The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

Vasileios C. Pezoulas, Dimitrios I. Fotiadis
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Abstract

Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant. Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models. Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain. Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.
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数据协调在全球医疗保健变革中的关键作用:框架和案例研究
目的:数据协调可使医疗信息标准化,提高可访问性和互操作性,这对于改善患者治疗效果、推动医学研究和创新至关重要。它能够实现精确诊断和个性化治疗,并提高人工智能模型的效率。然而,伦理问题、数据生命周期中的技术壁垒、人工智能偏见和不同地区的法规等重大挑战阻碍了进展,突出表明需要采用通用标准(如 HL7 FHIR)等解决方案,而在这些方面,缺乏普遍的协调努力是非常重要的。方法:我们提出了一个先进的整体框架,该框架利用符合 FAIR 标准的参考本体(基于 FAIRplus 和 FAIR CookBook 标准)来实现数据的可查找、可访问、可互操作和可重用性,并使用来自 OHDSI(观测健康数据科学与信息学)词汇表的术语和词嵌入来识别异构数据模型之间的词汇和概念重叠。结果:所提出的方法适用于自身免疫性疾病、心血管疾病和精神障碍,使用的非结构化数据来自欧盟队列,其中包括 7551 名原发性斯约格伦综合征患者、25000 名心血管疾病患者以及 3500 名抑郁和焦虑症患者。这些数据集的元数据被编入字典,并与三个新开发的参考本体(ROPSS、ROCVD 和 ROMD)相链接,这些本体可在 GitHub 上访问。这些本体促进了不同系统间的数据互操作性,并有助于在每个领域内高精度地识别通用术语。结论通过所提出的框架,我们旨在敦促将数据协调作为优先事项,强调全球合作、技术和基础设施投资以及遵守数据使用道德规范的必要性,从而建立一个更高效、更以患者为中心的全球医疗保健系统。
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