Clinical Informatics Service, Hospital Clínic de Barcelona. 08036 - Barcelona, Spain.
Institute for Bioengineering of Catalonia, 08028 - Barcelona, Spain.
Stud Health Technol Inform. 2024 Aug 22;316:1432-1436. doi: 10.3233/SHTI240681.
Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM. We examine flexibility and scalability in the management of new data sources, CDM updates, and the adoption of new CDMs. OntoBridge showed greater flexibility in integrating new data sources and adapting to CDM updates. It was also more scalable, facilitating the adoption of various CDMs like i2b2, unlike traditional methods reliant on OMOP-specific tools developed by OHDSI. In summary, while traditional ETL provides a structured approach to data integration, OntoBridge offers a more flexible, scalable, and maintenance-efficient alternative.
通用数据模型（CDMs）增强了来自不同来源的数据交换和集成，同时保留了语义和上下文。将本地数据转换为 CDM 通常是繁琐且资源密集型的，并且可重用性有限。本文比较了 OntoBridge，这是一种基于本体的工具，旨在简化将本地数据集转换为 CDM 的过程，同时还比较了传统的 ETL 方法在采用 OMOP CDM 方面的情况。我们研究了在管理新数据源、CDM 更新和采用新 CDM 方面的灵活性和可扩展性。OntoBridge 在集成新数据源和适应 CDM 更新方面具有更大的灵活性。它也更加可扩展，能够促进各种 CDM 的采用，如 i2b2，而不像传统方法那样依赖于 OHDSI 开发的特定于 OMOP 的工具。总之，虽然传统的 ETL 提供了一种结构化的数据集成方法，但 OntoBridge 提供了一种更灵活、更可扩展和更易于维护的替代方法。
