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Pathling: analytics on FHIR

BACKGROUND: Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in t...

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Autores principales: Grimes, John, Szul, Piotr, Metke-Jimenez, Alejandro, Lawley, Michael, Loi, Kylynn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455941/
https://www.ncbi.nlm.nih.gov/pubmed/36076268
http://dx.doi.org/10.1186/s13326-022-00277-1
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author Grimes, John
Szul, Piotr
Metke-Jimenez, Alejandro
Lawley, Michael
Loi, Kylynn
author_facet Grimes, John
Szul, Piotr
Metke-Jimenez, Alejandro
Lawley, Michael
Loi, Kylynn
author_sort Grimes, John
collection PubMed
description BACKGROUND: Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in the task of analyzing Fast Healthcare Interoperability Resources (FHIR(®)) data and associated clinical terminology. RESULTS: A server implementation was developed, featuring a FHIR API with new operations designed to support exploratory data analysis (EDA), advanced patient cohort selection and data preparation tasks. Integration with a FHIR Terminology Service is also supported, allowing users to incorporate knowledge from rich terminologies such as SNOMED CT within their queries. A prototype user interface for EDA was developed, along with visualizations in support of a health data analysis project. CONCLUSIONS: Experience with applying this technology within research projects and towards the development of analytics-enabled applications provides a preliminary indication that the FHIR Analytics API pattern implemented by Pathling is a valuable abstraction for data scientists and software developers within the health care domain. Pathling contributes towards the value proposition for the use of FHIR within health data analytics, and assists with the use of complex clinical terminologies in that context. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13326-022-00277-1.
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spelling pubmed-94559412022-09-09 Pathling: analytics on FHIR Grimes, John Szul, Piotr Metke-Jimenez, Alejandro Lawley, Michael Loi, Kylynn J Biomed Semantics Software BACKGROUND: Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in the task of analyzing Fast Healthcare Interoperability Resources (FHIR(®)) data and associated clinical terminology. RESULTS: A server implementation was developed, featuring a FHIR API with new operations designed to support exploratory data analysis (EDA), advanced patient cohort selection and data preparation tasks. Integration with a FHIR Terminology Service is also supported, allowing users to incorporate knowledge from rich terminologies such as SNOMED CT within their queries. A prototype user interface for EDA was developed, along with visualizations in support of a health data analysis project. CONCLUSIONS: Experience with applying this technology within research projects and towards the development of analytics-enabled applications provides a preliminary indication that the FHIR Analytics API pattern implemented by Pathling is a valuable abstraction for data scientists and software developers within the health care domain. Pathling contributes towards the value proposition for the use of FHIR within health data analytics, and assists with the use of complex clinical terminologies in that context. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13326-022-00277-1. BioMed Central 2022-09-08 /pmc/articles/PMC9455941/ /pubmed/36076268 http://dx.doi.org/10.1186/s13326-022-00277-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Grimes, John
Szul, Piotr
Metke-Jimenez, Alejandro
Lawley, Michael
Loi, Kylynn
Pathling: analytics on FHIR
title Pathling: analytics on FHIR
title_full Pathling: analytics on FHIR
title_fullStr Pathling: analytics on FHIR
title_full_unstemmed Pathling: analytics on FHIR
title_short Pathling: analytics on FHIR
title_sort pathling: analytics on fhir
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455941/
https://www.ncbi.nlm.nih.gov/pubmed/36076268
http://dx.doi.org/10.1186/s13326-022-00277-1
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