Standards in healthcare data
Open accessAbstract
Abstract Clinical data interoperability requires shared specifications of meaning. This is the rationale for clinical data standards. Up until now, the adoption of such standards has been varied, although they are increasingly advocated in an area where proprietary specifications prevail, and semantic resources are geared to specific purposes and limited by boundaries of languages and jurisdictions. This chapter highlights the need of data standards in the context of the difficult and heterogeneous field of clinical data and the way how they are addressed by terminologies, ontologies and information models. It provides an overview of existing standards and discusses quality and implementation issues. Emphasis is also put on the eStandards methodology, which investigates needs for health data standards, supports the creation of standardised artefacts and defines actions for the implementation of standards.
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Data Science is being explored in various areas of healthcare, including medical imaging, drug development, drug delivery, testing, genomics, predictive diagnosis, and new drug development.
“Now it is being explored in various areas of healthcare, like medical imaging, drug development, drug delivery, testing, genomics, predictive diagnosis, new drug development etc [137].”
From Medical 4.0 technologies for healthcare: Features, capabilities, and applications (2022) · cites this paper as [137]
Data science is altering the delivery of healthcare.
“Data science is also altering the delivery of healthcare.”
From Medical 4.0 technologies for healthcare: Features, capabilities, and applications (2022) · cites this paper as [137]
With improvements in Data Science, precise diagnostic and medical measures are now possible.
“However, with improvements in Data Science, precise diagnostic & medical measures are now possible.”
From Medical 4.0 technologies for healthcare: Features, capabilities, and applications (2022) · cites this paper as [137]
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References 23 · Cited by 86
Research works printed but not linked: 1
- UMLS® Reference Manual. NCBI bookshelf. https://www.ncbi.nlm.nih.gov/books/ NBK9684/.
- Authors
- Schulz, S., Stegwee, R., & Chronaki, C.
- Affiliations
- Medical University of Graz (AT)
- European Committee for Standardization (BE)
- Health Level Seven International (BE)
- Corresponding author
- Schulz, Stefan
- Venue
- Fundamentals of Clinical Data Science
- Publisher
- Springer International Publishing
- Published
- 2018-12-22
- Pages
- 19-36
- OpenAlex
- W2905855069
- Cited by
- 86
- References
- 23
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 18 · 2023: 6 · 2022: 20 · 2021: 11 · 2020: 10 · 2019: 3 · 2018: 1
- FWCI
- 26.06
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Springer eBooks (publishedVersion, cc-by)
- Funders
- NFU Citriendfonds
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- clinical data standards, clinical data interoperability, terminology, ontology (information science), information model, standard adoption, semantic interoperability, health data quality
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