Literature DB >> 19633547

Will benchmarking ICUs improve outcome?

Douglas Woodhouse1, Marc Berg, Joris van der Putten, Judith Houtepen.   

Abstract

PURPOSE OF REVIEW: In this article we discuss our experiences benchmarking eight ICUs in The Netherlands. Benchmarks must be carefully designed and implemented to generate meaningful results. We define prerequisites that we have identified for successful benchmarking and discuss the development, implementation and results of ICU benchmarks that we have completed. RECENT
FINDINGS: Previous articles have discussed benchmarking ICUs, but there are still few studies of significant size and appropriate design that measure the impact of benchmarking on outcomes. Perhaps the most well known, and still best example of a benchmarking study designed to measure outcome improvements is the work of Pronovost et al. in Michigan ICUs.
SUMMARY: Benchmarking is an increasingly common activity, however it is difficult to prove that benchmarks result in improved outcomes. Concurrent with our benchmarking activities the Standardized Mortality Ratio in Dutch ICUs has decreased. We have been able to show that larger ICUs in our benchmarks generally had improved outcomes despite a higher average patient severity. Quality assurance in healthcare is maturing and benchmarks will become an increasingly useful way of comparing performance between institutions.

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Year:  2009        PMID: 19633547     DOI: 10.1097/MCC.0b013e32833079fb

Source DB:  PubMed          Journal:  Curr Opin Crit Care        ISSN: 1070-5295            Impact factor:   3.687


  9 in total

1.  Assessing the utility of ICU readmissions as a quality metric: an analysis of changes mediated by residency work-hour reforms.

Authors:  Sydney E S Brown; Sarah J Ratcliffe; Scott D Halpern
Journal:  Chest       Date:  2015-03       Impact factor: 9.410

2.  The epidemiology of intensive care unit readmissions in the United States.

Authors:  Sydney E S Brown; Sarah J Ratcliffe; Jeremy M Kahn; Scott D Halpern
Journal:  Am J Respir Crit Care Med       Date:  2012-01-26       Impact factor: 21.405

3.  Understanding intensive care unit benchmarking.

Authors:  Jorge I F Salluh; Marcio Soares; Mark T Keegan
Journal:  Intensive Care Med       Date:  2017-03-15       Impact factor: 17.440

Review 4.  Benchmarking: a method for continuous quality improvement in health.

Authors:  Amina Ettorchi-Tardy; Marie Levif; Philippe Michel
Journal:  Healthc Policy       Date:  2012-05

5.  An empirical derivation of the optimal time interval for defining ICU readmissions.

Authors:  Sydney E S Brown; Sarah J Ratcliffe; Scott D Halpern
Journal:  Med Care       Date:  2013-08       Impact factor: 2.983

6.  Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

Authors:  Curtis E Kennedy; James P Turley
Journal:  Theor Biol Med Model       Date:  2011-10-24       Impact factor: 2.432

7.  Better prognostic marker in ICU - APACHE II, SOFA or SAP II!

Authors:  Iftikhar Haider Naqvi; Khalid Mahmood; Syed Ziaullaha; Syed Mohammad Kashif; Asim Sharif
Journal:  Pak J Med Sci       Date:  2016 Sep-Oct       Impact factor: 1.088

8.  Severe imported malaria in an intensive care unit: A case series.

Authors:  Fatma Al Farsi; Juhi Chandwani; Asmaa S Mahdi; Eskild Petersen
Journal:  IDCases       Date:  2019-04-30

9.  Predicting six-month mortality of patients with traumatic brain injury: usefulness of common intensive care severity scores.

Authors:  Rahul Raj; Markus Skrifvars; Stepani Bendel; Tuomas Selander; Riku Kivisaari; Jari Siironen; Matti Reinikainen
Journal:  Crit Care       Date:  2014-04-03       Impact factor: 9.097

  9 in total

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