Literature DB >> 21209145

Exponentially weighted moving average charts to compare observed and expected values for monitoring risk-adjusted hospital indicators.

D A Cook1, M Coory, R A Webster.   

Abstract

OBJECTIVE To introduce a new type of risk-adjusted (RA) exponentially weighted moving average (EWMA) chart and to compare it to a commonly used type of variable life adjusted display chart for analysis of patient outcomes. DATA Routine inpatient data on mortality following admission for acute myocardial infarction, from all public and private hospitals in Queensland, Australia. METHODS The RA-EWMA plots the EWMA of the observed and predicted values. Predicted values were obtained from a logistic regression model for all hospitals in Queensland. The EWMA of the predicted values is a moving centre line, reflecting current patient case mix at a particular hospital. Thresholds around this moving centre line provide a scale by which to assess the importance of trends in the EWMA of the observed values. RESULTS The RA-EWMA chart can be designed to have equivalent performance, in terms of average run lengths, as variable life adjusted display chart. The advantages of the RA-EWMA are that it communicates information about the current level of an indicator in a direct and understandable way, and it explicitly displays information about the current patient case mix. Also, because it is not reset, the RA-EWMA is a more natural chart to use in health, where it is exceedingly rare to stop or dramatically and abruptly alter a process of care. CONCLUSION The RA-EWMA chart is a direct and intuitive way to display information about an indicator while accounting for differences in case mix.

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Mesh:

Year:  2011        PMID: 21209145     DOI: 10.1136/bmjqs.2008.031831

Source DB:  PubMed          Journal:  BMJ Qual Saf        ISSN: 2044-5415            Impact factor:   7.035


  5 in total

Review 1.  Comparison of control charts for monitoring clinical performance using binary data.

Authors:  Jenny Neuburger; Kate Walker; Chris Sherlaw-Johnson; Jan van der Meulen; David A Cromwell
Journal:  BMJ Qual Saf       Date:  2017-09-25       Impact factor: 7.035

2.  Variation in and risk factors for paediatric inpatient all-cause mortality in a low income setting: data from an emerging clinical information network.

Authors:  David Gathara; Lucas Malla; Philip Ayieko; Stella Karuri; Rachel Nyamai; Grace Irimu; Michael Boele van Hensbroek; Elizabeth Allen; Mike English
Journal:  BMC Pediatr       Date:  2017-04-05       Impact factor: 2.125

3.  A 10-Year Longitudinal Analysis of Protocol-Based Sepsis Management in a Philippine Tertiary ICU.

Authors:  Niña M Bumanglag; Mari Des J San Juan; Jose Emmanuel M Palo
Journal:  Crit Care Explor       Date:  2019-11-14

4.  Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process.

Authors:  John L Moran; Patricia J Solomon
Journal:  BMC Med Res Methodol       Date:  2013-05-24       Impact factor: 4.615

5.  Comparison of Critical Care Occupancy and Outcomes of Critically Ill Patients during the 2020 COVID-19 Winter Surge and 2009 H1N1 Influenza Pandemic in Australia.

Authors:  Ary Serpa Neto; Aidan J C Burrell; Michael Bailey; Tessa Broadley; D Jamie Cooper; Craig J French; David Pilcher; Mark P Plummer; Tony Trapani; Steve A Webb; Rinaldo Bellomo; Andrew Udy
Journal:  Ann Am Thorac Soc       Date:  2021-08
  5 in total

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