Bayesian Analysis of AI-Driven Cost Savings in UK and Australian Health Care Systems: Cross-Sector Implementation Study - Jayanta Sarkar; Christopher Drovandi; Sandeep Reddy (JMIR Medical Informatics, 2026)
Background Health care systems face growing fiscal pressure while AI reaches clinical parity in several domains. UK National Health Service expenditure rose by 52%, while Australia's health expenditure grew by 29% between 2019 and 2023. Yet large-scale cost savings from AI remain limited, largely because implementation constraints continue to outweigh technical capability. Objective This study develops a Bayesian budget-impact framework to estimate AI-driven gross cost savings in radiology, workflow optimization, and workforce optimization in the United Kingdom and Australia, explicitly accounting for adoption uncertainty, effectiveness, and implementation risk. Methods We used a sequential Monte Carlo simulation with 1000 particles to estimate annual gross cost savings. The core savings function combined expenditure base, sector weight, adoption, effectiveness, and implementation risk. Priors were informed by a structured review of multiple studies per domain. The savings likelihood used a heteroscedastic noise specification in which the SD followed an exponential prior, with the mean set to 15% of each sector’s observed savings estimate, ranging from US $12.0 million for Australian radiology to US $87.2 million for UK workforce optimization. The likelihood was also augmented with sector-specific observations that anchored effectiveness to published cost-reduction estimates and implementation risk to observed deployment failure rates. Scenario analysis applied multipliers to 1000 bootstrap posterior draws across optimistic, conservative, and pessimistic settings. Sensitivity analyses varied the σ scaling factor from 0.10 to 0.20 and perturbed prior means for adoption, effectiveness, and implementation risk by ±20%. Results Posterior annual savings were US $949 million (95% credible interval [CrI] US $720.6-US $1173.5 million) for the United Kingdom and US $737 million (95% CrI US $526.1-US $953.6 million) for Australia. Workforce optimization generated the largest share of savings in both countries, contributing 62.4% of UK savings (US $591.9 million, 95% CrI US $398.8-US $854.8 million) and 76.2% of Australian savings (US $561.4 million, 95% CrI US $331.9-US $815.3 million). Posterior implementation risk estimates ranged from 35.5% to 47.4%, below prior means of 49.1% to 57.2%, reflecting the empirical anchoring introduced through deployment-failure data. Across scenarios, projected savings ranged from US $357 million to US $1.845 billion in the UK and from US $267.2 million to US $1.454 billion in Australia. Baseline cumulative projections for 2024-2030 were US $10.1 billion for the United Kingdom and US $8.0 billion for Australia. The sensitivity analyses confirmed the robustness of the posterior savings estimates. Conclusions AI could generate substantial expenditure reductions in both health systems, but implementation risk remains the main constraint on realizing those gains. Workforce savings should be interpreted primarily as capacity gains that can be redeployed to higher-value care, not as automatic cash savings. The Bayesian framework offers probabilistic planning ranges rather than point forecasts and provides a practical basis for policy planning under uncertainty.