Terminology Mapping�in Brazil: From Local Codes to Global Meaning
Terminology Services, Governance, OCL Mapper, and the path to semantic interoperability in the Brazilian health system
Jussara Rotzsch – Chair HL7 Brazil
Jonathan Payne – Director, Open Concept Lab; Co-chair, WHO DPI-H Metadata Registry Workgroup
Agenda
1
Brazil Case Introduction
Interoperability paradox, barriers, RNDS, and FHIR adoption reality
2
Terminology Services in the Brazilian Context
Conformance illusion, the semantic gap, and OCL infrastructure
3
Governance Framework for National Terminology Assets
Institutional roles, one vocabulary, DPI, and the paradigm shift
4
The SGI Layer of DPI for Health (DPI-H)
5
OCL Overview and Brazil SGI Use Cases
Native authoring, automated FHIR publication, IPS Brasil
6
Live Demo: Mapping Brazilian Terminologies
Walkthrough of OCL and OCL Mapper in practice
7
Lessons Learned and Next Steps
The interoperability equation and the coalition for DPI
HL7
01
SECTION
Section 01
Brazil Case Introduction
Interoperability paradox, barriers, RNDS, and FHIR adoption reality
HL7
The Brazilian Interoperability Paradox
83% of leaders see the topic as a strategic priority. The operational reality tells another story.
Where data flows a bit better (Back-office)
HIS/EHR
14.3%
Data Management
13.4%
Where data stops (Front-office)
Patient Engagement
7.1%
Telemedicine
7.1%
Education and Training
6.2%
Brazilian interoperability is still driven by regulatory obligations and billing. The patient continues to be treated as a data object, not as the center of the strategy.
More integrations don't mean more efficiency.
Without data governance, ad-hoc integrations create exponential complexity and technological lock-in (vendor lock-in).
\
The Illusion of Integration
Real Interoperability
HIS
LIS
EHR
Billing
Airports without international routes. Everything works locally, but longitudinality is broken.
Patient
Internal�Systems
RNDS
Wearables
External�Labs
Patient Longitudinality. Data follows the individual, not the software.
The Real Barriers (It's Not Technology).
Systemic failure occurs due to absence of strategic priority from top management and ineffective governance, not for lack of standards.
Economic Block
Implementation cost / Lack of budget
58.9%
Low vendor maturity
40.2%
Executive Block
Lack of institutional priority at Board level
55.4%
Cultural resistance from professionals
31.2%
Technical Block
Lack of applied standards
51.8%
Shortage of specialized talent
50.0%
The Reality of Standards in Brazil
The market doesn't suffer from lack of technology, but from lack of organizational discipline to apply it.
13.1%: HL7 FHIR adoption.�(The emerging gold standard).
~40%: The fragmented legacy. Systems that don't connect, or use legacy/closed standards (HL7 v2.x, CDA, Others).
~46%: The knowledge vacuum. Can't answer or the system doesn't apply standards.
71% of countries globally already use FHIR actively. Brazil has 13.1% local adoption. But does adopting the structure solve the problem?
02
SECTION
Section 02
Terminology Services in the Brazilian Context
Conformance illusion, the semantic gap, and OCL infrastructure
HL7
The "Webservice" That Isn't.
RTS is a batch CSV distribution, not a live API — and why that's the gap FHIR closes.
NOT a live API
Batch distribution mechanism.
You download CSV files organized by competência (reference period).
Static exports, not runtime queries.
The name "Webservice" is misleading by modern standards.
Pull-by-period
Pull-by-period, not query-by-code.
Download the CSV snapshot for a given competência, process it locally, work from that file.
No $expand, no $lookup, no real-time validation — just periodic file retrieval.
The replacement model
Bringing real semantics into FHIR.
The replacement model — terminologia.saude.gov.br/fhir via OCL — was adopted to integrate terminology semantics directly into FHIR.
CodeSystems, ValueSets, ConceptMaps served as proper FHIR resources — meaning, not just structure.
Queryable at runtime via $expand, $lookup, $validate-code.
The Terminology Lifecycle.
How terminologia.saude.gov.br authors, maps, manages, and distributes Brazil's health terminology.
OCL — Open Concept Lab (terminology management platform)
1
AUTHOR
Domain experts define CodeSystems for clinical concepts — BRParentesco, BRHPVInterpretacao, BRCondutaColposcopia, BRTabelaSUS…
2
MAP
ConceptMaps link national codes to LOINC, SNOMED CT, ICD-10, and reconcile legacy TUSS / SUS terminologies.
3
MANAGE
Versioning, status, and governance handled in OCL — every change is reviewed, dated, and traceable.
4
DISTRIBUTE
National CodeSystems, ValueSets, ConceptMaps shipped via a FHIR Implementation Guide as installable packages — drop-in for implementers, no CSV ingest required.
FHIR Terminology Server terminologia.saude.gov.br
Implementation Guide terminologia.saude.gov.br/fhir
OCL: Brazil's Semantic Infrastructure.
The portal built and freely available today: terminologia.saude.gov.br
69
National CodeSystems�(Native in OCL: ICD-10, SIGTAP, LOINC)
1
FHIR Terminology�Implementation Guide (IG)
∞
Scalable Reuse�across the entire Ecosystem
Terms governed by CGSD and the Terminology Subcommittee (INSPIRA). Mappings and "Bindings" officially aligned with RNDS, BR-Core and IPS Brasil.
03
SECTION
Section 03
Governance Framework for National Terminology Assets
Institutional roles, one vocabulary, DPI, and the paradigm shift
HL7
Terminology Governance Framework
1
Policy (CGSD / SEIDIGI)
Institutional authority and strategic decision making for SUS Digital.
2
Clinical Rules (Terminology Subcommittee)
Technical analysis, specialty knowledge, and expert clinical review of mappings.
3
Implementation (HL7 Brasil)
Translation of rules into standards: BR-Core, IPS Brazil profiles.
4
Execution (OCL & OCL Mapper)
Curation, versioning, and national FHIR terminology services.
Feedback Loop
OCL executes decisions. It doesn’t make them.
Semantic Governance: One country, one vocabulary.
Who writes
Specialists decide the words.
A clinical panel agrees on the meaning of each medical term, freezes its spelling, and revises it as medicine evolves.
Slow, careful, official.
Where it lives
One official copy.
The approved dictionary resides in a single trusted digital location that anyone can query at any time.
No outdated printed copies in hospital drawers.
Who queries it
Hospitals, Apps, Reports.
Every system queries the same dictionary instead of maintaining its own mapping spreadsheets.
Terminologies are Digital Public Infrastructure (DPI).
Shared Foundations
Reusable across the entire national health ecosystem. Not built for a single isolated project or lab.
National Services
Open APIs. Accessible as basic utility services that any system (public or private) can and should invoke.
Built to Scale
Architecturally designed to handle the national load (Big Data, PGHD, +2.8 billion records in RNDS). Not designed for "pilots".
Just as the CPF is the citizen's identification infrastructure, OCL is the infrastructure of clinical meaning.
Architectural Pattern for Terminology Governance and Runtime Services in Brazil
OCL �Mapper
OCL Terminology Server
OCL �TermBrowser
OCL �Mapper
FHIRSmith
Authoring & Governance
Publishing & Runtime
Authoring: Single source of truth for management of CodeSystems, ValueSets and ConceptMaps.
Governance: Proposals, reviews and approvals documented with full provenance traceability.
Publication-time Sync → �1 Source of Truth.
Runtime APIs: FHIR operations $lookup, $expand, $translate, $validate-code available to systems nationwide�
Terminology Publishing: IG builds, �profile validation
Brazil operationalizes the SGI pattern: OCL governs the content, FHIRSmith serves it — �one source of truth, two complementary responsibilities.
04
SECTION
Section 04
The Semantic Governance Infrastructure Layer of Digital Public Infrastructure for Health (DPI-H)
HL7
DPI-H Architecture Principles
Layers of the WHO/ITU DPI-H Reference Architecture (DRAFT)
Health System Goals
Build Trusted Data Foundations, Digitally-enabled Health Workforce, Ensure Quality and Continuity of Care, �Establish Trusted Personal Health Records, Optimize Supply Chain Management, �Digitally strengthened Health Financing, Strengthen Climate and Epidemic Resilience
DPI-H Components
Business services: Lifelong Health Record, Computable Decision Support Engine, HMIS, Public Health Surveillance Platform, etc.�Metadata registries: Client, Facility, Health Workforce, Health Product, Terminology Service, Logical Information Model Registry, etc.
Functional Requirements and Specifications
Detailed 'shall' statements, conformance profiles, and validation rules that make the architecture testable.
Capabilities
What the health system must do to achieve its goals.
e.g. master data & registry management, decision support, care coordination, claims management, �disease surveillance, analytics
realized by
delivered by
specified by
What makes Terminology Services DPI-H: Semantic Governance (DRAFT)
Runtime �Layer
Publication Layer
Packaging, validation, build, distribution
FHIR operations lookup, validation, expansion, translation
“Runtime services without governance produce content that cannot be trusted, while
governance without runtime services produces decisions that cannot be operationalised.”
Application Layer
FHIR Terminology Ecosystem, CRMI, FHIR Packages
Applications that consume content and services exposed by the runtime layer
FHIR TS, CTS2
SVCM IHE Profile, Implementation Guides
Governance Layer
Stewardship, approval, authoring, lifecycle, publication authority, collaboration, provenance
No unifying governance interop framework, �but there are important fragments: �ISO 17117, SNOMED governance, HL7 UTG, etc.
Functions
Existing Standards
DPI-H Semantic Governance Infrastructure Capabilities (DRAFT)
SGI Capability Category | Definition | Value Proposition to Government |
Intake | Managing the intake of change requests, new upstream releases, and deprecation notifications from stakeholders and standards organizations | Ensures national standards stay current with global updates and local needs without ad-hoc, error-prone processes |
Authoring and Publication Processes | Coordinating the review, approval, versioning, and release of new content and changes to existing content according to defined business rules and audit requirements | Guarantees that only vetted, traceable changes reach the health system — reducing costly downstream errors |
Monitoring | Tracking compliance with governance rules, content quality/staleness, and field-level uptake of published standards | Reveals whether published standards are actually being used and where gaps or drift are emerging before they cause harm |
Distribution | Providing reliable, access-controlled infrastructure for delivering content across multiple consumption modalities in a defined architecture | Makes national standards practically accessible to all implementers — a standard no one can retrieve is a standard no one follows |
Consumption | Enabling downstream stakeholders and upstream maintainers to discover, retrieve, and integrate published content into their systems and workflows | Lowers the barrier for systems to align with national standards, accelerating interoperability across the health ecosystem |
05
SECTION
Section 05
Open Concept Lab Overview and Brazil SGI Use Cases
Native authoring, automated FHIR publication, IPS Brasil
HL7
What is the Open Concept Lab?
OCL is an open-source terminology management toolkit to collaboratively map, manage, and use health data standards alongside the global community.
Open-source
Recognized as a Digital Public Good1
Cloud-based
Use OCL Online for community collaboration
Community-driven
OpenHIE, HL7
Standards-based
FHIR, IHE SVCM
Globally adopted
Implemented globally
Snapshot of Global OCL Implementations
Brazil International Patient Summary (IPS) Exchange
Kenya National Health Terminology Service
OpenMRS Concept Dictionary Management
Learn how 1,000s of health facilities use metadata managed in OCL
Architectural Pattern for Terminology Governance and Runtime Services in Brazil
OCL �Mapper
OCL Terminology Server
OCL �TermBrowser
OCL �Mapper
FHIRSmith
Authoring & Governance
Publishing & Runtime
Authoring: Single source of truth for management of CodeSystems, ValueSets and ConceptMaps.
Governance: Proposals, reviews and approvals documented with full provenance traceability.
Publication-time Sync → �1 Source of Truth.
Runtime APIs: FHIR operations $lookup, $expand, $translate, $validate-code available to systems nationwide�
Terminology Publishing: IG builds, �profile validation
Brazil operationalizes the SGI pattern: OCL governs the content, FHIRSmith serves it — �one source of truth, two complementary responsibilities.
Use Case #1: IPS Brasil – runtime translation of local codes to SNOMED CT via governed ConceptMaps
OCL governs the SIGTAP/ICD-10 → SNOMED CT mappings; FHIR service executes $translate; output conforms to IPS with meaning preserved.
Step 1: Data Capture
A SUS patient is coded locally using SIGTAP or ICD-10.
Step 2: The Translation Engine (ConceptMap)
The system queries the public infrastructure (OCL + FHIRSmith) using the FHIR $translate operation. The official mapping converts the local code into SNOMED CT.
Step 3: Global Destination (IPS)
The data is packaged into the International Patient Summary using SNOMED CT, enabling global continuity of care and portable patient control via the "Meu SUS Digital" app.
The Challenge
Brazil relies heavily on localized terminologies (SIGTAP, CBHPM) while transitioning to international clinical standards (SNOMED CT, ICD-11). Manually mapping thousands of concepts across spreadsheets is slow, error-prone, impossible to version control, and hard to govern.
Local Medication Table
Meddra
OCL Mapper
Ontologia Brasileira de Medicamentos (OBM)
Governance Loop
Concepts without an OBM equivalent are tagged as “Unmatched (0)” and exported as governance reports to expand the national ontology.
Use Case #2: National Medication Governance (OBM)
06
SECTION
Section 06
Live Demo: Mapping Brazilian Terminologies
Walkthrough of OCL and OCL Mapper in practice
HL7
What does an AI-enabled Terminology Mapping Workflow look like?
Recommend
e.g. LLM-as-judge
Preprocess
e.g. keyword expansion
Multi-algorithm Retrieval
e.g. semantic search, scispacy, bridge search, etc.
User initiates matching process
Accompanied review process
Save/export final mappings in desired format
Auto-match Process
User Workflow
Validate & �Learn
Auto-match
Ranked Candidates
Candidate Pool
Input Data
LOINC Mapping Example 1
Order Mnemonic | Order Description | Tube Type | Specimen | Test Mnemonic | Test Description | Result Type | Units of Measure |
BNP | B type Natriuretic Peptide, Plasma | Pink | Plasma | BNP | B type Natriuretic Peptide, Plasma | N | pg/mL |
AI Assistant – RECOMMEND
30934-4 shows excellent alignment: exact component match (Natriuretic peptide.B), system compatibility (Ser/Plas covers Plasma), matching units (pg/mL), and strong algorithm consensus (100% normalized score). The LOINC system 'Ser/Plas' explicitly includes plasma as indicated in the long common name 'in Serum or Plasma'. High COMMON_TEST_RANK (180) and COMMON_ORDER_RANK (57) indicate this is a frequently used, well-established code. Verify that your local term measures mass concentration rather than molar concentration - if molar units are used, consider 47092-2 instead.
AI Assistant Analysis
Correct candidate RECOMMENDED with explanation of each matching axis.
Grade
Candidate Pool: A
AI Assistant: A
PRIMARY – 30934-4 Natriuretic peptide B [Mass/volume] in Serum or Plasma
ALTERNATES
PRIMARY – 30934-4 Natriuretic peptide B [Mass/volume] in Serum or Plasma
Input Data
LOINC Mapping Example 2
CONDITIONAL assessment identified correct alternate and guided mapper with verification steps.
Grade
Candidate Pool: A
AI Assistant: A
Order Mnemonic | Order Description | Tube Type | Specimen | Test Mnemonic | Test Description | Result Type | Units of Measure |
SPEP | Protein Electrophoresis, Serum | SST | Serum | GLOBS | Globulin | N | g/dL |
AI Assistant – CONDITIONAL
The input term 'Globulin' with system 'Serum' creates ambiguity between gamma globulin (2874-6) and total globulin (10834-0, 2336-6). The order mnemonic 'SPEP' (Serum Protein Electrophoresis) and units 'g/dL' suggest this is likely gamma globulin from electrophoresis, supporting 2874-6. However, the generic term 'Globulin' could refer to total globulin. Key decision point: Does your local 'Globulin' term specifically mean gamma globulin from SPEP, or does it represent total globulin? Verify: (1) Whether SPEP order produces gamma globulin results or total globulin, (2) If your lab reports gamma globulin separately from other globulin fractions, (3) Whether the result represents a calculated total or electrophoresis-derived gamma fraction. If SPEP context confirms gamma globulin measurement, proceed with 2874-6.
AI Assistant Analysis
PRIMARY – 2874-6 Gamma globulin [Mass/volume] in Serum or Plasma by Electrophoresis
ALTERNATES
Input Data
LOINC Mapping Example 3
Order Mnemonic | Order Description | Tube Type | Specimen | Test Mnemonic | Test Description | Result Type | Units of Measure |
UAMICRO | Microscopic Urinalysis | URN | Urine | UAWBC | UA WBC | N | per HPF |
AI Assistant – REJECT
All current candidates have System='WBC' (white blood cells as specimen) but the input term 'UA WBC' refers to white blood cells IN urine (System should be 'Urine'). The candidates measure various enzymes/chemicals within isolated white blood cells, not white blood cell counts in urine sediment. The metadata confirms this is for 'Microscopic Urinalysis' with units 'per HPF' (high power field), indicating microscopic counting of leukocytes in urine. ��Key decision point: Search for LOINC codes where System='Urine' and Component='Leukocytes' with counting properties (NCnc/Naric) rather than the current WBC-system enzyme measurements.
AI Assistant Analysis
NO CANDIDATES IN POOL RECOMMENDED
Correctly REJECTED candidates in pool and provided steps to find the right match
Grade
Candidate Pool: F
AI Assistant: A
LOINC Mapping Evaluation: Results
Candidate Pool Generation
OCL AI Assistant for automated recommendation
Top-n Recall (Aggregate) | ||
top-1 | top-5 | top-10 |
53% | 80% | 86% |
For “human in the loop” mapping workflow, the goal is increased accuracy/ efficiency and reduced cognitive load for the human mapper
Source: P Naliyatthaliyazchayl et al. “Automated Logical Observation Identifiers Names and Codes mapping with biomedical natural language processing models: enabling scalable health information exchange via OCL.” JAMIA. May 2026. https://doi.org/10.1093/jamia/ocag010
07
SECTION
Section 07
Lessons Learned and Next Steps
HL7
The Interoperability Equation.
FHIR
FHIR
Structure
+
IGs
IGs
Usage Profiles
+
Terminology
Terminology
Meaning�(OCL)
+
Governance
Governance
Trust�(HL7 + MoH)
=
Digital Public Infrastructure for Health (DPI)
Together, we can build a connected ecosystem.
www.hl7.org.br | terminologia.saude.gov.br | openconceptlab.org
Questions for Discussion
www.hl7.org.br | terminologia.saude.gov.br | openconceptlab.org