Implementation of stroke teams and simulation training shortened process times in a regional stroke network—a network-wide prospective trial

Background: To meet the requirements imposed by the time-dependency of acute stroke therapies, it is necessary 1) to initiate structural and cultural changes in the breadth of stroke-ready hospitals and 2) to find new wa
Background: To meet the requirements imposed by the time-dependency of acute stroke therapies, it is necessary 1) to initiate structural and cultural changes in the breadth of stroke-ready hospitals and 2) to find new ways to train the personnel treating patients with acute stroke. We aimed to implement and validate a composite intervention of a stroke team algorithm and simulation-based stroke team training as an effective quality initiative in our regional interdisciplinary neurovascular network consisting of 7 stroke units.
Methods: We recorded door-to-needle times of all consecutive stroke patients receiving thrombolysis at seven stroke units for 3 months before and after a 2 month intervention which included setting up a team-based stroke workflow at each stroke unit, a train-the-trainer seminar for stroke team simulation training and a stroke team simulation training session at each hospital as well as a recommendation to take up regular stroke team trainings.
Results: The intervention reduced the network-wide median door-to-needle time by 12 minutes from 43,0 (IQR 29,8–60,0, n = 122) to 31,0 (IQR 24,0–42,0, n = 112) minutes (p < 0.001) and substantially increased the share of patients receiving thrombolysis within 30 minutes of hospital arrival from 41.5% to 59.6% (p < 0.001). Stroke team training participants stated a significant increase in knowledge on the topic of acute stroke care and in the perception of patient safety. The overall course concept was regarded as highly useful by most participants from different professional backgrounds.
Conclusions: The composite intervention of a binding team-based algorithm and stroke team simulation training showed to be well-transferable in our regional stroke network. We provide suggestions and materials for similar campaigns in other stroke networks.
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Metadaten
Author:Damla Tahtali, Ferdinand Bohmann, Natalia Kurka, Peter Rostek, Anelia Todorova-Rudolph, Martin Buchkremer, Mario Abruscato, Ann-Kathrin Hartmetz, Andrea Kuhlmann, Christian Henke, André Stegemann, Sanjay Menon, Björn Misselwitz, Anke Reihs, Stefan Weidauer, Sven Thonke, Uta Meyding-Lamadé, Oliver Caspar Singer, Helmuth Steinmetz, Waltraud Pfeilschifter
URN:urn:nbn:de:hebis:30:3-344087
DOI:http://dx.doi.org/10.1371/journal.pone.0188231
ISSN:1932-6203
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=29206838
Parent Title (English):PLoS one
Publisher:PLoS
Place of publication:Lawrence, Kan.
Contributor(s):Jens Minnerup
Document Type:Article
Language:English
Year of Completion:2017
Date of first Publication:2017/12/05
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2017/12/12
Tag:Critical care and emergency medicine; Neural networks; Nurses; Physicians; Radiology and imaging; Simulation and modeling; Stroke; Technicians
Volume:12
Issue:(12): e0188231
Pagenumber:13
First Page:1
Last Page:13
Note:
opyright: © 2017 Tahtali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HeBIS PPN:426120000
Institutes:Medizin
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0

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