Christian Reich

Introduction: McCune-Albright syndrome (MAS) is a rare congenital disorder affecting endocrine, skeletal and dermatological systems. MAS is often coded as fibrous dysplasia (FD) in routine health data, which includes other conditions and does not allow precise identification. This coding gap complicates reliable epidemiological estimates and clinical characterization. The study MAScINSIGHT aims to address this by investigating MAS features within FD-coded cohorts. Methods and analysis: MAScINSIGHT is a retrospective observational study using a federated network of data in the OMOP Common Data Model. Routine data from multiple sites will be analyzed within a federated setting to assess endocrine abnormalities and treatment patterns in patients with FD or MAS. Ethics and dissemination: Analyses will be performed locally at the data partner, with only aggregate statistics made available and centrally analyzed. The study protocol was approved by the ethics committee (reference number: 2026-18760-retrospektiv). Findings will be disseminated through open-access publications and patient-oriented reports. Strengths and limitations of this study: The study uses a federated approach based on the Observational Health Data Sciences and Informatics (OHDSI) framework and the OMOP Common Data Model, enabling standardized and reproducible analyses across institutions and countries while preserving data privacy. The multinational approach enables the analysis of larger and more diverse patient populations than typically available at individual institutions, addressing a major challenge in rare disease research. The integration of rare disease-specific terminologies, particularly ORPHAcodes, into the OHDSI vocabulary system supports more precise cohort identification and characterization, providing a transferable methodological approach for rare disease research. The retrospective design relies on routinely collected healthcare data, which may be affected by incomplete documentation, inconsistent coding practices and differences in data availability and quality across participating sites. Due to the rarity of McCune-Albright syndrome, sample sizes may remain limited despite the multinational approach, potentially restricting statistical power and subgroup analyses.

Background: Large-scale international real-world evidence generation benefits from terminology harmonization. Despite widespread adoption of standardized vocabularies, their effective use and long-term sustainability at scale remain poorly understood. Objective: This study aimed to examine real-world terminology and code use in data across a federated network of observational data sources. Methods: We conducted a 2-part survey of researchers and data owners within the Observational Health Data Sciences and Informatics community on their terminology use, challenges, and needs, accompanied by the analysis of code use across a subset of real-world data sources. Results: The survey covered 144 institutions across the United States, the United Kingdom, Europe, Asia, and Africa. Data on terminology use covered 60 sources, including 22 data sources that provided detailed code-level use information. We observed significant variations in terminology use, with 61 out of 89 terminologies used in the data present in less than 10% of the data sources. Code use even after data harmonization was also highly variable: less than 1% (95/742,337) of codes were found in all data sources. Mapping and hierarchy completeness, terminology coverage, versioning, and terminology changes were among the most common challenges. We outlined several of our subsequent process improvements: community contribution and stewardship pipelines, metadata for relationships, and informatics tools for assessment of the impact of terminology change. Conclusions: Terminology and coding inconsistencies across observational data sources require a standardized terminology system. Such a system is complex and time-consuming and needs community contribution and informatics solutions for harmonization to be scalable and sustainable. Even with a common reference standard, high heterogeneity of terminology and code use across different observational data sources remains.

Objective: Despite the increased availability of electronic health records, open-source standardized data collection to facilitate high-resolution data during extracorporeal life support (ECLS) is lacking. This project aimed to assess the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for interoperability to store data sufficiently generated in the context of ECLS and to develop a custom data model expansion in case the OMOP CDM proved insufficient. Design and Setting: The OMOP CDM was analyzed qualitatively by expert consensus for its capability to capture data relative to ECLS as well as the presence of fitting ECLS-related concepts. Database entries necessary to store information about primary ECLS components were compared using the OMOP CDM versus the custom data model expansion. Main Results: Analysis of data elements required to capture ECLS data within the OMOP CDM revealed a paucity of suitable concepts within the OHDSI Standardized Vocabularies, limiting capture of ECLS circuit-derived data. Custom ECLS-specific database tables and novel concepts were introduced as part of a custom expansion, the ECLS Common Data Model (ECLS CDM). The number of database entries necessary to store ECLS use cases was reduced by up to 90%. The ECLS CDM was released as an open-source project on GitHub and placed in the public domain. Conclusions: With the first iteration of the ECLS CDM, we introduce a data model to improve interoperability for data describing ECLS and elevate data quality, enabling multi-center research and quality initiatives.

Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.

The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization. We created the OHDSI Standardized Vocabularies—a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts. The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research—the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting. OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

UNSTRUCTURED Practitioners of digital health are familiar with disjointed data environments that often inhibit effective communication among different elements of the ecosystem. This fragmentation leads in turn to issues such as inconsistencies in services versus payments, wastage, and notably, care delivered being less than best-practice. Despite the long-standing recognition of interoperable data as a potential solution, efforts in achieving interoperability have been disjointed and inconsistent, resulting in numerous incompatible standards, despite the widespread agreement that fewer standards would enhance interoperability. This paper introduces a framework for understanding health care data needs, discussing the challenges and opportunities of open data standards in the field. It emphasizes the necessity of acknowledging diverse data standards, each catering to specific viewpoints and needs, while proposing a categorization of health care data into three domains, each with its distinct characteristics and challenges, along with outlining overarching design requirements applicable to all domains and specific requirements unique to each domain.

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Easy access to large quantities of accurate health data is required to understand medical and scientific information in real-time; evaluate public health measures before, during, and after times of crisis; and prevent medical errors. Introducing a system in the USA that allows for efficient access to such health data and ensures auditability of data facts, while avoiding data silos, will require fundamental changes in current practices. Here, we recommend the implementation of standardized data collection and transmission systems, universal identifiers for individual patients and end users, a reference standard infrastructure to support calibration and integration of laboratory results from equivalent tests, and modernized working practices. Requiring comprehensive and binding standards, rather than incentivizing voluntary and often piecemeal efforts for data exchange, will allow us to achieve the analytical information environment that patients need.