Funnel plots, performance variation and the Myocardial Infarction National Audit Project 2003–2004

• Main Authors: Hall Alistair S, Batin Phil D, Roberts Anthony P, Gale Christopher P
• Format: Article
• Language: English
• Published: BMC 2006-08-01
• Series: BMC Cardiovascular Disorders
• Online Access: http://www.biomedcentral.com/1471-2261/6/34

<p>Abstract</p> <p>Background</p> <p>Clinical governance requires health care professionals to improve standards of care and has resulted in comparison of clinical performance data. The Myocardial Infarction National Audit Project (a UK cardiology dataset) tabulates its performance. However funnel plots are the display method of choice for institutional comparison. We aimed to demonstrate that funnel plots may be derived from MINAP data and allow more meaningful interpretation of data.</p> <p>Methods</p> <p>We examined the attainment of National Service Framework standards for all hospitals (n = 230) and all patients (n = 99,133) in the MINAP database between 1^stApril 2003 and 31^stMarch 2004. We generated funnel plots (with control limits at 3 sigma) of Door to Needle and Call to Needle thrombolysis times, and the use of aspirin, beta-blockers and statins post myocardial infarction.</p> <p>Results</p> <p>Only 87,427 patients fulfilled criteria for analysis of the use of secondary prevention drugs and 15,111 patients for analysis by Door to Needle and Call to Needle times (163 hospitals achieved the standards for Door to Needle times and 215 were within or above their control limits). One hundred and sixteen hospitals fell outside the 'within 25%' and 'more than 25%' standards for Call to Needle times, but 28 were below the lower control limits. Sixteen hospitals failed to reach the standards for aspirin usage post AMI and 24 remained below the lower control limits. Thirty hospitals were below the lower CL for beta-blocker usage and 49 outside the standard. Statin use was comparable.</p> <p>Conclusion</p> <p>Funnel plots may be applied to a complex dataset and allow visual comparison of data derived from multiple health-care units. Variation is readily identified permitting units to appraise their practices so that effective quality improvement may take place.</p>