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An IoT-Based GeoData Production System Deployed in a Hospital

Navigation in large hospitals remains a challenge, especially for patients, visitors and, in some cases, for staff, but in particular it is notable in the case of tracking ambulatory equipment. Current techniques generally seek to reproduce what outdoor navigation systems provide, i.e., “good” accur...

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Autores principales: Samama, Nel, Patarot, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966547/
https://www.ncbi.nlm.nih.gov/pubmed/36850684
http://dx.doi.org/10.3390/s23042086
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author Samama, Nel
Patarot, Alexandre
author_facet Samama, Nel
Patarot, Alexandre
author_sort Samama, Nel
collection PubMed
description Navigation in large hospitals remains a challenge, especially for patients, visitors and, in some cases, for staff, but in particular it is notable in the case of tracking ambulatory equipment. Current techniques generally seek to reproduce what outdoor navigation systems provide, i.e., “good” accuracy. In many cases, especially in hospitals, reliability is much more important than accuracy. We show that it is possible to realize a simple, reliable system with a low accuracy, but which perfectly fulfills the task assigned in the particular case of tracking stretchers. Optimizing the use of hospital equipment requires the knowledge of its movement. The possibility to access equipment location in real time as well as on the knowledge of the time necessary to move it between two locations allows to predict or to estimate the load and possibly to scale the necessary number of stretchers, and thus the availability of the stretcher bearers. In this paper, an approach of the real-time location of these devices is proposed, and it is called “symbolic”. The principle is described, as well as the practical implementation and the data that can be retrieved. In the second part, an analysis of the results obtained is provided in two directions: the location of stretchers and the determination of travel times. The methodology followed is described, and it is shown that a correct positioning rate of 90% is reached, which is slightly lower than expected, explained by the chosen practical implementation. Moreover, the average error on the determination of travel times is approximately ten seconds on 2 to 7 min trips. The “reliability” (the terminology of which is discussed at the end of the paper) of the results is related to the simplicity of the approach.
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spelling pubmed-99665472023-02-26 An IoT-Based GeoData Production System Deployed in a Hospital Samama, Nel Patarot, Alexandre Sensors (Basel) Article Navigation in large hospitals remains a challenge, especially for patients, visitors and, in some cases, for staff, but in particular it is notable in the case of tracking ambulatory equipment. Current techniques generally seek to reproduce what outdoor navigation systems provide, i.e., “good” accuracy. In many cases, especially in hospitals, reliability is much more important than accuracy. We show that it is possible to realize a simple, reliable system with a low accuracy, but which perfectly fulfills the task assigned in the particular case of tracking stretchers. Optimizing the use of hospital equipment requires the knowledge of its movement. The possibility to access equipment location in real time as well as on the knowledge of the time necessary to move it between two locations allows to predict or to estimate the load and possibly to scale the necessary number of stretchers, and thus the availability of the stretcher bearers. In this paper, an approach of the real-time location of these devices is proposed, and it is called “symbolic”. The principle is described, as well as the practical implementation and the data that can be retrieved. In the second part, an analysis of the results obtained is provided in two directions: the location of stretchers and the determination of travel times. The methodology followed is described, and it is shown that a correct positioning rate of 90% is reached, which is slightly lower than expected, explained by the chosen practical implementation. Moreover, the average error on the determination of travel times is approximately ten seconds on 2 to 7 min trips. The “reliability” (the terminology of which is discussed at the end of the paper) of the results is related to the simplicity of the approach. MDPI 2023-02-13 /pmc/articles/PMC9966547/ /pubmed/36850684 http://dx.doi.org/10.3390/s23042086 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Samama, Nel
Patarot, Alexandre
An IoT-Based GeoData Production System Deployed in a Hospital
title An IoT-Based GeoData Production System Deployed in a Hospital
title_full An IoT-Based GeoData Production System Deployed in a Hospital
title_fullStr An IoT-Based GeoData Production System Deployed in a Hospital
title_full_unstemmed An IoT-Based GeoData Production System Deployed in a Hospital
title_short An IoT-Based GeoData Production System Deployed in a Hospital
title_sort iot-based geodata production system deployed in a hospital
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966547/
https://www.ncbi.nlm.nih.gov/pubmed/36850684
http://dx.doi.org/10.3390/s23042086
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