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Published on: September 22, 2023
Use of graphical statistical process control tools to monitor and improve outcomes in cardiac surgery
Ian R Smith1, Bruce Garlick2, Michael A Gardner2
1St Andrew's Medical Institute, 457 Wickham Terrace, Spring Hill, Queensland 4001, Australia; St Andrew's War Memorial Hospital, 457 Wickham Terrace, Spring Hill, Queensland 4001, Australia.
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.
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