2019
DOI: 10.1136/bmjopen-2019-029261
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An interrupted time-series analysis of the impact of emergency department reconfiguration on regional emergency department trolley numbers in Ireland from 2005 to 2015

Abstract: ObjectivesTo understand the impact of emergency department (ED) reconfiguration on the number of patients waiting for hospital beds on trolleys in the remaining EDs in four geographical regions in Ireland using time-series analysis.SettingEDs in four Irish regions; the West, North-East, South and Mid-West from 2005 to 2015.ParticipantsAll patients counted as waiting on trolleys in an ED for a hospital bed in the study hospitals from 2005 to 2015.InterventionThe system intervention was the reconfiguration of ED… Show more

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Cited by 4 publications
(4 citation statements)
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References 29 publications
(48 reference statements)
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“…34 35 Due to the lack of hospital bed space and staffing, many patients received inpatient care on trolleys in corridors and common areas. 37 The Irish Nurses and Midwife Organisation reported a 46% increase in patients being treated on trolleys in 2015 (n=92 998) compared with 2009. 38 The ageing population and older case-mix captured in this study may also have influenced our results and may have offset the positive effect of the national programmes and guidelines.…”
Section: Discussionmentioning
confidence: 99%
“…34 35 Due to the lack of hospital bed space and staffing, many patients received inpatient care on trolleys in corridors and common areas. 37 The Irish Nurses and Midwife Organisation reported a 46% increase in patients being treated on trolleys in 2015 (n=92 998) compared with 2009. 38 The ageing population and older case-mix captured in this study may also have influenced our results and may have offset the positive effect of the national programmes and guidelines.…”
Section: Discussionmentioning
confidence: 99%
“…8 To model the impact of the pandemic, both at the time of the initial declaration of the public health emergency and during the subsequent months of the pandemic, a level and slope change impact model was constructed. [9][10][11] This included both a term denoting the instantaneous change in outcome after the public health emergency (stay-at-home orders) and a term denoting the change in slope after declaration of the public health emergency (progression since stay-at-home orders). Further terms of adjusting were included for the year (2017-2018, 2018-2019, 2019-2020), and a linear term represented the seasonal change from winter (December 1) into summer (August 31) (seasonal trend).…”
Section: Discussionmentioning
confidence: 99%
“…Interrupted time-series is an established flexible method of estimating the change in intercept and slope of an outcome after a discrete event such as an intervention or change in policy 8 . To model the impact of the pandemic, both at the time of the initial declaration of the public health emergency and during the subsequent months of the pandemic, a level and slope change impact model was constructed 9–11 . This included both a term denoting the instantaneous change in outcome after the public health emergency ( stay-at-home orders ) and a term denoting the change in slope after declaration of the public health emergency ( progression since stay-at-home orders ).…”
Section: Methodsmentioning
confidence: 99%
“…The SIREN analysis of demand and capacity changes in regions which had undergone reconfiguration, found a substantial rise in emergency admissions over the period 2005 to 2015 without a commensurate increase in hospital beds. 4 In three of the studied regions, the immediate impact of reconfiguration on ED overcrowding was either non-significant or associated with a short-term 'shock' to observed trolley numbers followed by convergence with the pre-reconfiguration trend over the following 12 months. In one of the regions, a longer-term increase in the overcrowding trend was observed.…”
Section: What Was the Impact Of Emergency Care Reconfiguration In Irementioning
confidence: 90%