'Rapid learning': Using real world data to improve clinical practice

Abstract

Abstract text Real world data – the information routinely collected about patients over their care pathway – offers an opportunity to provide evidence where Randomized Controlled Trials (RCTs) are not practical. There is an unmet need for such approaches in radiotherapy where many changes to practice are not suited to RCTs meaning there is often only limited assessment of their impact on clinical outcomes. The quantity and quality of data collected in modern radiotherapy mean it is ideally suited to such analyses. Furthermore, if real world data can be used to evaluate the effect of changes to radiotherapy practice, it opens the door to the use of iterative quality improvement techniques to optimize treatments. In this approach, often called rapid learning, a change to practice is made, its effect evaluated, and this information used to refine the next change before testing its effect again. It has the potential to transform the way in which new technologies and protocols are introduced into the radiotherapy clinic. It is not yet, however, in widespread use. This lecture will explore the promise of rapid learning and consider some of the challenges to its routine implementation. It will discuss the advantages and disadvantages of working with real world data in different ways, comparing the use of selective ‘simple trials’ and Trials within Cohorts (TwiCs) to before-after and timeseries analyses. As well as examining the trade-offs in the evidence produced by different methodologies we will discuss their practicalities, including consideration of different patient consent models. Finally we will use a case study of heart sparing in lung radiotherapy to discuss the steps that need to be taken to move rapid learning into the clinic.