Literature DB >> 23063751

Use of graphical statistical process control tools to monitor and improve outcomes in cardiac surgery.

Ian R Smith1, Bruce Garlick2, Michael A Gardner2, Russell D Brighouse2, Kelley A Foster3, John T Rivers4.   

Abstract

BACKGROUND: Graphical Statistical Process Control (SPC) tools have been shown to promptly identify significant variations in clinical outcomes in a range of health care settings. We explored the application of these techniques to qualitatively inform the routine cardiac surgical morbidity and mortality (M&M) review process at a single site.
METHODS: Baseline clinical and procedural data relating to 4774 consecutive cardiac surgical procedures, performed between the 1st January 2003 and the 30th April 2011, were retrospectively evaluated. A range of appropriate performance measures and benchmarks were developed and evaluated using a combination of CUmulative SUM (CUSUM) charts, Exponentially Weighted Moving Average (EWMA) charts and Funnel Plots. Charts have been discussed at the unit's routine M&M meetings. Risk adjustment (RA) based on EuroSCORE has been incorporated into the charts to improve performance.
RESULTS: Discrete and aggregated measures, including Blood Product/Reoperation, major acute post-procedural complications and Length of Stay/Readmission<28 days have proved to be usable measures for monitoring outcomes. Monitoring trends in minor morbidities provides a valuable warning of impending changes in significant events. Instances of variation in performance have been examined and could be related to differences in individual operator performance via individual operator curves.
CONCLUSION: SPC tools facilitate near "real-time" performance monitoring allowing early detection and intervention in altered performance. Careful interpretation of charts for group and individual operators has proven helpful in detecting and differentiating systemic vs. individual variation.
Copyright © 2012 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23063751     DOI: 10.1016/j.hlc.2012.08.060

Source DB:  PubMed          Journal:  Heart Lung Circ        ISSN: 1443-9506            Impact factor:   2.975


  1 in total

1.  Statistical process monitoring to improve quality assurance of inpatient care.

Authors:  Lena Hubig; Nicholas Lack; Ulrich Mansmann
Journal:  BMC Health Serv Res       Date:  2020-01-07       Impact factor: 2.655

  1 in total

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