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Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study

OBJECTIVE: ‘Code Stroke’ (Code) is used in health services to streamline hyperacute assessment and treatment delivery for patients with ischaemic stroke. However, there are few studies that detail the time spent on individual components performed during a Code. We sought to quantify the time taken f...

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Autores principales: Wong, Joseph Zhi Wen, Park, Peter Si Woo, Frost, Tanya, Stephens, Karen, Newk-Fon Hey Tow, Foong Kien, Garcia, Pamela Gayle, Senanayake, Channa, Choi, Philip M C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884893/
https://www.ncbi.nlm.nih.gov/pubmed/36697041
http://dx.doi.org/10.1136/bmjopen-2022-067816
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author Wong, Joseph Zhi Wen
Park, Peter Si Woo
Frost, Tanya
Stephens, Karen
Newk-Fon Hey Tow, Foong Kien
Garcia, Pamela Gayle
Senanayake, Channa
Choi, Philip M C
author_facet Wong, Joseph Zhi Wen
Park, Peter Si Woo
Frost, Tanya
Stephens, Karen
Newk-Fon Hey Tow, Foong Kien
Garcia, Pamela Gayle
Senanayake, Channa
Choi, Philip M C
author_sort Wong, Joseph Zhi Wen
collection PubMed
description OBJECTIVE: ‘Code Stroke’ (Code) is used in health services to streamline hyperacute assessment and treatment delivery for patients with ischaemic stroke. However, there are few studies that detail the time spent on individual components performed during a Code. We sought to quantify the time taken for each process during a Code and investigate associations with modifiable and non-modifiable factors. DESIGN: Continuous observation workflow time study. SETTING AND PARTICIPANTS: Recordings of 100 Codes were performed at a high-volume primary stroke centre in Melbourne, Australia, between January and June 2020 using a body camera worn by a member of the stroke team. MAIN OUTCOME MEASURES: The main measures included the overall duration of Codes and the individual processes within the Code workflow. Associations between variables of interest and process times were explored using linear regression models. RESULTS: 100 Codes were captured, representing 19.2% of all Codes over the 6 months. The median duration of a complete Code was 54.2 min (IQR 39.1–74.7). Administrative work performed after treatment is completed (median 21.0 min (IQR 9.8–31.4)); multimodal CT imaging (median 13.0 min (IQR 11.5–15.7)), and time between decision and thrombolysis administration (median 8.1 min (IQR 6.1–10.8)) were the longest components of a Code. Tenecteplase was able to be prepared faster than alteplase (median 1.8 vs 4.9 min, p=0.02). The presence of a second junior doctor was associated with shorter administrative work time (median 10.3 vs 25.1 min, p<0.01). No specific modifiable factors were found to be associated with shorter overall Code duration. CONCLUSIONS: Codes are time intensive. Time spent on decision-making was a relatively small component of the overall Code duration. Data from body cameras can provide granular data on all aspects of Code workflow to inform potential areas for improvement at individual centres.
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spelling pubmed-98848932023-01-31 Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study Wong, Joseph Zhi Wen Park, Peter Si Woo Frost, Tanya Stephens, Karen Newk-Fon Hey Tow, Foong Kien Garcia, Pamela Gayle Senanayake, Channa Choi, Philip M C BMJ Open Neurology OBJECTIVE: ‘Code Stroke’ (Code) is used in health services to streamline hyperacute assessment and treatment delivery for patients with ischaemic stroke. However, there are few studies that detail the time spent on individual components performed during a Code. We sought to quantify the time taken for each process during a Code and investigate associations with modifiable and non-modifiable factors. DESIGN: Continuous observation workflow time study. SETTING AND PARTICIPANTS: Recordings of 100 Codes were performed at a high-volume primary stroke centre in Melbourne, Australia, between January and June 2020 using a body camera worn by a member of the stroke team. MAIN OUTCOME MEASURES: The main measures included the overall duration of Codes and the individual processes within the Code workflow. Associations between variables of interest and process times were explored using linear regression models. RESULTS: 100 Codes were captured, representing 19.2% of all Codes over the 6 months. The median duration of a complete Code was 54.2 min (IQR 39.1–74.7). Administrative work performed after treatment is completed (median 21.0 min (IQR 9.8–31.4)); multimodal CT imaging (median 13.0 min (IQR 11.5–15.7)), and time between decision and thrombolysis administration (median 8.1 min (IQR 6.1–10.8)) were the longest components of a Code. Tenecteplase was able to be prepared faster than alteplase (median 1.8 vs 4.9 min, p=0.02). The presence of a second junior doctor was associated with shorter administrative work time (median 10.3 vs 25.1 min, p<0.01). No specific modifiable factors were found to be associated with shorter overall Code duration. CONCLUSIONS: Codes are time intensive. Time spent on decision-making was a relatively small component of the overall Code duration. Data from body cameras can provide granular data on all aspects of Code workflow to inform potential areas for improvement at individual centres. BMJ Publishing Group 2023-01-25 /pmc/articles/PMC9884893/ /pubmed/36697041 http://dx.doi.org/10.1136/bmjopen-2022-067816 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Neurology
Wong, Joseph Zhi Wen
Park, Peter Si Woo
Frost, Tanya
Stephens, Karen
Newk-Fon Hey Tow, Foong Kien
Garcia, Pamela Gayle
Senanayake, Channa
Choi, Philip M C
Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study
title Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study
title_full Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study
title_fullStr Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study
title_full_unstemmed Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study
title_short Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: a continuous observation workflow time study
title_sort using body cameras to quantify the duration of a code stroke and identify workflow issues: a continuous observation workflow time study
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884893/
https://www.ncbi.nlm.nih.gov/pubmed/36697041
http://dx.doi.org/10.1136/bmjopen-2022-067816
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