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 Fahad Ahmed Satti1 · Taqdir Ali2 · Jamil Hussain1 · Wajahat Ali Khan3 · Asad Masood Khattak4 · Sungyoung Lee1 Received: 29 September 2019 / Accepted: 31 July 2020 © Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract 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. Keywords Big data · Healthcare information systems · Data curation · Data interoperability Mathematics Subject Classification 68U35 · 68P20
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00607020-00837-2) contains supplementary material, which is available to authorized users.
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Sungyoung Lee [email protected]
Extended author information available on the last page of the article
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1 Introduction In the last decade, the digital healthcare space has witnessed a rapid technological expansion, which has led to the development and deployment of a plethora of policies, software and devices [45]. As a result, the quality and quantity of healthcare delivery, in terms of diagnostics, treatment, and follow-up has greatly improved [47,74]. Additionally, supplementary healthcare sources, such as whole-genome sequencing[73], precision medicine [57], Clinical Practice Guidelines (CPGs) [37], and medical Internet of Things (IoT), and others have added new dimensions, to medical data. Today, healthcare data is characterized [40] by its large Volume (number of patients, size of patient data, additional information), Velocity (production rate, which can range from
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