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

BACKGROUND: 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. METHODS: 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(st )April 2003 and 31(st )March 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. RESULTS: 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. CONCLUSION: 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

Experience with an online prospective database on adolescent idiopathic scoliosis: development and implementation

Considerable variability exists in the surgical treatment and outcomes of adolescent idiopathic scoliosis (AIS). This is due to the lack of evidence-based treatment guidelines and outcome measures. Although clinical trials have been extolled as the highest form of evidence for evaluating treatment efficacy, the disadvantage of cost, time, lack of feasibility, and ethical considerations indicate a need for a new paradigm for evidence based research in this spinal deformity. High quality clinical databases offer an alternative approach for evidence-based research in medicine. So, we developed and established Scolisoft, an international, multidimensional and relational database designed to be a repository of surgical cases for AIS, and an active vehicle for standardized surgical information in a format that would permit qualitative and quantitative research and analysis. Here, we describe and discuss the utility of Scolisoft as a new paradigm for evidence-based research on AIS. Scolisoft was developed using dot.net platform and SQL server from Microsoft. All data is deidentified to protect patient privacy. Scolisoft can be accessed at www.scolisoft.org. Collection of high quality data on surgical cases of AIS is a priority and processes continue to improve the database quality. The database currently has 67 registered users from 21 countries. To date, Scolisoft has 200 detailed surgical cases with pre, post, and follow up data. Scolisoft provides a structured process and practical information for surgeons to benchmark their treatment methods against other like treatments. Scolisoft is multifaceted and its use extends to education of health care providers in training, patients, ability to mine important data to stimulate research and quality improvement initiatives of healthcare organizations

Use of Binary Cumulative Sums and Moving Averages in Nosocomial Infection Cluster Detection1

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections