In this module, you will learn about the importance of measuring quality of care and health outcomes in order to determine whether QI initiatives have achieved their aims. You will learn about how data is utilised to identify areas of improvement and the importance of using both quantitative and qualitative data in evaluating change. You will learn about the specific methods appropriate for improvement as distinct from methods more suited to research, including how to design measurement schemes suitable for improvement initiatives.

In this module you will learn how to use the methods of statistical process control to distinguish between variation due to changes in the underlying process of care and variation that is part of this process, including how to distinguish when variation in a measure constitutes evidence of improvement in care. You will use the statistical software package R to analyse real time series data using Shewhart charts, and learn to interpret and act on the results. Example data is taken from a project screening for Atrial Fibrillation in an at risk population, and data on time spent in the accident and emergency department

In this module you will build on what you learnt previously, expanding the repertoire of methods to cover additional types of data frequently encountered in quality improvement.

In this module, you will learn about the use of qualitative research methods in QI in healthcare. This includes how qualitative methods can be used to drive improvement, and in studying and evaluating improvement. You will learn about the Matching Michigan study, including methods and key findings, as an example of the importance of theory in quality improvement. You will compare three theoretical frameworks for implementation and improvement, identifying their strengths and weaknesses, and use a tool designed to aid framework selection.