Definitions to data flow: Operationalizing MIABIS in HL7 FHIR.

IF 4.5 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Radovan Tomasik, Simon Konar, Niina Eklund, Cäcilia Engels, Zdenka Dudova, Radoslava Kacova, Roman Hrstka, Petr Holub
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引用次数: 0

Abstract

Objective: Biobanks and biomolecular resources are increasingly central to data-driven biomedical research, encompassing not only metadata but also granular, sample-related data from diverse sources such as healthcare systems, national registries, and research outputs. However, the lack of a standardised, machine-readable format for representing such data limits interoperability, data reuse and integration into clinical and research environments. While MIABIS provides a conceptual model for biobank data, its abstract nature and reliance on heterogeneous implementations create barriers to practical, scalable adoption. This study presents a pragmatic, operational implementation of MIABIS focused on enabling real-world exchange and integration of sample-level data.

Methods: We systematically evaluated established data exchange standards, comparing HL7 FHIR and OMOP CDM with respect to their suitability for structuring sample-related data in a semantically robust and machine-readable form. Based on this analysis, we developed a FHIR-based representation of MIABIS that supports complex biobank structures and enables integration with federated data infrastructures. Supporting tools, including a Python library and an implementation guide, were created to ensure usability across diverse research and clinical contexts.

Results: We created nine interoperable FHIR profiles covering core MIABIS entities, ensuring consistency with FHIR standards. To support adoption, we developed an open-source Python library that abstracts FHIR interactions and provides schema validation for MIABIS-compliant data. The library was integrated into an ETL tool in operation at Czech Node of BBMRI-ERIC, European Biobanking and Biomolecular Resources Research Infrastructure, to demonstrate usability with real-world sample-related data. Separately, we validated the representation of MIABIS entities at the organisational level by converting the data structures of BBMRI-ERIC Directory into FHIR, demonstrating compatibility with federated data infrastructures.

Conclusion: This work delivers a machine-readable, interoperable implementation of MIABIS, enabling the exchange of both organisational and sample-level data across biobanks and health information systems. By integrating MIABIS with HL7 FHIR, we provide a host of reusable tools and mechanisms for further evolution of the data model. Combined, these benefits can help with the integration into clinical and research workflows, supporting data discoverability, reuse, and cross-institutional collaboration in biomedical research.

数据流的定义:在HL7 FHIR中实现MIABIS。
目的:生物银行和生物分子资源在数据驱动的生物医学研究中越来越重要,不仅包括元数据,还包括来自不同来源(如医疗保健系统、国家登记处和研究成果)的颗粒状样本相关数据。然而,缺乏一种标准化的、机器可读的格式来表示这些数据,限制了互操作性、数据重用和临床和研究环境的集成。虽然MIABIS为生物银行数据提供了一个概念模型,但它的抽象性和对异构实现的依赖为实际的、可扩展的采用创造了障碍。本研究提出了一种实用的、可操作的MIABIS实现方法,重点是实现样本级数据的真实交换和集成。方法:我们系统地评估了已建立的数据交换标准,比较了HL7 FHIR和OMOP CDM在以语义鲁棒性和机器可读形式构建样本相关数据方面的适用性。基于这一分析,我们开发了一个基于fhir的MIABIS表示,它支持复杂的生物库结构,并能够与联邦数据基础设施集成。包括Python库和实现指南在内的支持工具被创建,以确保在不同的研究和临床环境中可用性。结果:我们创建了9个可互操作的FHIR配置文件,涵盖了核心MIABIS实体,确保了与FHIR标准的一致性。为了支持采用,我们开发了一个开源Python库,它抽象了FHIR交互,并为符合miabis的数据提供了模式验证。该库被整合到BBMRI-ERIC捷克节点的ETL工具中,欧洲生物银行和生物分子资源研究基础设施,以展示与现实世界样本相关数据的可用性。另外,我们通过将BBMRI-ERIC目录的数据结构转换为FHIR,验证了MIABIS实体在组织级别的表示,展示了与联邦数据基础设施的兼容性。结论:这项工作提供了一个机器可读、可互操作的MIABIS实现,使生物库和卫生信息系统之间的组织和样本级数据交换成为可能。通过将MIABIS与HL7 FHIR集成,我们为数据模型的进一步发展提供了大量可重用的工具和机制。综合起来,这些优势可以帮助整合到临床和研究工作流程中,支持生物医学研究中的数据发现、重用和跨机构协作。
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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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