An integrated discrete event simulation and particle swarm optimisation model for optimising efficiency of cancer diagnosis pathways

The National Health Service (NHS) constitution sets out minimum standards for rights of access patients to NHS services. ‘Faster Diagnosis Standard’ (FDS) states that 75% should be told whether they have a diagnosis cancer or not within 28 days an urgent GP referral. Timely and treatment lead improved outcomes patients, however, compliance with these has recently been challenged, particularly in the context operational pressures resource constraints relating COVID-19 pandemic. In order minimise diagnostic delays, Physical Laboratory collaboration Royal Free London (RFL) Foundation Trust address this problem by treating it as formal optimisation, aiming number who breach FDS. We use discrete event simulation particle swarm optimisation identify areas improving efficiency at RFL. highlight capacity-demand mismatches current pathways RFL, including imaging endoscopy investigations. This is due volume requiring investigations meet 28-day FDS target. find increasing resources one area alone does fully solve problem. By looking system whole we improvement which will system-wide impact even though individually do necessarily seem significant. project potential make valuable on shaping future hospital activity. • Visualisation complex pathway data trust. Optimised solutions pathways. Discrete interdependent Identification timely diagnoses. Multiple candidate users select e.g., planning.