Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment

Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data’ diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards’ requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Systems Springer Journals

Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment

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Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Medicine & Public Health; Health Informatics; Health Informatics; Statistics for Life Sciences, Medicine, Health Sciences
ISSN
0148-5598
eISSN
1573-689X
D.O.I.
10.1007/s10916-019-1183-y
Publisher site
See Article on Publisher Site

Abstract

Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data’ diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards’ requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism.

Journal

Journal of Medical SystemsSpringer Journals

Published: Feb 5, 2019

References

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