Longitudinal impact of cloud-based medical imaging: an analysis using ARIMA modeling and Monte Carlo simulation.

BACKGROUND: Cloud-based medical imaging systems represent a paradigm shift in healthcare digitization. However, existing evaluations often rely on linear models that overlook critical methodological issues, particularly time-series autocorrelation in longitudinal data and uncertainty quantification in economic assessments. OBJECTIVE: To quantify the geographic, economic, and film-related environmental impacts of cloud medical imaging adoption using ARIMA modeling and Monte Carlo simulation. METHODS: We analyzed 15,709 cleaned cloud access records from November 2021 to March 2025 and 464 month-by-format film utilization records covering 114 calendar months from January 2015 to June 2024 at a major tertiary hospital. Time-series autocorrelation was evaluated using ARIMA modeling. Geographic inequality was assessed using the Gini coefficient. Economic robustness was evaluated using a six-scenario deterministic sensitivity analysis and Monte Carlo simulation (10,000 iterations). RESULTS: The system exhibited marked geographic concentration (Gini coefficient = 0.980, 95% CI: 0.977-0.982), with 85.6% of access originating from local and provincial users. ARIMA(1,1,2) modeling of monthly film savings suggested an upward trend in film savings (monthly increase: 281.5 sheets). Forecasts suggested a plateauing trend rather than indefinite growth. Monte Carlo simulation yielded a mean estimated savings of ¥15.75 million (95% CI: [¥13.16, ¥18.35] million). Film-related avoided emissions were estimated at 341 tons of CO2e. CONCLUSION: This single-center study suggests that cloud imaging can be associated with substantial film-related cost savings and avoided emissions in a large urban tertiary hospital. Broader generalizability, net system-wide economic benefit, and full environmental impact require multicenter validation and more comprehensive life-cycle assessment.