Artificial Intelligence Governance of Radiotherapy Machine Scheduling Models Under Unmeasured Oncology Department Consultant Block-Booking Privilege: A Healthcare Political Fairness Evaluation

Radiotherapy scheduling algorithms allocate linear accelerator time under constraints that are formally clinical but substantively political, because consultant block-booking privileges convert departmental hierarchy into control over machine access. Governance frameworks for these models typically audit the algorithm against recorded scheduling rules while leaving the privilege structure that generates those rules unmeasured. Using a before-after-control-impact design with a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), the analysis estimated power across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Power reached conventional levels only at 54 clusters for a 5-percentage-point effect, a sample size that exceeds the departmental scale at which radiotherapy scheduling operates. The framework distinguishes three governance failures: privilege invisibility, aggregation bias and accountability displacement. It proposes that AI governance for clinical scheduling must incorporate institutional-privilege auditing as a precondition for algorithmic fairness claims, and it specifies the design conditions under which such auditing can produce defensible inferences.