The Gravity Project is a national collaborative initiative that convenes diverse stakeholders to develop and maintain standardized terminologies for social determinants of health (SDOH) documentation. Its core output consists of consensus-driven data elements and their associated value sets, designed to enable interoperable representation of social risk factors—such as food insecurity, housing instability, and transportation access—within electronic health records (EHRs) and HL7 FHIR APIs.
Glossary
Gravity Project Terminology
A consensus-driven initiative that develops standardized data elements and value sets for representing social determinants of health in electronic health records and FHIR APIs.
STANDARDIZED SDOH DATA ELEMENTS
What is Gravity Project Terminology?
A consensus-driven initiative that develops standardized data elements and value sets for representing social determinants of health in electronic health records and FHIR APIs.
By creating a common language for SDOH data, the Gravity Project bridges the gap between clinical care and community-based services. Its terminology is mapped to standard code systems like SNOMED CT, LOINC, and ICD-10-CM, ensuring that structured social risk data can be seamlessly exchanged, aggregated, and analyzed across disparate health IT systems to support holistic, value-based care and health equity initiatives.
STANDARDIZED SDOH CODIFICATION
Key Features of Gravity Project Terminology
The Gravity Project is a national collaborative that develops consensus-driven, standardized data elements and value sets to represent social determinants of health (SDOH) in electronic health records and FHIR APIs, enabling interoperable screening, diagnosis, and intervention.
01
Consensus-Driven Terminology Development
The Gravity Project convenes multi-stakeholder expert panels to define standardized data elements for specific social risk domains. This process ensures that the resulting terminology is clinically relevant, technically feasible, and aligned with the needs of providers, payers, and community-based organizations. The output is a set of LOINC codes for screening instruments and SNOMED CT and ICD-10-CM codes for diagnoses and interventions.
02
FHIR Implementation Guide Alignment
A core output of the Gravity Project is the HL7 FHIR SDOH Clinical Care Implementation Guide. This guide defines how to use standardized FHIR resources—such as Observation, Condition, Procedure, and ServiceRequest—to exchange Gravity-defined SDOH data. It provides a technical blueprint for embedding social care data directly into interoperable clinical workflows.
03
Domain-Specific Value Sets
The project organizes its work into distinct social risk domains, each with a curated set of coded concepts. Key domains include:
- Food Insecurity: Screening questions and diagnostic codes for lack of access to adequate food.
- Housing Instability: Concepts covering homelessness, inadequate housing, and eviction risk.
- Transportation Insecurity: Codes for lack of access to transportation for medical appointments.
- Financial Strain: Terminology for difficulty paying for utilities, medications, or other necessities.
04
Closed-Loop Referral System Enablement
Gravity terminology is designed to support a closed-loop referral process. By standardizing not only the screening and diagnosis of a social need but also the intervention and outcome, the project enables systems to track a patient's journey from identification of a risk factor to a confirmed connection with a community resource, closing the gap between clinical and social care.
05
USCDI and Regulatory Inclusion
Gravity Project data elements are a foundational component of the United States Core Data for Interoperability (USCDI). The inclusion of SDOH data classes like 'Problems' and 'Health Concerns' in USCDI mandates that certified health IT systems must be capable of exchanging this standardized social risk data, making Gravity terminology a regulatory requirement for EHR vendors.
06
Open-Source and Community-Driven
All Gravity Project deliverables, including value sets, implementation guides, and reference materials, are publicly available under open-source licenses. The project operates under the governance of the HL7 International standards organization, ensuring a transparent, community-driven development process that invites broad participation from across the healthcare ecosystem.
GRAVITY PROJECT TERMINOLOGY
Frequently Asked Questions
Clear, technical answers to the most common questions about the Gravity Project's consensus-driven standards for representing social determinants of health in FHIR APIs and EHR systems.
The Gravity Project is a national, multi-stakeholder collaborative that develops consensus-driven, standardized data elements and value sets for representing social determinants of health (SDOH) in electronic health records and FHIR APIs. Its criticality stems from solving a fundamental fragmentation problem: before Gravity, every health system, payer, and community-based organization used proprietary, non-interoperable screening tools and codes to document social risks like food insecurity or housing instability. This made closed-loop referral and population-level health equity analysis impossible. The project convenes experts from across the healthcare ecosystem to create open-source, HL7 FHIR-based implementation guides that define exactly how to structure, code, and exchange SDOH data using standardized terminologies like ICD-10-CM Z-codes, SNOMED CT, and LOINC. The output is a computable, machine-readable specification that enables seamless data liquidity between EHRs, community information exchanges, and payer systems, directly supporting value-based care models and regulatory mandates like CMS interoperability rules.
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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