Citation

BibTex format

@article{Cecil:2018:10.1136/bmjqs-2017-007495,
author = {Cecil, E and Bottle, A and Esmail, A and Wilkinson, S and Vincent, C and Aylin, PP},
doi = {10.1136/bmjqs-2017-007495},
journal = {BMJ Quality and Safety},
pages = {965--973},
title = {Investigating the association of alerts from a national mortality surveillance system with subsequent hospital mortality in England: an interrupted time series analysis},
url = {http://dx.doi.org/10.1136/bmjqs-2017-007495},
volume = {27},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - OBJECTIVE: To investigate the association between alerts from a national hospital mortality surveillance system and subsequent trends in relative risk of mortality. BACKGROUND: There is increasing interest in performance monitoring in the NHS. Since 2007, Imperial College London has generated monthly mortality alerts, based on statistical process control charts and using routinely collected hospital administrative data, for all English acute NHS hospital trusts. The impact of this system has not yet been studied. METHODS: We investigated alerts sent to Acute National Health Service hospital trusts in England in 2011-2013. We examined risk-adjusted mortality (relative risk) for all monitored diagnosis and procedure groups at a hospital trust level for 12 months prior to an alert and 23 months post alert. We used an interrupted time series design with a 9-month lag to estimate a trend prior to a mortality alert and the change in trend after, using generalised estimating equations. RESULTS: On average there was a 5% monthly increase in relative risk of mortality during the 12 months prior to an alert (95% CI 4% to 5%). Mortality risk fell, on average by 61% (95% CI 56% to 65%), during the 9-month period immediately following an alert, then levelled to a slow decline, reaching on average the level of expected mortality within 18 months of the alert. CONCLUSIONS: Our results suggest an association between an alert notification and a reduction in the risk of mortality, although with less lag time than expected. It is difficult to determine any causal association. A proportion of alerts may be triggered by random variation alone and subsequent falls could simply reflect regression to the mean. Findings could also indicate that some hospitals are monitoring their own mortality statistics or other performance information, taking action prior to alert notification.
AU  - Cecil,E
AU  - Bottle,A
AU  - Esmail,A
AU  - Wilkinson,S
AU  - Vincent,C
AU  - Aylin,PP
DO  - 10.1136/bmjqs-2017-007495
EP  - 973
PY  - 2018///
SN  - 2044-5415
SP  - 965
TI  - Investigating the association of alerts from a national mortality surveillance system with subsequent hospital mortality in England: an interrupted time series analysis
T2  - BMJ Quality and Safety
UR  - http://dx.doi.org/10.1136/bmjqs-2017-007495
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29728447
UR  - https://qualitysafety.bmj.com/content/27/12/965
UR  - http://hdl.handle.net/10044/1/60267
VL  - 27
ER  -
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by Paul Aylin and Alex Bottle

Statistical Methods for Healthcare Performance Monitoring
A book for those measuring, monitoring, comparing and improving healthcare performance