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Sequential monitoring of hospital adverse events when control charts fail: the example of fall injuries in hospitals

Sequential monitoring of hospital adverse events when control charts fail: the example of fall injuries in hospitals
Sequential monitoring of hospital adverse events when control charts fail: the example of fall injuries in hospitals
Objective: to evaluate methods for monitoring monthly aggregated hospital adverse event data that display clustering, non-linear trends and possible autocorrelation.

Design: retrospective audit.

Setting: The Northern Hospital, Melbourne, Australia.

Participants: 171,059 patients admitted between January 2001 and December 2006.

Measurements: the analysis is illustrated with 72 months of patient fall injury data using a modified Shewhart U control chart, and charts derived from a quasi-Poisson generalised linear model (GLM) and a generalised additive mixed model (GAMM) that included an approximate upper control limit.

Results: the data were overdispersed and displayed a downward trend and possible autocorrelation. The downward trend was followed by a predictable period after December 2003. The GLM-estimated incidence rate ratio was 0.98 (95% CI 0.98 to 0.99) per month. The GAMM-fitted count fell from 12.67 (95% CI 10.05 to 15.97) in January 2001 to 5.23 (95% CI 3.82 to 7.15) in December 2006 (p<0.001). The corresponding values for the GLM were 11.9 and 3.94. Residual plots suggested that the GLM underestimated the rate at the beginning and end of the series and overestimated it in the middle. The data suggested a more rapid rate fall before 2004 and a steady state thereafter, a pattern reflected in the GAMM chart. The approximate upper two-sigma equivalent control limit in the GLM and GAMM charts identified 2 months that showed possible special-cause variation.

Conclusion: charts based on GAMM analysis are a suitable alternative to Shewhart U control charts with these data
473-477
Barker, A.
78b92811-7954-4614-91b1-70032c58bb07
Morton, A.
5633d9e0-db77-4c03-ae3d-9f879a123e4c
Gatton, M.
fa346e1e-a9e6-4668-90b9-8651d5566a11
Tong, E.
d296c683-25c9-456b-80be-54828f082df9
Clements, A.
67f9bcef-1688-4c5f-8b87-0da07a119184
Barker, A.
78b92811-7954-4614-91b1-70032c58bb07
Morton, A.
5633d9e0-db77-4c03-ae3d-9f879a123e4c
Gatton, M.
fa346e1e-a9e6-4668-90b9-8651d5566a11
Tong, E.
d296c683-25c9-456b-80be-54828f082df9
Clements, A.
67f9bcef-1688-4c5f-8b87-0da07a119184

Barker, A., Morton, A., Gatton, M., Tong, E. and Clements, A. (2009) Sequential monitoring of hospital adverse events when control charts fail: the example of fall injuries in hospitals. BMJ Quality and Safety, 18 (6), 473-477. (doi:10.1136/qshc.2007.025601). (PMID:19955460)

Record type: Article

Abstract

Objective: to evaluate methods for monitoring monthly aggregated hospital adverse event data that display clustering, non-linear trends and possible autocorrelation.

Design: retrospective audit.

Setting: The Northern Hospital, Melbourne, Australia.

Participants: 171,059 patients admitted between January 2001 and December 2006.

Measurements: the analysis is illustrated with 72 months of patient fall injury data using a modified Shewhart U control chart, and charts derived from a quasi-Poisson generalised linear model (GLM) and a generalised additive mixed model (GAMM) that included an approximate upper control limit.

Results: the data were overdispersed and displayed a downward trend and possible autocorrelation. The downward trend was followed by a predictable period after December 2003. The GLM-estimated incidence rate ratio was 0.98 (95% CI 0.98 to 0.99) per month. The GAMM-fitted count fell from 12.67 (95% CI 10.05 to 15.97) in January 2001 to 5.23 (95% CI 3.82 to 7.15) in December 2006 (p<0.001). The corresponding values for the GLM were 11.9 and 3.94. Residual plots suggested that the GLM underestimated the rate at the beginning and end of the series and overestimated it in the middle. The data suggested a more rapid rate fall before 2004 and a steady state thereafter, a pattern reflected in the GAMM chart. The approximate upper two-sigma equivalent control limit in the GLM and GAMM charts identified 2 months that showed possible special-cause variation.

Conclusion: charts based on GAMM analysis are a suitable alternative to Shewhart U control charts with these data

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More information

Published date: 2009
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 199559
URI: https://eprints.soton.ac.uk/id/eprint/199559
PURE UUID: 67298954-2f6d-4a2f-8017-ea59f333b5ee

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Date deposited: 19 Oct 2011 09:24
Last modified: 16 Jul 2019 23:22

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