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Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia

OBJECTIVES: While CT scanning plays a significant role in healthcare, its increasing use has raised concerns about inappropriate use. This study investigated factors driving the changing use of CT among people admitted to tertiary hospitals in Western Australia (WA). DESIGN AND SETTING: A repeated c...

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Autores principales: Ha, Ninh Thi, Maxwell, Susannah, Bulsara, Max K, Doust, Jenny, Mcrobbie, Donald, O’Leary, Peter, Slavotinek, John, Moorin, Rachael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587703/
https://www.ncbi.nlm.nih.gov/pubmed/34764174
http://dx.doi.org/10.1136/bmjopen-2021-052954
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author Ha, Ninh Thi
Maxwell, Susannah
Bulsara, Max K
Doust, Jenny
Mcrobbie, Donald
O’Leary, Peter
Slavotinek, John
Moorin, Rachael
author_facet Ha, Ninh Thi
Maxwell, Susannah
Bulsara, Max K
Doust, Jenny
Mcrobbie, Donald
O’Leary, Peter
Slavotinek, John
Moorin, Rachael
author_sort Ha, Ninh Thi
collection PubMed
description OBJECTIVES: While CT scanning plays a significant role in healthcare, its increasing use has raised concerns about inappropriate use. This study investigated factors driving the changing use of CT among people admitted to tertiary hospitals in Western Australia (WA). DESIGN AND SETTING: A repeated cross-sectional study of CT use in WA in 2003–2005 and 2013–2015 using linked administrative heath data at the individual patient level. PARTICIPANTS: A total of 2 375 787 tertiary hospital admissions of people aged 18 years or older. MAIN OUTCOME MEASURE: Rate of CT scanning per 1000 hospital admissions. METHODS: A multivariable decomposition model was used to quantify the contribution of changes in patient characteristics and changes in the probability of having a CT over the study period. RESULTS: The rate of CT scanning increased by 112 CT scans per 1000 admissions over the study period. Changes in the distribution of the observed patient characteristics were accounted for 62.7% of the growth in CT use. However, among unplanned admissions, changes in the distribution of patient characteristics only explained 17% of the growth in CT use, the remainder being explained by changes in the probability of having a CT scan. While the relative probability of having a CT scan generally increased over time across most observed characteristics, it reduced in young adults (−2.8%), people living in the rural/remote areas (−0.8%) and people transferred from secondary hospitals (−0.8%). CONCLUSIONS: Our study highlights potential improvements in practice towards reducing medical radiation exposure in certain high risk population. Since changes in the relative probability of having a CT scan (representing changes in scope) rather than changes in the distribution of the patient characteristics (representing changes in need) explained a major proportion of the growth in CT use, this warrants more in-depth investigations in clinical practices to better inform health policies promoting appropriate use of diagnostic imaging tests.
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spelling pubmed-85877032021-11-15 Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia Ha, Ninh Thi Maxwell, Susannah Bulsara, Max K Doust, Jenny Mcrobbie, Donald O’Leary, Peter Slavotinek, John Moorin, Rachael BMJ Open Health Services Research OBJECTIVES: While CT scanning plays a significant role in healthcare, its increasing use has raised concerns about inappropriate use. This study investigated factors driving the changing use of CT among people admitted to tertiary hospitals in Western Australia (WA). DESIGN AND SETTING: A repeated cross-sectional study of CT use in WA in 2003–2005 and 2013–2015 using linked administrative heath data at the individual patient level. PARTICIPANTS: A total of 2 375 787 tertiary hospital admissions of people aged 18 years or older. MAIN OUTCOME MEASURE: Rate of CT scanning per 1000 hospital admissions. METHODS: A multivariable decomposition model was used to quantify the contribution of changes in patient characteristics and changes in the probability of having a CT over the study period. RESULTS: The rate of CT scanning increased by 112 CT scans per 1000 admissions over the study period. Changes in the distribution of the observed patient characteristics were accounted for 62.7% of the growth in CT use. However, among unplanned admissions, changes in the distribution of patient characteristics only explained 17% of the growth in CT use, the remainder being explained by changes in the probability of having a CT scan. While the relative probability of having a CT scan generally increased over time across most observed characteristics, it reduced in young adults (−2.8%), people living in the rural/remote areas (−0.8%) and people transferred from secondary hospitals (−0.8%). CONCLUSIONS: Our study highlights potential improvements in practice towards reducing medical radiation exposure in certain high risk population. Since changes in the relative probability of having a CT scan (representing changes in scope) rather than changes in the distribution of the patient characteristics (representing changes in need) explained a major proportion of the growth in CT use, this warrants more in-depth investigations in clinical practices to better inform health policies promoting appropriate use of diagnostic imaging tests. BMJ Publishing Group 2021-11-11 /pmc/articles/PMC8587703/ /pubmed/34764174 http://dx.doi.org/10.1136/bmjopen-2021-052954 Text en © Author(s) (or their employer(s)) 2021. 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 Health Services Research
Ha, Ninh Thi
Maxwell, Susannah
Bulsara, Max K
Doust, Jenny
Mcrobbie, Donald
O’Leary, Peter
Slavotinek, John
Moorin, Rachael
Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia
title Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia
title_full Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia
title_fullStr Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia
title_full_unstemmed Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia
title_short Factors driving CT utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in Western Australia
title_sort factors driving ct utilisation in tertiary hospitals: a decomposition analysis using linked administrative data in western australia
topic Health Services Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587703/
https://www.ncbi.nlm.nih.gov/pubmed/34764174
http://dx.doi.org/10.1136/bmjopen-2021-052954
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