Arbaz Kapadi is a Research Associate in the Division of Psychology & Mental Health at The University of Manchester, working on implementation science research funded by the National Institute for Health Research (NIHR). His current research study seeks to explore the feasibility of implementing quality improvement informed rapid-learning approaches to inform radiotherapy development. Prior to this, Arbaz recently completed his PhD which explored the landscape of healthcare quality improvement with particular focus on organisational behaviour and culture, and the role of service users within these spaces.
Pragmatic continuous learning approaches (‘rapid-learning’) using real-world data (RWD) have the potential to provide evidence to optimise interventions in radiotherapy [1,2]. RWD is the data routinely collected as standard of care about all patients. An NIHR-funded method-development study, RAPID-RT, is currently evaluating the clinical effectiveness of a rapid-learning approach within lung cancer and the feasibility of implementing rapid-learning in practice . We report on radiotherapy professionals’ perceptions of rapid-learning and RWD, and identifying key factors that affect implementation in the clinic.
Interviews were conducted with radiotherapy professionals (n=23) based across five geographically diverse UK cancer sites. Interview participants included clinical oncologists, physicists, radiographers, treatment planning and digital services staff. Data collection took place between January and May 2023, analysing data using inductive thematic analysis .
Participants’ opinions centred on four themes: 1) The alignment of rapid-learning methodologies with the reality of practice, 2) Concerns related to the variability of clinical and RWD, 3) The maturity of data and digital infrastructures for rapid-learning, 4) Further support, education and evidence needed to convince adoption of rapid-learning approaches.
Rapid-learning approaches using RWD offer alternatives to traditional randomised controlled trials for the evaluation of changes in radiotherapy practice. They may also provide better external validity. However, rapid-learning is dependent upon the quality of supporting data. The development of data and digital infrastructures are necessary to improve data accessibility and quality, along with support mechanisms for implementation (e.g. analytical support, time, resource investment). This will strengthen the evidence needed to support rapid-learning approaches.