Cargando…

Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study

BACKGROUND: Human workload is a key factor for system performance, but data on emergency medical services (EMS) are scarce. We investigated paramedics’ workload and the influencing factors for non-emergency medical transfers. These missions make up a major part of EMS activities in Germany and are g...

Descripción completa

Detalles Bibliográficos
Autores principales: Keunecke, Johann Georg, Gall, Christine, Birkholz, Torsten, Moritz, Andreas, Eiche, Christian, Prottengeier, Johannes
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6836439/
https://www.ncbi.nlm.nih.gov/pubmed/31699084
http://dx.doi.org/10.1186/s12913-019-4638-4
_version_ 1783466906688159744
author Keunecke, Johann Georg
Gall, Christine
Birkholz, Torsten
Moritz, Andreas
Eiche, Christian
Prottengeier, Johannes
author_facet Keunecke, Johann Georg
Gall, Christine
Birkholz, Torsten
Moritz, Andreas
Eiche, Christian
Prottengeier, Johannes
author_sort Keunecke, Johann Georg
collection PubMed
description BACKGROUND: Human workload is a key factor for system performance, but data on emergency medical services (EMS) are scarce. We investigated paramedics’ workload and the influencing factors for non-emergency medical transfers. These missions make up a major part of EMS activities in Germany and are growing steadily in number. METHODS: Paramedics rated missions retrospectively through an online questionnaire. We used the NASA-Task Load Index (TLX) to quantify workload and asked about a variety of medical and procedural aspects for each mission. Teamwork was assessed by the Weller teamwork measurement tool (TMT). With a multiple linear regression model, we identified a set of factors leading to relevant increases or decreases in workload. RESULTS: A total of 194 non-emergency missions were analysed. Global workload was rated low (Mean = 27/100). In summary, 42.8% of missions were rated with a TLX under 20/100. TLX subscales revealed low task demands but a very positive self-perception of performance (Mean = 15/100). Teamwork gained high ratings (Mean TMT = 5.8/7), and good teamwork led to decreases in workload. Aggression events originating from patients and bystanders occurred frequently (n = 25, 12.9%) and increased workload significantly. Other factors affecting workload were the patient’s body weight and the transfer of patients with transmittable pathogens. CONCLUSION: The workload during non-emergency medical transfers was low to very low, but performance perception was very positive, and no indicators of task underload were found. We identified several factors that led to workload increases. Future measures should attempt to better train paramedics for aggression incidents, to explore the usefulness of further technical aids in the transfer of obese patients and to reconsider standard operating procedures for missions with transmittable pathogens.
format Online
Article
Text
id pubmed-6836439
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-68364392019-11-08 Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study Keunecke, Johann Georg Gall, Christine Birkholz, Torsten Moritz, Andreas Eiche, Christian Prottengeier, Johannes BMC Health Serv Res Research Article BACKGROUND: Human workload is a key factor for system performance, but data on emergency medical services (EMS) are scarce. We investigated paramedics’ workload and the influencing factors for non-emergency medical transfers. These missions make up a major part of EMS activities in Germany and are growing steadily in number. METHODS: Paramedics rated missions retrospectively through an online questionnaire. We used the NASA-Task Load Index (TLX) to quantify workload and asked about a variety of medical and procedural aspects for each mission. Teamwork was assessed by the Weller teamwork measurement tool (TMT). With a multiple linear regression model, we identified a set of factors leading to relevant increases or decreases in workload. RESULTS: A total of 194 non-emergency missions were analysed. Global workload was rated low (Mean = 27/100). In summary, 42.8% of missions were rated with a TLX under 20/100. TLX subscales revealed low task demands but a very positive self-perception of performance (Mean = 15/100). Teamwork gained high ratings (Mean TMT = 5.8/7), and good teamwork led to decreases in workload. Aggression events originating from patients and bystanders occurred frequently (n = 25, 12.9%) and increased workload significantly. Other factors affecting workload were the patient’s body weight and the transfer of patients with transmittable pathogens. CONCLUSION: The workload during non-emergency medical transfers was low to very low, but performance perception was very positive, and no indicators of task underload were found. We identified several factors that led to workload increases. Future measures should attempt to better train paramedics for aggression incidents, to explore the usefulness of further technical aids in the transfer of obese patients and to reconsider standard operating procedures for missions with transmittable pathogens. BioMed Central 2019-11-07 /pmc/articles/PMC6836439/ /pubmed/31699084 http://dx.doi.org/10.1186/s12913-019-4638-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Keunecke, Johann Georg
Gall, Christine
Birkholz, Torsten
Moritz, Andreas
Eiche, Christian
Prottengeier, Johannes
Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
title Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
title_full Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
title_fullStr Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
title_full_unstemmed Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
title_short Workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
title_sort workload and influencing factors in non-emergency medical transfers: a multiple linear regression analysis of a cross-sectional questionnaire study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6836439/
https://www.ncbi.nlm.nih.gov/pubmed/31699084
http://dx.doi.org/10.1186/s12913-019-4638-4
work_keys_str_mv AT keuneckejohanngeorg workloadandinfluencingfactorsinnonemergencymedicaltransfersamultiplelinearregressionanalysisofacrosssectionalquestionnairestudy
AT gallchristine workloadandinfluencingfactorsinnonemergencymedicaltransfersamultiplelinearregressionanalysisofacrosssectionalquestionnairestudy
AT birkholztorsten workloadandinfluencingfactorsinnonemergencymedicaltransfersamultiplelinearregressionanalysisofacrosssectionalquestionnairestudy
AT moritzandreas workloadandinfluencingfactorsinnonemergencymedicaltransfersamultiplelinearregressionanalysisofacrosssectionalquestionnairestudy
AT eichechristian workloadandinfluencingfactorsinnonemergencymedicaltransfersamultiplelinearregressionanalysisofacrosssectionalquestionnairestudy
AT prottengeierjohannes workloadandinfluencingfactorsinnonemergencymedicaltransfersamultiplelinearregressionanalysisofacrosssectionalquestionnairestudy