Routine hospital episode statistics can be used to identify patients who are at high risk of suffering future multiple emergency hospital admissions. The potential cost savings in preventing a proportion of these subsequent admissions need to be compared with the costs of case management of these patients.

Objective. To use statistical process control charts to monitor in‐hospital outcomes at the hospital level for a wide range of procedures and diagnoses. Data Sources. Routine English hospital admissions data. Study Design. Retrospective analysis using risk‐adjusted log‐likelihood cumulative sum (CUSUM) charts, comparing each hospital with the national average and its peers for in‐hospital mortality, length of stay, and emergency readmission within 28 days. Data Collection. Data were derived from the Department of Health administrative hospital admissions database, with monthly uploads from the clearing service. Principal Findings. The tool is currently being used by nearly 100 hospitals and also a number of primary care trusts responsible for purchasing hospital care. It monitors around 80 percent of admissions and in‐hospital deaths. Case‐mix adjustment gives values for the area under the receiver operating characteristic curve between 0.60 and 0.86 for mortality, but the values were poorer for readmission. Conclusions. CUSUMs are a promising management tool for managers and clinicians for driving improvement in hospital performance for a range of outcomes, and interactive presentation via a web‐based front end has been well received by users. Our methods act as a focus for intelligently directed clinical audit with the real potential to improve outcomes, but wider availability and prospective monitoring are required to fully assess the method's utility.

Following several recent inquiries in the UK into medical malpractice and failures to deliver appropriate standards of health care, there is pressure to introduce formal monitoring of performance outcomes routinely throughout the National Health Service. Statistical process control (SPC) charts have been widely used to monitor medical outcomes in a variety of contexts and have been specifically advocated for use in clinical governance. However, previous applications of SPC charts in medical monitoring have focused on surveillance of a single process over time. We consider some of the methodological and practical aspects that surround the routine surveillance of health outcomes and, in particular, we focus on two important methodological issues that arise when attempting to extend SPC charts to monitor outcomes at more than one unit simultaneously (where a unit could be, for example, a surgeon, general practitioner or hospital): the need to acknowledge the inevitable between-unit variation in 'acceptable' performance outcomes due to the net effect of many small unmeasured sources of variation (e.g. unmeasured case mix and data errors) and the problem of multiple testing over units as well as time. We address the former by using quasi-likelihood estimates of overdispersion, and the latter by using recently developed methods based on estimation of false discovery rates. We present an application of our approach to annual monitoring 'all-cause' mortality data between 1995 and 2000 from 169 National Health Service hospital trusts in England and Wales. Copyright 2004 Royal Statistical Society.

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Summary. The public inquiry into paediatric cardiac surgery at the Bristol Royal In®rmary commissioned the authors to design and conduct analyses of routine data sources to compare surgical outcomes between centres. Such analyses are necessarily complex in this context but were further hampered by the inherent inconsistencies and mediocre quality of the various sources of data. Three levels of analysis of increasing sophistication were carried out. The reasonable consistency of the results arising from different sources of data, together with a number of sensitivity analyses, led us to conclude that there had been excess mortality in Bristol in open heart operations on children under 1 year of age. We consider criticisms of our analysis and discuss the role of statisticians in this inquiry and their contribution to the ®nal report of the inquiry. The potential statistical role in future programmes for monitoring clinical performance is highlighted.