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Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support

This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information syst...

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Autores principales: Semenov, Ilia, Osenev, Roman, Gerasimov, Sergey, Kopanitsa, Georgy, Denisov, Dmitry, Andreychuk, Yuriy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981801/
https://www.ncbi.nlm.nih.gov/pubmed/31861851
http://dx.doi.org/10.3390/ijerph17010073
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author Semenov, Ilia
Osenev, Roman
Gerasimov, Sergey
Kopanitsa, Georgy
Denisov, Dmitry
Andreychuk, Yuriy
author_facet Semenov, Ilia
Osenev, Roman
Gerasimov, Sergey
Kopanitsa, Georgy
Denisov, Dmitry
Andreychuk, Yuriy
author_sort Semenov, Ilia
collection PubMed
description This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.
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spelling pubmed-69818012020-02-07 Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support Semenov, Ilia Osenev, Roman Gerasimov, Sergey Kopanitsa, Georgy Denisov, Dmitry Andreychuk, Yuriy Int J Environ Res Public Health Article This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models. MDPI 2019-12-20 2020-01 /pmc/articles/PMC6981801/ /pubmed/31861851 http://dx.doi.org/10.3390/ijerph17010073 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Semenov, Ilia
Osenev, Roman
Gerasimov, Sergey
Kopanitsa, Georgy
Denisov, Dmitry
Andreychuk, Yuriy
Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
title Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
title_full Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
title_fullStr Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
title_full_unstemmed Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
title_short Experience in Developing an FHIR Medical Data Management Platform to Provide Clinical Decision Support
title_sort experience in developing an fhir medical data management platform to provide clinical decision support
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6981801/
https://www.ncbi.nlm.nih.gov/pubmed/31861851
http://dx.doi.org/10.3390/ijerph17010073
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