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Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability
Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability
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Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability
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Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability
Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability

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Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability
Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability
Journal Article

Ubiquitous Health Profile (UHPr): a big data curation platform for supporting health data interoperability

2020
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Overview
The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.