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ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers
The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749730/ https://www.ncbi.nlm.nih.gov/pubmed/29293556 http://dx.doi.org/10.1371/journal.pone.0190028 |
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author | Teodoro, Douglas Sundvall, Erik João Junior, Mario Ruch, Patrick Miranda Freire, Sergio |
author_facet | Teodoro, Douglas Sundvall, Erik João Junior, Mario Ruch, Patrick Miranda Freire, Sergio |
author_sort | Teodoro, Douglas |
collection | PubMed |
description | The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms. |
format | Online Article Text |
id | pubmed-5749730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-57497302018-01-26 ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers Teodoro, Douglas Sundvall, Erik João Junior, Mario Ruch, Patrick Miranda Freire, Sergio PLoS One Research Article The openEHR specifications are designed to support implementation of flexible and interoperable Electronic Health Record (EHR) systems. Despite the increasing number of solutions based on the openEHR specifications, it is difficult to find publicly available healthcare datasets in the openEHR format that can be used to test, compare and validate different data persistence mechanisms for openEHR. To foster research on openEHR servers, we present the openEHR Benchmark Dataset, ORBDA, a very large healthcare benchmark dataset encoded using the openEHR formalism. To construct ORBDA, we extracted and cleaned a de-identified dataset from the Brazilian National Healthcare System (SUS) containing hospitalisation and high complexity procedures information and formalised it using a set of openEHR archetypes and templates. Then, we implemented a tool to enrich the raw relational data and convert it into the openEHR model using the openEHR Java reference model library. The ORBDA dataset is available in composition, versioned composition and EHR openEHR representations in XML and JSON formats. In total, the dataset contains more than 150 million composition records. We describe the dataset and provide means to access it. Additionally, we demonstrate the usage of ORBDA for evaluating inserting throughput and query latency performances of some NoSQL database management systems. We believe that ORBDA is a valuable asset for assessing storage models for openEHR-based information systems during the software engineering process. It may also be a suitable component in future standardised benchmarking of available openEHR storage platforms. Public Library of Science 2018-01-02 /pmc/articles/PMC5749730/ /pubmed/29293556 http://dx.doi.org/10.1371/journal.pone.0190028 Text en © 2018 Teodoro et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Teodoro, Douglas Sundvall, Erik João Junior, Mario Ruch, Patrick Miranda Freire, Sergio ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers |
title | ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers |
title_full | ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers |
title_fullStr | ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers |
title_full_unstemmed | ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers |
title_short | ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers |
title_sort | orbda: an openehr benchmark dataset for performance assessment of electronic health record servers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749730/ https://www.ncbi.nlm.nih.gov/pubmed/29293556 http://dx.doi.org/10.1371/journal.pone.0190028 |
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