Advances in cancer treatment have improved patient outcomes and survival in recent decades. Increased complexity, duration, and individualisation of treatment protocols present an important challenge for care teams monitoring adherence to best-practice care. A rigid rules-based system for flagging outliers is not fit for purpose, as there are sound reasons for deviating from baseline protocols, such as the management of treatment side effects to a tolerable degree, however the methods for determining the bounds of appropriateness for variation are not well studied or understood. The development of digital representations to inform cancer care delivery in a timely and continuing manner is crucial. This scoping review seeks to identify gaps in current methods and propose a novel approach to digitally represent patient journeys in clinically meaningful visual and computational forms. These methods can be combined to produce real-time, clinically applicable tools such as group-level business-intelligence dashboards (are processes and resources adequate to ensure that patients are being treated according to best practice?) as well as individual-level decision support (what is the likely outcome for this patient if treatment is stopped early based on prior data?) and day to day clinical workflows (what has happened to this patient so far?)