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Sequential monitoring of hospital adverse events when control charts fail: the example of fall injuries in hospitals
  1. A Barker1,2,
  2. A Morton3,
  3. M Gatton4,
  4. E Tong5,
  5. A Clements5,6
  1. 1
    The Northern Clinical Research Centre, Melbourne, Victoria, Australia
  2. 2
    Division of Physiotherapy, The School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, Queensland, Australia
  3. 3
    Infection Management Services, Princess Alexandra Hospital, Brisbane, Queensland, Australia
  4. 4
    The Queensland Institute of Medical Research, Brisbane, Queensland, Australia
  5. 5
    Centre for Healthcare Related Infection Surveillance and Prevention, Queensland Health, Brisbane, Queensland, Australia
  6. 6
    The School of Population Health, The University of Queensland, Brisbane, Queensland, Australia
  1. Correspondence to Dr Anthony Morton, 40 Garioch Street, Tarragindi, Brisbane, QLD 4121, Australia; amor5444{at}bigpond.net.au

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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Footnotes

  • Funding The Northern Hospital received AUD$50 000 from the Rural and Regional Health Aged Care Services Division, Department of Human Services, Victoria, for the 12-month development and implementation stage of the falls prevention programme in 2002. The ongoing falls prevention programme is funded by The Northern Hospital through their Injury Prevention Projects.

  • Competing interests None declared.

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