Data interoperability in health surveillance: a literature review to support the development of One Health frameworks release_wkd6s3chrvbu5oigpwjmkpmwyq

by Dórea, F.C., Oliveira V. H. S.

Published by Zenodo.

2022  

Abstract

This literature review aimed to investigate the state-of-art in data interoperability solutions in health surveillance. Focus was given to the identification of methodologies which can support the construction of a framework for One Health (OH) Surveillance. As OH relies on data reuse across multiple health sectors, and preservation of the original context of the data is fundamental for correct inference and decision making, particular focus was given to semantic interoperability (ensuring the integrity and meaning of the data across systems). Papers were grouped and presented by the context of interoperability application. Within each context, we highlighted interoperability needs and the solutions reported. In all contexts presented, however, a clear narrative was repeated, with approaches based on the use of data models and coding of data based on specific terminologies (schema representation approaches) being replaced by knowledge modeling and ontology-based solutions as systems evolved, and in particular as the complexity of the data usage context increased. While challenges to implementation in practice and scalability are still present, the evidence brought up by this review made it clear that it is possible to build a framework of OHS that is designed as a knowledge layer capable of connecting data on demand, while preserving data sources context and structure.
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