> For the complete documentation index, see [llms.txt](https://docs.bcbi.brown.edu/codiac-for-health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bcbi.brown.edu/codiac-for-health/foundations/health-data-standards.md).
# Health Data Standards
"*Health data standards are key to the U.S. quest to create an aggregated, patient-centric electronic health record; to build regional health information networks; to interchange data among independent sites involved in a person’s care; to create a population database for health surveillance and for bioterrorism defense; and to create a personal health record*." \[[1](#references)]
Health data standards and interoperability are essential for the seamless exchange and interpretation of data within and across systems and organizations to address the issue of having "too many ways to say the same thing" (e.g., [**Health Data**](https://docs.bcbi.brown.edu/codiac-for-health/foundations/health-data) sources such as electronic health record \[EHR] systems across different hospitals and health systems).
There are different levels of standards and interoperability including "syntactic" (structure or format) and "semantic" (content or meaning). Common data models (CDM) such as the Observational Medical Outcomes Partnership (OMOP) CDM are an example of syntactic standards \[[2](#references)]. Terminologies, vocabularies, or coding systems defined in the United States Core Data for Interoperability (USCDI) are examples of semantic standards \[[3](#references)]. These include:
* **ICD-10-CM** (International Classification of Diseases, Tenth Revision, Clinical Modification) for diagnoses
* **CPT** (Current Procedural Terminology) for procedures
* **LOINC** (Logical Observation Identifiers Names and Codes) for laboratory tests, clinical observations, etc.
* **RxNorm** for medications
* **SNOMED CT** (Systematized Nomenclature of Medicine Clinical Terms) for clinical data in EHR systems
Syntactic and semantic standards are developed and maintained by numerous Standards Development Organizations (SDOs) \[[1](#references)]. Founded in 1987, HL7 International is a major SDO that provides a framework and standards for exchange, integration, sharing, and retrieval of electronic health information (e.g., in EHR systems). HL7 primary standards for integration and interoperability include Version 2.x (or V2), Version 3.x (or V3), CDA (Clinical Document Architecture), and Fast Healthcare Interoperability Resources (FHIR) \[[4](#references)].
### Example: Syntactic Differences
The following tables represent the same blood-pressure observation across different organizations using different structure.
#### Site 1
| patient\_id | record\_date | blood\_pressure |
| ----------- | ------------ | --------------- |
| 3333333 | 01/12/2016 | 120/80 |
#### Site 2
| patient\_id | record\_date | systolic\_blood\_pressure | diastolic\_blood\_pressure |
| ----------- | ------------ | ------------------------: | -------------------------: |
| 3333333 | 12.01.2016 | 120 | 80 |
#### Site 3
| patient\_id | record\_date | attribute | value |
| ----------- | ---------------- | ------------------------ | -------- |
| 3333333 | January 12, 2016 | systolic blood pressure | 120 mmHg |
| 3333333 | January 12, 2016 | diastolic blood pressure | 80 mmHg |
#### Site 4
| patient\_id | record\_date | attribute | value | unit |
| ----------- | ------------ | ------------ | ----: | ---- |
| 3333333 | 2016-01-12 | systolic bp | 120 | mmHg |
| 3333333 | 2016-01-12 | diastolic bp | 80 | mmHg |
Although the tables describe the same clinical encounter, they differ in several syntactic features, including date formats, column names, whether systolic and diastolic measurements appear in one or multiple fields, whether units are combined with or separated from numeric values, and whether observations are represented across one or multiple rows. When these differences occur across thousands or millions of clinical data points, the difficulty of exchanging, combining, and analyzing data can quickly compound.
### Example: Semantic Differences
Two organizations that use different codes to represent the same clinical concepts.
#### Local Codes
| patient\_id | attribute\_code | attribute\_value |
| ----------- | --------------: | ---------------: |
| 4444444 | 100 | 120 |
| 4444444 | 101 | 80 |
| Local code | Local description |
| ---------: | ------------------------ |
| 100 | Systolic blood pressure |
| 101 | Diastolic blood pressure |
#### Standard Codes (e.g., LOINC)
| patient\_id | LOINC code | attribute\_value |
| ----------- | ---------- | ---------------: |
| 4444444 | 8480-6 | 120 |
| 4444444 | 8462-4 | 80 |
| LOINC code | Standardized concept |
| ---------- | ------------------------ |
| 8480-6 | Systolic blood pressure |
| 8462-4 | Diastolic blood pressure |
Using standardized terminology allows receiving systems to interpret clinical concepts consistently without first translating organization-specific local codes into nationally or internationally recognized standards.
### How It All Connects
Together, standards development organizations and federal governing bodies shape the modern landscape of health information exchange. The Office of the National Coordinator for Health Information Technology (ONC) oversees federal health IT standards, policies, and certification requirements. \[[5](#references)] USCDI establishes a baseline set of health data classes and data elements for nationwide exchange and identifies applicable terminology standards for representing certain information consistently. \[[6](#references)]
These standards serve complementary purposes. Terminologies and coding systems such as SNOMED CT, LOINC, and RxNorm define the meaning of clinical concepts. FHIR provides a standard framework for structuring and exchanging electronic health information between systems. \[[7](#references)] The OMOP Common Data Model standardizes the structure and content of observational health data to support consistent analysis and research. \[[8](#references)]
{% hint style="info" %}
**See CODIAC for Health chapter on** [**Observational Research with EHR Data**](https://docs.bcbi.brown.edu/codiac-for-health/observational-research) **for more information.**
{% endhint %}
## References
1. Hammond WE. [**The making and adoption of health data standards**](https://pubmed.ncbi.nlm.nih.gov/16162564/). Health Aff (Millwood). 2005 Sep-Oct;24(5):1205-13. doi: 10.1377/hlthaff.24.5.1205. PMID: 16162564.
2. Weeks J, Pardee R. [**Learning to Share Health Care Data: A Brief Timeline of Influential Common Data Models and Distributed Health Data Networks in U.S. Health Care Research**](https://pubmed.ncbi.nlm.nih.gov/30937326/). EGEMS (Wash DC). 2019 Mar 25;7(1):4. doi: 10.5334/egems.279. PMID: 30937326; PMCID: PMC6437693.
3. Bodenreider O, Cornet R, Vreeman DJ. [**Recent Developments in Clinical Terminologies - SNOMED CT, LOINC, and RxNorm**](https://pubmed.ncbi.nlm.nih.gov/30157516/). Yearb Med Inform. 2018 Aug;27(1):129-139. doi: 10.1055/s-0038-1667077. Epub 2018 Aug 29. PMID: 30157516; PMCID: PMC6115234.
4. Braunstein ML. [**Healthcare in the Age of Interoperability: The Promise of Fast Healthcare Interoperability Resources**](https://pubmed.ncbi.nlm.nih.gov/30452344/). IEEE Pulse. 2018 Nov-Dec;9(6):24-27. doi: 10.1109/MPUL.2018.2869317. PMID: 30452344.
5. Office of the National Coordinator for Health Information Technology. [*About the ONC Health IT Certification Program*](https://healthit.gov/certification-health-it/about-onc-health-it-certification-program/). HealthIT.gov. Updated April 1, 2026. Accessed August 26, 2026.
6. Office of the National Coordinator for Health Information Technology. [*United States Core Data for Interoperability (USCDI)*](https://isp.healthit.gov/united-states-core-data-interoperability-uscdi). Interoperability Standards Platform. Accessed August 26, 2026.
7. Health Level Seven International. [*FHIR Overview*](https://hl7.org/fhir/overview.html). *FHIR Specification, Version 5.0.0 (R5)*. Published March 26, 2023. Accessed August 26, 2026.
8. Observational Health Data Sciences and Informatics. [*Areas of Focus*](https://www.ohdsi.org/who-we-are/areas-of-focus/). OHDSI. Accessed August 26, 2026.
## Resources
### Books/Chapters
* [**Principles of Health Interoperability: FHIR, HL7 and SNOMED CT**](https://link.springer.com/book/10.1007/978-3-319-30370-3)
* [**Health Informatics on FHIR: How HL7's New API is Transforming Healthcare**](https://link.springer.com/book/10.1007/978-3-030-91563-6)
* [**Health IT Playbook**](https://www.healthit.gov/playbook/)
* [**What are Health IT Standards?**](https://www.healthit.gov/playbook/sdo-education/chapter-1/)
* [**Standards Development Organizations**](https://www.healthit.gov/playbook/sdo-education/chapter-2/)
### Articles
* Bodenreider O. [**The Unified Medical Language System (UMLS): integrating biomedical terminology**](https://pubmed.ncbi.nlm.nih.gov/14681409/). Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70. doi: 10.1093/nar/gkh061. PMID: 14681409; PMCID: PMC308795.
* Reich C, Ostropolets A, Ryan P, Rijnbeek P, Schuemie M, Davydov A, Dymshyts D, Hripcsak G. [**OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization**](https://pubmed.ncbi.nlm.nih.gov/38175665/). J Am Med Inform Assoc. 2024 Feb 16;31(3):583-590. doi: 10.1093/jamia/ocad247. PMID: 38175665; PMCID: PMC10873827.
* Wei WQ, Bastarache LA, Carroll RJ, Marlo JE, Osterman TJ, Gamazon ER, Cox NJ, Roden DM, Denny JC. [**Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record**](https://pubmed.ncbi.nlm.nih.gov/28686612/). PLoS One. 2017 Jul 7;12(7):e0175508. doi: 10.1371/journal.pone.0175508. PMID: 28686612; PMCID: PMC5501393.
### Links
* [**United States Core Data for Interoperability (USCDI)**](https://www.healthit.gov/isp/united-states-core-data-interoperability-uscdi)
* [**HL7 International**](https://www.hl7.org/)
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