Clinical Data Standard

A Clinical Data Standard is a Standard Specification that deals with clinical, medical or health care data elements.

AKA: Health Data Standard, Medical Data Standard.
Context:
- It can range from being a Clinical Vocabulary/Terminology Standard to being a Clinical Data Transport Standard.
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Example(s):
Counter-Example(s):
See: Standard-Developing Organization (SDO), American National Standards Institute (ANSI), International Organization for Standardization (ISO), Clinical Trial Data, National Cancer Institute's Enterprise Vocabulary Services (NCI-EVS) Program, CDISC Shared Health And Research Electronic (SHARE) Library, Clinical Data Interchange Standards Consortium (CDISC) RWD Connect Initiative.

References

2022a

(Facile et al., 2022) ⇒ Rhonda Facile, Erin Elizabeth Muhlbradt, Mengchun Gong, Qingna Li, Vaishali Popat, Frank Petavy, Ronald Cornet, Yaoping Ruan, Daisuke Koide, Toshiki I. Saito, Sam Hume, Frank Rockhold, Wenjun Bao, Sue Dubman, Barbara Jauregui Wurst (2022). "Use of Clinical Data Interchange Standards Consortium (CDISC) Standards for Real-world Data: Expert Perspectives From a Qualitative Delphi Survey". In: JMIR medical informatics, 10(1), e30363.
- QUOTE: The CDISC standards span the clinical research process and include standards for the exchange of nonclinical data (SEND), data collection case report forms (CRFs; clinical data acquisition standards harmonization (CDASH)), aggregation and tabulation (study data tabulation model (SDTM)), Biomedical Research Integrated Domain Group (BRIDG) logical model, and operational data model (ODM) for transport (Figure 1). In collaboration with the National Cancer Institute's Enterprise Vocabulary Services (NCI-EVS) program, CDISC has developed a rich controlled terminology that is linked to other common research semantics through the NCI-EVS tools. These standards, presented in data models, implementation guides, and user guides, are globally recognized and heavily used by the biopharmaceutical industry and some academic institutions.

**Figure 1:** Clinical Data Interchange Standards Consortium standards in the clinical research process. ADaM: Analysis Data Model; BRIDG: Biomedical Research Integrated Domain Group; CDASH: clinical data acquisition standards harmonization; ODM: operational data model; PRM: Protocol Representation Model; SDM: Study Design Model; SDTM: study data tabulation model.

2022b

(HIMSS, 2022) ⇒ https://www.himss.org/resources/interoperability-healthcare#Part2 Retrieved:2022-2-26.
- QUOTE: There are over 40 different SDOs in the health IT arena. Some entities create standards, such as Health Level Seven (HL7), Systematized Nomenclature of Medicine (SNOMED) International, and the Clinical Data Interchange Standards Consortium (CDISC). Others, like Integrating the Healthcare Enterprise (IHE), do not develop new standards, but rather bundle complementary base standards into IHE profiles that are used to define a specific function or use case, and then are balloted. This creates a scenario that helps drive adoption of the base standards by providing implementation guidance that describes how multiple standards can be used together to support interoperable health information exchange(...)
  In order to understand the types of health data standards available for use, informatics professionals organize these standards into the following specific categories: vocabulary/terminology, content, transport, privacy and security, and identifiers.

2011

(Richesson & Nadkarni, 2011) ⇒ Rachel L. Richesson, and Prakash Nadkarni (2011). "Data standards for clinical research data collection forms: current status and challenges". In: Journal of the American Medical Informatics Association, 18(3), 341-346. DOI:10.1136/amiajnl-2011-000107.
- QUOTE: Data-capture standards can facilitate efficacious development and implementation of new studies, element reuse, data quality and consistent data collection, and interoperability. Because of the protocol-centric nature of clinical research, opportunities for shared standards at levels higher than individual items are relatively limited compared with item-level standards. Nevertheless, disease-specific CRF standardization efforts have helped identify standard pools of data items within focused research and professional communities, and consequently helped achieve research efficiencies within their application areas. It will be interesting to see whether disease-specific efforts such as the NCI CRF standardization initiatives can remain in harmony with evolving national research standards specifications.

2004

(Aspden et al., 2004) ⇒ Philip Aspden, Janet M. Corrigan, Julie Wolcott, and Shari M. Erickson (2004). "Health care data standards" In: Patient Safety: Achieving a New Standard for Care. pp. 127-168.
- QUOTE: Data standards are the principal informatics component necessary for information flow through the national health information infrastructure. With common standards, clinical and patient safety systems can share an integrated information infrastructure whereby data are collected and reused for multiple purposes to meet more efficiently the broad scope of data collection and reporting requirements. Common data standards also support effective assimilation of new knowledge into decision support tools, such as an alert of a new drug contraindication, and refinements to the care process(...)
  In the context of health care, the term data standards encompasses methods, protocols, terminologies, and specifications for the collection, exchange, storage, and retrieval of information associated with health care applications, including medical records, medications, radiological images, payment and reimbursement, medical devices and monitoring systems, and administrative processes (Washington Publishing Company, 1998). Standardizing health care data involves the following:
  - Definition of data elements — determination of the data content to be collected and exchanged.
  - Data interchange formats— standard formats for electronically encoding the data elements (including sequencing and error handling) (Hammond, 2002). Interchange standards can also include document architectures for structuring data elements as they are exchanged and information models that define the relationships among data elements in a message.
  - Terminologies—the medical terms and concepts used to describe, classify, and code the data elements and data expression languages and syntax that describe the relationships among the terms/concepts.
  - Knowledge Representation— standard methods for electronically representing medical literature, clinical guidelines, and the like for decision support.

At the most basic level, data standards are about the standardization of data elements: (1) defining what to collect, (2) deciding how to represent what is collected (by designating data types or terminologies), and (3) determining how to encode the data for transmission.