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A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory
We present a model for drone transport of the complete annual analytic volume of 6.5 million analyses—(routine and emergency) between two inner-city university laboratories at Oslo University Hospital located 1.8 km apart and with a time restriction for the analyses of no more than 60 min. The total...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123595/ https://www.ncbi.nlm.nih.gov/pubmed/33926130 http://dx.doi.org/10.3390/ijerph18094580 |
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author | Johannessen, Karl-Arne Comtet, Hans Fosse, Erik |
author_facet | Johannessen, Karl-Arne Comtet, Hans Fosse, Erik |
author_sort | Johannessen, Karl-Arne |
collection | PubMed |
description | We present a model for drone transport of the complete annual analytic volume of 6.5 million analyses—(routine and emergency) between two inner-city university laboratories at Oslo University Hospital located 1.8 km apart and with a time restriction for the analyses of no more than 60 min. The total laboratory activity was analyzed per min for the complete year of 2018. The time from the clinical ordering of tests to the loading of the drone, drone transport time, and analysis time after the sample arrived at the analyzing laboratory were assessed using the lead time of emergency analyses of C-reactive protein, troponin, and the international normalized ratio. The activity had characteristic diurnal patterns, with the most intensive traffic between 8 and 12 a.m. on weekdays and there being considerably less traffic for the rest of the day, at night and on weekends. Drone schedules with departures 15–60 min apart were simulated. A maximum of 15 min between flights was required to meet the emergency demand for the analyses being completed within 60 min. The required drone weight capacity was below 3.5 kg at all times. In multiple simulations, the drone times were appropriate, whereas variations in the clinic- and laboratory-related time intervals caused violations of the allowed time 50% of the time. Drone transport with regular schedules may potentially improve the transport time compared with traditional ground transport and allow the merging of large laboratories, even when the demand for emergency analyses restricts the maximum transport time. Comprehensive economic evaluations and robust drone technology are needed before such solutions can be ready for implementation. |
format | Online Article Text |
id | pubmed-8123595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81235952021-05-16 A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory Johannessen, Karl-Arne Comtet, Hans Fosse, Erik Int J Environ Res Public Health Article We present a model for drone transport of the complete annual analytic volume of 6.5 million analyses—(routine and emergency) between two inner-city university laboratories at Oslo University Hospital located 1.8 km apart and with a time restriction for the analyses of no more than 60 min. The total laboratory activity was analyzed per min for the complete year of 2018. The time from the clinical ordering of tests to the loading of the drone, drone transport time, and analysis time after the sample arrived at the analyzing laboratory were assessed using the lead time of emergency analyses of C-reactive protein, troponin, and the international normalized ratio. The activity had characteristic diurnal patterns, with the most intensive traffic between 8 and 12 a.m. on weekdays and there being considerably less traffic for the rest of the day, at night and on weekends. Drone schedules with departures 15–60 min apart were simulated. A maximum of 15 min between flights was required to meet the emergency demand for the analyses being completed within 60 min. The required drone weight capacity was below 3.5 kg at all times. In multiple simulations, the drone times were appropriate, whereas variations in the clinic- and laboratory-related time intervals caused violations of the allowed time 50% of the time. Drone transport with regular schedules may potentially improve the transport time compared with traditional ground transport and allow the merging of large laboratories, even when the demand for emergency analyses restricts the maximum transport time. Comprehensive economic evaluations and robust drone technology are needed before such solutions can be ready for implementation. MDPI 2021-04-26 /pmc/articles/PMC8123595/ /pubmed/33926130 http://dx.doi.org/10.3390/ijerph18094580 Text en © 2021 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 Johannessen, Karl-Arne Comtet, Hans Fosse, Erik A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory |
title | A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory |
title_full | A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory |
title_fullStr | A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory |
title_full_unstemmed | A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory |
title_short | A Drone Logistic Model for Transporting the Complete Analytic Volume of a Large-Scale University Laboratory |
title_sort | drone logistic model for transporting the complete analytic volume of a large-scale university laboratory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123595/ https://www.ncbi.nlm.nih.gov/pubmed/33926130 http://dx.doi.org/10.3390/ijerph18094580 |
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