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

Vasileios C. Pezoulas, Dimitrios I. Fotiadis
{"title":"The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study","authors":"Vasileios C. Pezoulas, Dimitrios I. Fotiadis","doi":"10.20517/chatmed.2023.37","DOIUrl":null,"url":null,"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.\n 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.\n 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.\n 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.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"74 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Connected health and telemedicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20517/chatmed.2023.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference Validation of deep learning models for cuffless blood pressure estimation on a large benchmarking dataset A multi-channel photoplethysmography array with contact-force regulation for tonoarteriographic imaging
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1