Advancing real-world evidence in Brazil: regulatory gaps and global lessons

Summary

Regulatory agencies are increasingly incorporating real-world data (RWD) and real-world evidence (RWE) into decision-making frameworks to complement randomized clinical trials. While some regions, such as the U.S. and EU, have developed structured approaches for RWE use, Brazil's regulatory environment remains comparatively limited. This study examines the status of RWE regulatory integration in Brazil through an analysis of normative documents, institutional publications, and selected case studies, using a comparative policy perspective. Although advances have been made in data standardization and the publication of technical guidelines, such as ANVISA's Guidance No. 64/2023, the practical use of RWE in regulatory processes is still nascent. Key challenges include fragmented data infrastructure, and limited intersectoral coordination. Addressing these gaps will require improved interoperability across health information systems, convergence of methodological standards, and sustained collaboration among regulatory authorities, academia, and data holders to enable consistent and scientifically robust use of RWE in the Brazilian context and, potentially, in other low- and middle-income countries.

Introduction: from randomized trials to real-world evidence

Real-World Data (RWD) and the resulting Real-World Evidence (RWE) represent a paradigm shift in healthcare decision-making, moving beyond the traditional confines of randomized controlled trials (RCTs). RWD is changing how to assess the safety, effectiveness, and value of medical products in diverse populations and real-world clinical settings. Major regulatory bodies globally, including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), have established formal frameworks and guidelines to accelerate the integration of RWE into regulatory submissions, recognizing its potential to complement RCT data and inform drug development throughout the lifecycle.^1,2

Brazil, with its vast Universal Health System (“Sistema Único de Saúde”—SUS)³ and the world's seventh-largest population,⁴ holds enormous potential for generating high-impact RWE. The country benefits from substantial research capabilities and an established, regulatory-grade clinical research network.⁵ The sheer scale and diversity of the Brazilian population, including its highly admixed genetics,⁶ offer a fundamental and often underrepresented cohort for global health research. However, despite this wealth of data, the practical application and regulatory acceptance of RWE by the Brazilian Health Regulatory Agency (ANVISA) remain limited and nascent. Systemic issues, including challenges related to data fragmentation, governance models, a lack of clear conceptual standardization, and infrastructural deficiencies, have hindered the translation of data potential into actionable evidence.^7,8

To address this gap, this article provides a comprehensive analysis of Brazil's RWE landscape. Our primary objective is threefold: (i) to critically examine the specific regulatory and infrastructural deficiencies within the Brazilian context; (ii) to benchmark these challenges against global best practices and established frameworks from major international agencies; and (iii) to propose a clear, actionable roadmap of recommendations tailored to Brazil's unique healthcare environment, thereby advancing the integration of RWE into national regulatory and Health Technology Assessment (HTA) processes.

Conceptual clarity is essential in this domain, as inconsistent terminology⁹ has led to interpretive variability across academic, regulatory, and industry settings. Table 1 therefore consolidates the principal definitions adopted in this study to ensure terminological consistency and analytical coherence throughout the discussion.

Table 1.

Key definitions in the RWE ecosystem.

Term	Definition and context
Real-World Data (RWD)	Data related to patients' health status and the delivery of healthcare routinely collected outside traditional randomized controlled trials. Common sources include Electronic Health Records (EHRs), administrative claims, disease registries, and patient-generated data.10,11
Real-World Evidence (RWE)	Evidence concerning the use, benefits, or risks of a medical product derived from the analysis of RWD using scientifically valid methods. Generating RWE requires methodological rigor, appropriate statistical modeling, and clearly defined clinical questions to ensure validity and reproducibility.12,13
Regulatory-Grade RWE	RWE that is sufficiently reliable, relevant, and robust to support regulatory decision-making, such as product approvals, label extensions, or post-marketing commitments.14,15 It requires transparent documentation, traceability of data provenance, and rigorous quality safeguards throughout the study lifecycle.16
Interoperability	The ability of different information systems and data sources to exchange and use data consistently and accurately. Interoperability is fundamental for integrating health data across systems, enabling comprehensive RWE generation and multi-source analyses.17, 18, 19
Common Data Model (CDM)	A structured, standardized framework (e.g., OMOP or Sentinel CDM) designed to harmonize heterogeneous RWD sources, enabling multicenter analyses. The adoption of a CDM, alongside HL7 FHIR standards, enhances both syntactic and semantic interoperability within healthcare ecosystems, including emerging contexts such as Brazil.20, 21, 22, 23

Acronyms. FHIR: Fast Healthcare Interoperability Resources; HL7: Health Level Seven; OMOP: Observational Medical Outcomes Partnership.

Search strategy and selection criteria

A structured search was performed to identify academic literature and regulatory documents addressing the use and regulatory integration of RWD and RWE, with a comparative focus on ANVISA and international agencies. Searches were conducted across the PubMed, Medline, and SciELO databases for publications dated from January 2010 to May 2025. Key terms utilized included those related to RWD/RWE and regulatory science, specifically: “real-world evidence”, “real-world data”, “regulatory science”, “ICH”, “EMA”, “FDA”, “ANVISA”, and “Brazil”. Articles published in both English and Portuguese were included for review.

Regulatory documents were retrieved directly from the official websites of ICH, ANVISA, MHRA, FDA, EMA, and other ICH member agencies. Documents focused solely on medical devices or produced by non-regulatory organizations were excluded. This strategy supported the identification of regulatory gaps in Brazil and enabled benchmarking against established international frameworks.

RWD and digital health in Brazil

In Brazil, pharmacovigilance relies primarily on VigiMed, the national reporting system managed by ANVISA. Designed to capture spontaneous reports related to drug and vaccine safety, the platform complies with the ICH E2B standard for structured electronic submission of individual case safety reports (ICSRs), allowing integration with international pharmacovigilance networks.²⁴ While VigiMed plays a role in early signal detection and requires minimal resources, it suffers from chronic underreporting and lacks systematic case follow-up.²⁵

Active surveillance remains mostly confined to the Sentinel Hospital Network, created in 2001 to monitor post-marketing drug safety. Despite its continued relevance, the network still grapples with interoperability issues, fragmented data flows, and slow signal identification.²⁵

Underlying these challenges is ANVISA's limited capacity to generate and evaluate RWD and RWE. Infrastructure deficits and a shortage of trained analysts hinder progress.²⁶ Unlike agencies in high-income countries, ANVISA lacks tools for assessing RWD quality or auditing RWE-based studies.²⁷ Regulatory asymmetries persist, even as RWE gains ground in global drug approval processes.28, 29, 30, 31

This shortfall is exacerbated by Brazil's fragmented health system. Despite generating substantial health data, Brazil's public system (SUS) and private sectors operate in isolation, often using non-interoperable information systems. Efforts to improve this began in the early 2000s and gained regulatory traction with the 2011 adoption of interoperability standards. The National Health Card and the Information Exchange in Supplementary Health (“Troca de Informação em Saúde Suplementar”–TISS) standard for private data exchange laid important groundwork. Still, implementation varies widely and is often constrained by local institutional capacity.³²

Progress has been more visible in primary care. Through the e-SUS strategy, the Citizen's Electronic Health Record (“Prontuário Eletrônico do Cidadão”–PEC) system has steadily expanded. Between 2017 and 2022, it grew from 8930 to over 26,000 Basic Health Units (BHUs), especially in the North, Northeast, and Southeast. By 2022, PEC was used in roughly 60% of primary care visits, replacing older systems.33, 34, 35

The 2020 launch of the National Health Data Network (RNDS) aimed to strengthen interoperability and enable broader data use, including for research and policymaking.³⁶ Built on HL7 FHIR-based RESTful APIs, it facilitates real–time exchange of lab results and clinical records.³⁷ Standardization has improved, but local barriers persist. Many municipalities lack stable internet, updated equipment, or staff trained in digital tools. These gaps have slowed PEC integration. Furthermore, PEC remains largely disconnected from hospital and specialist records, which constrains its value for comprehensive RWE analysis.³³

Despite these limitations, there have been advances in data governance and exchange. Some technical initiatives in Brazil have implemented federated, cloud-based systems that allow for standardized, pseudonymized data sharing while maintaining institutional control and compliance with data protection laws.^38,39 These models show potential for secure, scalable interoperability, but remain unevenly distributed across the country.

Regulatory challenges in the convergence of RWD use

The inconsistent and interchangeable use of RWD and RWE remains a challenge, reflecting ongoing misunderstandings in their application. Situations where RWD and RWE are incorrectly characterized include using RWD merely for trial site selection or patient recruitment, misclassifying data collected through protocol-driven procedures in clinical trials as RWD, and interpreting aggregated literature data as RWE. Confusion also arises when external control arms based on clinical trial data are incorrectly considered RWD, or when RWD is used only to describe disease prevalence without directly evaluating medicine–outcome associations. Furthermore, non-interventional studies that collect additional data under a protocol can still generate RWE if treatments reflect routine clinical practice.^2,40

Regulatory agencies around the world use RWD and RWE according to their own definitions, which can influence the analysis and interpretation of data on medicines and treatments. To better understand the semantic similarities and differences among these definitions, Fig. 1 has been created.

Fig. 1.

RWD and RWE Semantic Similarity among Regulatory Agencies: The heatmap shows the semantic similarity between multiple regulatory agencies based on their official documentation. Higher values (depicted in darker blue) represent greater semantic proximity. The agencies included in this analysis are: ANVISA (Brazilian Health Regulatory Agency), EMA (European Medicines Agency), FDA (U.S. Food and Drug Administration), Health Canada, MHRA (Medicines and Healthcare products Regulatory Agency, UK), MFDS (Ministry of Food and Drug Safety, South Korea), NMPA (National Medical Products Administration, China), PMDA (Pharmaceuticals and Medical Devices Agency, Japan), SFDA (Saudi Food and Drug Authority), Swissmedic (Swiss Agency for Therapeutic Products) and Taiwan Food and Drug Administration (TFDA). Note: The graph was generated using embeddings — technique that converts words or texts into numerical vectors capturing semantic meaning — and was developed in R Studio, based on definitions drawn from the guidance documents listed in the ICH Reflection Paper on RWD/RWE harmonization.⁴¹

The heatmap displays semantic similarity scores ranging from approximately 0.86 (lightest blue) to 1.0 (deepest blue, indicating perfect semantic match). The values are generally high across the board, suggesting a considerable degree of semantic alignment among these agencies' RWD/RWE definitions.

Agencies such as the U.S. FDA, Health Canada, SFDA, and ANVISA exhibit closely aligned definitions, forming a dense cluster with similarity scores approaching 1.0. This convergence may reflect shared regulatory practices or reference to common international sources, including guidelines issued by the agencies themselves. In contrast, agencies like PMDA, Swissmedic, and MHRA are positioned farther apart, both visually and semantically. PMDA presents lower similarity scores, suggesting a more distinct conceptualization of RWD and RWE. These differences may arise from linguistic variation or the influence of specific national regulatory contexts.

While these terms share semantic proximity, yet their application demonstrates considerable heterogeneity in scope and regulatory emphasis. This variability is often influenced by underlying definitions, particularly in the interpretation of key terms such as data quality, reliability, and relevance.⁴² These divergences may limit the extent to which definitions and practices are transferable across regulatory contexts.

An international movement toward harmonizing the use of RWD and RWE in regulatory contexts has gained traction through coordinated efforts. In 2022, the International Coalition of Medicines Regulatory Authorities (ICMRA) issued a joint statement outlining four strategic priorities to enhance the credibility and utility of RWE: harmonizing terminology, promoting regulatory convergence through shared guidance and best practices, strengthening preparedness for public health emergencies, and fostering transparency in the generation and use of RWE.⁴³ Previously, in April 2020, ICMRA established a COVID-19 RWE Working Group, which in February 2024 transitioned into a broader initiative focused on generating RWE to support preparedness for future public health emergencies.⁴⁴

In June 2024, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) released a Reflection Paper titled “Pursuing Opportunities for Harmonization in Using Real-World Data to Generate Real-World Evidence, with a Focus on Effectiveness of Medicines”.⁴¹ The document presents a shared vision for aligning key terminology and establishing overarching principles for designing, conducting, and reporting RWD-based studies across regulatory jurisdictions. In line with this initiative, ICH published guideline M14,⁴⁵ which provides harmonized recommendations for pharmacoepidemiological studies using RWD to assess medicine safety.

Creditability of RWE

The mere use of RWD or the labeling of a study as producing RWE is not enough to ensure credibility; the transformation of raw data into meaningful clinical evidence depends on methodological standards, appropriate statistical modeling, and well-defined clinical questions. A clear understanding of data provenance, study design, and methodological rigor remains essential for transparency.^46,47

The credibility of RWD-based studies also relies on the consistent application of best practices across the data life cycle. A trustworthy and sustainable RWD system depends on several key elements such as adherence to international standards for data organization, application of quality control according to the intended use, encouragement of complete and accurate data entry, use of methods to process unstructured information, development of flexible analytical systems, maintenance of transparent governance with strong privacy protections, and promotion of diversity and inclusion in the collected data.⁴⁸

Protocol transparency

Promoting protocol transparency through public registration of RWE studies is increasingly recognized as a fundamental step toward improving the reproducibility, credibility, and regulatory utility of evidence derived from RWD. A 2024 scoping review⁴⁹ showed that 47.8% of studies published in leading medical journals in 2022 were observational. Among these, only 10% were registered in public repositories, and just 12% disclosed a publicly accessible study protocol.

Pre-registration of study designs and analytic methods reduces bias, supports external validation, and strengthens stakeholder trust.⁵⁰ Moreover, transparent documentation is essential for building confidence in RWE for decision-making across global regulatory bodies.⁵¹ Initiatives like the HARPER Protocol Template⁵² and structured registries — like ClinicalTrials.gov and the HMA-EMA Catalogue of RWD studies — provide supports for standardizing protocols and ensuring accessibility.^52,53 Additionally, while not intended to assess study quality, tools like the STROBE offer a common structure to report observational research in a standardized and rigorous manner, facilitating transparency and interpretability.⁵⁴

Regulatory-grade RWD and RWE

A foundational step in advancing the generation of high-quality evidence is the establishment of a clear and consistent definition of regulatory-grade RWD and RWE — that is, a set of regulatory requirements specifying the minimum standards that must meet to be considered sufficiently robust for regulatory decision-making.

Multiple regions are taking steps to regulate and promote the use of RWD and RWE to support regulatory decisions. As shown in Fig. 2, several guidance documents have been issued globally.

Fig. 2.

Global distribution of RWD and RWE regulatory guidance documents. Notes: The choropleth map was generated based on the number of publicly available regulatory RWD/RWE guidance documents. For ICH member countries, documents were identified through the ICH Reflection Paper on RWD/RWE harmonization.⁴¹ For all other countries, documents were sourced directly from the official websites of their respective regulatory agencies. Documents focusing solely on medical devices were excluded, as were additional relevant materials from non-regulatory actors (e.g., academic societies, multi-stakeholder initiatives).

The U.S. FDA and the UK's MHRA offer two examples of how regulatory agencies are approaching the use of RWD and RWE. Since the 21st Century Cures Act of 2016, the FDA has been developing a program to explore RWE in regulatory contexts. In 2018, it published a framework detailing how RWE might support label expansions for approved medicines and meet post-marketing commitments. Additional guidance outlined expectations for data quality, study design, and regulatory use.⁵⁵

Similar, the MHRA released guidance in 2021 on how RWD can inform regulatory decisions. It stresses the importance of validated processes to ensure data integrity and reliability. Sponsors must be able to trace data provenance and apply quality safeguards throughout the study. MHRA Good Clinical Practice (GCP) inspections may examine systems overseeing RWD, including those related to randomization, safety reporting, investigational product handling, and the management of external databases.²⁷

Regulator-initiated RWE: strategic use by authorities

Unlike sponsor-submitted RWE, which is often product-specific and commercially motivated, regulator-initiated studies typically address broader public health questions. These may include the comparative effectiveness of therapeutic classes, vaccination safety across subpopulations, and the real-world impact of newly authorized medicines.⁵⁶ Among the Regulator-Initiated RWE, the United States and Europe stand out with the development of the Sentinel System and the DARWIN EU® network, respectively.^30,57

Since the passage of the FDA Amendments Act in 2007, the U.S. FDA has significantly advanced its ability to actively monitor the safety of regulated medical products through the creation of the Sentinel System — a national electronic surveillance network. Initiated in 2008 with the Mini-Sentinel pilot and officially launched in 2016, Sentinel enables the FDA to link and analyze RWD from multiple sources. It now hosts a multi-site distributed database dedicated to medical product safety.²⁸

Launched in 2022, DARWIN EU (Data Analysis and Real World Interrogation Network) represents a significant EMA-led effort to embed RWE within regulatory processes across the European Union. The platform offers access to diverse, high-quality healthcare datasets drawn from hospitals, registries, insurance claims, and biobanks. By 2024, DARWIN EU reached full operational status, actively supporting EMA scientific committees and national authorities with evidence generation for regulatory assessments. Projections indicate involvement of at least 40 data partners and up to 100 studies conducted annually by 2025.³⁰

Leveraging standards to generate reliable RWE

The generation of RWE from RWD can leverage semantic and structural interoperability to ensure regulatory and clinical utility.⁵⁸ The emergence of Common Data Models (CDMs) has made interoperable analytics possible in the medicine safety and clinical research communities that have adopted them.¹⁹ Mapping datasets to CDMs, such as OMOP, Sentinel CDM (SCDM), PEDSnet, PCORnet, and i2b2, facilitates data pooling across diverse sources. This can involve Extract, Transform, Load (ETL) pipelines, mapping specifications, and versioning protocols, enabling large-scale, multi-centric, regulatory-grade studies.⁵⁹

In addition to CDMs, internationally recognized semantic standards are essential for data consistency, interoperability, and reuse. These include terminologies such as ICD, SNOMED CT, ATC, RxNorm, and the WHO-Drug Global Dictionary, as well as domain-specific standards like Orphacode and the Human Phenotype Ontology (HPO).^60,61 Free-text entries, when unavoidable, should be flagged for future semantic enrichment.

DARWIN EU, for instance, follows a federated model in which data remains with local partners but is harmonized via the OMOP-CDM and ETL processes. Studies are designed centrally, executed locally using Observational Health Data Sciences and Informatics (OHDSI) tools, and results are aggregated by the EMA.⁵⁸ Similarly, the U.S. FDA's Sentinel system is a distributed data network, and uses the proprietary SCDM — distinct from OMOP but conceptually similar in its use of standard terminologies and federated queries.^19,62, 63, 64

Despite ongoing efforts, the fragmentation of data models, terminologies, and governance structures continues to hinder interoperability. One early attempt to confront this issue was the U.S. National Institutes of Health's Common Data Model Harmonization (CDMH) project, which developed and tested an infrastructure to support interoperability across four major CDMs by mapping them to a shared intermediary model (BRIDG).⁶⁵ Building on this type of approach, the Code Map Services for Interoperability of Common Data Models and Data Standards represents an active initiative led by the U.S. Department of Health and Human Services (HHS) Office of the Assistant Secretary for Planning and Evaluation (ASPE), with participation from the FDA. This project focuses on the development of automated tools to map and harmonize data across multiple CDMs.⁶⁶

The Brazilian sponsor-submitted RWE

A 2021 survey, featured in the 2022 DIA Global Forum,⁶⁷ examined the use of RWD and RWE by the pharmaceutical industry in Brazil. Among the 36 companies that responded — representing 44% of the national pharmaceutical market — 70% had conducted or were conducting RWE studies using Brazilian population data. Additionally, 44% reported using RWE in regulatory submissions or interactions with authorities such as ANVISA. However, significant challenges were pointed out: 84% of respondents cited issues with data quality and completeness, 74% pointed to the absence of regulatory guidance, and 65% highlighted a shortage of skilled professionals. The article concluded with a call for clear regulatory frameworks, increased transparency from ANVISA, and investment in local capacity to support the broader application of RWE in regulatory decision-making.

Since then, a notable regulatory advancement occurred in 2023, when ANVISA published Guideline No. 64/2023, introducing initial methodological standards and outlining possible uses of RWE in medicine approval, post-marketing monitoring, and labeling changes.⁶⁸ Concurrently, ANVISA established the Working Group on Real-World Evidence (GT-EMR) to foster technical discussions and encourage cross-sector collaboration.⁶⁹ Despite these initiatives, industry engagement has remained limited. As of early 2025, the GT-EMR had received only one formal protocol submission, suggesting low responsiveness from regulated parties.

These findings raise questions about whether industry-generated data accurately reflect the current state of RWE adoption among regulated entities. Moreover, it is unclear whether the terms RWD and RWE were used interchangeably or incorrectly characterized in the survey presented in the 2022 DIA Global Forum, which could lead to ambiguous interpretations and overestimations of regulatory maturity in this area. For instance, the collection of medicine performance data under Commitment Terms — a legal possibility^70,71 for post-approval monitoring of medicines authorized with limited clinical data — normally does not constitute a new evidence generation per se. Alternatively, the methodological expectations introduced by ANVISA's guideline may have prompted stakeholders to reassess ongoing projects and identify necessary adjustments before formal engagement. In either case, the GT-EMR remains underutilized, limiting its potential as a platform for technical exchange and capacity building.

Discussion and strategic recommendations for Brazil

This article explores the development and current state of national regulations regarding the use of RWE in Brazil. It examines the legal and institutional framework, recent efforts by other regulatory agencies, and key barriers to adoption. By analyzing these factors, the article seeks to contribute to discussions on how RWE can improve evidence-based policymaking and encourage regulatory innovation in Brazil and other emerging health systems.

Brazil is still in the early stages of incorporating RWE into decision-making processes. Currently, its use in regulatory contexts remains limited for pharmacovigilance purposes and rarely contributes to regulatory assessments. Challenges such as institutional limitations and fragmentation, regional differences in digital infrastructure, and insufficient coordination among stakeholders have limited progress.

National systems often operate in silos, with poor interoperability and inconsistent adherence to common standards. The absence of well-defined methodological guidelines further complicates the process. Additionally, there is no comprehensive mapping of available or emerging RWD sources, limiting insight into the data that Brazil could leverage or further develop. As a result, much of the existing infrastructure remains underutilized, particularly when compared to regions where RWD is already embedded in routine regulatory practices.

Despite these challenges, there are signs of progress. ANVISA has started to establish standardized definitions and issue methodological guidelines, which are essential for improving the design, reporting, and evaluation of RWE studies. National digital health initiatives, particularly those focused on interoperability standards and advanced analytics — such as the adoption of HL7 FHIR within the RNDS — present promising pathways for accelerating the integration of RWE into regulatory practices.

Global standards like HL7 FHIR, when integrated with common data models (e.g., OMOP),^72,73 have the potential to improve the quality, comparability, and regulatory usability of RWD. Strengthening governance structures and enhancing semantic interoperability will be essential for unlocking the full potential of health data, enabling more robust, evidence-informed regulatory decision-making.

To develop a more mature and internationally harmonized RWE ecosystem, Brazil must undertake strategic reforms aimed at bridging the gap between data availability and regulatory-grade data quality. These reforms should focus on improving interoperability, enhancing transparency, promoting governance standards, and ensuring methodological rigor. The primary recommendation is to expand the use of RWE to strengthen regulatory decision-making and improve risk communication throughout the product lifecycle, as summarized in Table 2.

Table 2.

Strategic recommendations for advancing RWE in Brazil.

Thematic cluster	Recommendation
Data governance	Establish a dedicated national coordination center for RWD/RWE governance and integration into regulatory processes.
Data governance	Develop a publicly accessible catalogue of RWE studies and source datasets.
Data governance	Mandate protocol registration for all RWE-based regulatory submissions.
Data governance	Harmonize Brazilian data governance frameworks with global privacy norms and federated models.
Analytical capacity & interoperability	Implement a national data quality framework and dashboards.
Analytical capacity & interoperability	Integrate a CDM and HL7 FHIR into national platforms.
Analytical capacity & interoperability	Foster public–private partnerships for federated data networks.
Analytical capacity & interoperability	Engage proactively with global initiatives (e.g., DARWIN EU, ICH, and ICMRA).
Implementation capacity & workforce development	Invest in capacity building and training for RWE stakeholders.
Implementation capacity & workforce development	Create dedicated funding streams for RWE research, guaranteeing long-term sustainability of data infrastructures and grants to support high-quality RWE studies, reducing reliance on ad hoc resources.
Future trends & advanced analytics	Adopt metadata standards for Artificial Intelligence (AI) generated data, including model lineage and confidence scoring.
Future trends & advanced analytics	Expand national terminology sets to accommodate emerging clinical and technical concepts.
Future trends & advanced analytics	Establish validation frameworks and quality metrics for AI-assisted data processing.
Equity & participation	Mandate equity and diversity metrics in RWE studies, promoting representativeness and helping identify health disparities by requiring disaggregation of data by sex, race, ethnicity, and socioeconomic status.
Equity & participation	Encourage patient and community involvement in study design.

Acronyms. CDM: Common Data Model; DARWIN EU: Data Analysis and Real World Interrogation Network for the European Union; FHIR: Fast Healthcare Interoperability Resources; HL7: Health Level Seven; ICH: International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use; ICMRA: International Coalition of Medicines Regulatory Authorities; RWD: Real-World Data; RWE: Real-World Evidence.

Conclusion

Brazil has improved its ability to produce high-quality RWD, yet regulatory practices have not fully capitalized on this progress. Despite initiatives like ANVISA's Guideline No. 64/2023 and the GT-EMR working group, the institutional adoption of RWD and RWE remains limited. The barriers are not just technical; they are systemic. Fragmented data governance, inconsistent standards, and the lack of a comprehensive regulatory framework hinder wider implementation. Unlike Brazil, programs like those from the U.S. FDA and EMA demonstrate how structured frameworks and transparent practices can make RWE a reliable tool for decision-making.

Closing this gap demands targeted reforms. Adopting data standards, defining clearer submission processes, and fostering collaboration between regulators and stakeholders would help. But beyond technical fixes, institutional changes are necessary. Clearer policies, better incentives, and stronger coordination must accompany innovation.

To institutionalize RWE in Brazil, technical capacity must be reached by governance changes and international alignment. Strategic implementation of federated data models, alongside mandatory protocol registration and broad stakeholder engagement, determines the capacity of Real-World Data (RWD) to inform national health policy and regulation.

Importantly, incorporating RWE in Brazil has implications that extend beyond the national context. Given the country's large, diverse, and highly admixed population, the evidence generated domestically holds value for international regulatory science. In this sense, ANVISA is well positioned to assume a leadership role in Latin America by advancing structured and interoperable approaches to RWE. For RWE to become a relevant basis for Brazil's regulatory system, scientific advances must align with institutional modernization. This alignment would enhance regulatory responsiveness and contribute to better public health outcomes.

Contributors

N.C.A. and C.O.R. contributed to the conception and design of the review. N.C.A. and C.O.R. performed the literature search and wrote the first draft of the manuscript. J.P. provided senior oversight, contributed to the critical revision, and had final responsibility for the decision to submit the manuscript for publication. L.A.P.C.L., H.F.C., V.R., F.S.R.R. and F.N.R.A. contributed to data interpretation and critical review of the manuscript. All authors reviewed and approved the final version of the manuscript.

Data sharing statement

This manuscript is a narrative review that did not involve primary data collection or analysis. All authors had full access to all referenced materials and verified the accuracy of the information synthesized in the review.

Editor note

The designations employed and the presentation of material on the map do not imply the expression of any opinion whatsoever on the part of the Authors or the Journal concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Declaration of interests

All authors declared no competing interests. The views and opinions expressed in this article are solely those of the authors and do not necessarily reflect the official policy or position of the institutions with which they are affiliated.