Standards for data and interoperability

Standards are documented, reusable agreements that solve a specific set of problems or meet clearly defined needs. Standards detail the language, concepts, rules, guidance or results that have been agreed. Standards are used when it is important to be consistent, be able to repeat processes, make comparisons, or reach a shared understanding.

Types of agreements that fall under data standards

Standards for data help with:

• Increasing interoperability: data can be shared more successfully if tools and processes are used that are developed in line with a standard for data exchange and a standard for vocabulary and/or a way of working.

• Improving comparability: using a standard to share vocabulary that makes language and concepts reusable and consistent can make it easier to compare data from different sources and draw conclusions.

• Increasing discoverability: using open standards can make it easier to find data assets structured in a consistent way across different systems.

• Enabling aggregation: open standards encourage the publication of new data and better quality data that is structured in a similar way, making it easier to combine datasets, and decreasing the cost and complexity of combining similar data from multiple sources. Open standards encourage the creation of new tools and services to take advantage of data that conforms to the standard.

• Enabling linkability: a standard to share vocabulary featuring common codes and identifiers for people, places, events and things allows data from multiple sources to be linked, which increases the ease with which diverse datasets can be combined to increase usefulness and insight.

Examples of health data standards include:

• Electronic healthcare records standards, like OpenEHR.
• Models to ensure data exchange of electronic healthcare records, like the FHIR API standards.
• Standards and models in specific areas such as clinical research, like the Clinical Data Interchange Standards Consortium.
• International Classification of Diseases data models, like WHO ICD11.
• Standardisation of medical terminology models, like the Snomed CT model.
• Data models for managing healthcare data, like Observational Medical Outcomes Partnership (OMOP).
• Open-source solutions for sharing, integrating and standardising data from multiple sources, like i2b2/transSMART for improving precision medicine.