Cost and value in medical education: the role of statistical process control

Medical education is associated with significant costs. These costs have led to a growing interest in how to deliver high quality or high quantity education on a limited budget. This in turn has led to an interest in how best to measure quantity, quality and cost and how to track these variables over time. The ultimate aim of the interest in cost and value in medical education is to improve quality or quantity over time and to reduce costs. This may occur by a number of methods – which might include e-learning or simple simulations. In order for improvements in cost and value to occur, there needs to be a usable and easily understood methodology to demonstrate improvement. There are good reasons to believe that statistical process control is one such methodology. Statistical process control is a discipline within statistics that enables analysis of data over time and graphical representation of that data. Statistical process control is a methodology that can be used with minimal statistical training and that can be understood by a range of different types of health and education professionals. Measurements of data can change over time and can change for a variety of reasons. These changes might be due to inherent variation or to defects in the technique of measurement or to deliberate attempts to improve the data or accidental disimprovements in the data. For example, changes in student examination marks from year to year might be due to more intelligent students (inherent variation) or problems with the exam (defects in the technique of measurement) or an investment in curriculum delivery that resulted in better educated students (a deliberate attempt to improve the data). There are a variety of means to demonstrate change – the advantage of statistical process control is that it enables the detection of change in real time – while there is still time to do something about it – and it can be easily understood by non-experts. At the same time, statistical process control is a rigorous methodology – it is not a means of getting a "rough" view of data. In statistical process control, changes over time can be graphically represented by a statistical control chart or a control chart. According to statistical process control nomenclature, natural changes in data over time are known as common cause variation. Depending on the nature of the data, natural changes over time may follow a normal distribution or perhaps another form of distribution (for example a binomial distribution). If, however, data changes as result of something going wrong or as a result of an intervention to cause improvement, this is known as special cause variation. The control chart enables the measurement of data over time and also enables all stakeholders to see whether the data are within the upper control limit and/or the lower control limit. If the data are within these limits, then it is assumed that changes in data are a result of common cause variation. If, however, the measurements fall above or below the upper or the lower control limit, then it is assumed that changes in data are a result of special cause variation. Most experts recommend that the upper and lower control limits should be placed at 3 standard