Rapid learning systems

Rapid learning systems represent an opportunity for advancing evidence-based medical care through the development of integrated health systems that aggregate and apply evidence from real-time clinical data. Despite the continual growth of the published medical literature, patients and providers alike are often unsure of best practices surrounding therapies and supportive care for a variety of medical conditions including cancer. Health-care spending in oncology patients is growing in the USA, and the value of purchased oncology care is not well defined. The ultimate application of new knowledge from the medical literature to an individual patient with consideration of efficacy and cost is thus at best unclear. Rapid learning systems offer the potential to leverage real-time, practice-based clinical data in conjunction with research, administrative, and patient-reported data to drive the process of scientific discovery and address these knowledge gaps. Efforts to develop such systems are supported by the evolution of more sophisticated health information technologies, comparative effectiveness research, patient-reported outcome research, and an increasing focus on quality improvement. Rapid learning systems provide the opportunity for continuous bidirectional learning and have the potential to positively reshape individual care delivery. In this chapter, we will discuss the components of rapid learning systems and explore some working examples of rapid learning systems.