvention, proven real-world examples are centrally tracked and shared.

• Authorization Policy.

• Architecture governance; managing the Health-RI ecosystem architecture, through this Wiki with accompanying consultation cycle.

Target transformation

The process of transforming healthdata from its original purpose, which is for example to support patient care, to other purposes, such as research or analysis.

Healthcare data for example often contains personally identifiable information about patients and should be handled with care. To ensure patient privacy and confidentiality, healthcare data should be anonymized or pseudonymized before being used for purposes other than patient care.

On the other hand, research data that is for example gathered using an EDC tool may need target transformation to be suitable for secondary use.

Technology Transfer Office

Technology Transfer Offices (TTOs) within a university or university medical Center manage its intellectual property rights (IP) and the transfer of knowledge and technology to public and private partners.

Terms of use service

The terms of use service is a service in which a citizen records preferences that describe who is allowed to do what with the health data of that citizen. This is a generic service used both for healthcare itself (primary use) and for research & innovation (secondary use).

Ticketing booth

A place where participants in the Health-RI ecosystem can ask their questions

Tool provider

A company that specializes in providing tools/software used to handle health data.

Unity of language

Unity of language does not prescribe that every target group should use the same way of coding and modeling. Loss of quality while mapping the various encodings and modelling used, should be avoided as far as possible by harmonizing or, if necessary, standardizing the different encodings and modelling methods.

source: RIVM ‘grondlegger van eenheid van taal’.

Vertical partitioning of data

In the case of vertically partitioned data, sources contain data with different characteristics (the columns) of the same patients (the rows).