Data-Driven MRI Workflow Optimization and Platform Modernization: An Imaging Informatics and Energy Sustainability...

Magnetic resonance imaging (MRI) is an energy-intensive modality, with substantial electricity consumption contributing to healthcare-related greenhouse gas emissions and operational costs. A significant proportion of MRI energy usage occurs during non-imaging periods, including idle and standby states. While recent technological advancements and industry guidelines, such as those from the European Coordination Committee of the Radiological, Electromedical and Healthcare IT Industry (COCIR), emphasize improved energy efficiency, most available data originate from high-resource settings. Evidence from resource-limited environments remains scarce, where financial constraints and increasing imaging demands necessitate cost-effective and sustainable solutions. Leveraging imaging informatics-including Radiology Information System (RIS) utilization analytics-to guide scanner workload distribution and operational mode scheduling offers an unexplored pathway to lower energy demands and operational costs in resource-constrained environments. This study aims to quantify differences in instantaneous power draw and cumulative energy consumption between a legacy and a modernized 3 T MRI platform, and to evaluate an informatics-driven workflow restructuring model utilizing low-power states to optimize departmental energy efficiency and operational expenditure. This retrospective observational study was conducted at a tertiary care hospital in North India, comparing two 3 Tesla MRI systems from the same manufacturer. Unit 1 (installed 2013) represented a legacy 3 T platform, while Unit 2 (upgraded 2024) underwent a comprehensive vendor platform modernization (upgraded gradient drivers, direct-digital RF architecture, modern cold-head compressor, and integrated power-save software). Sub-metering class-1.0 digital energy analyzers recorded instantaneous power (kW) and energy consumption (kWh) across Active Scan, Ready to Scan, Off, and Low Power modes. RIS scheduling and timestamp audit trails informed a two-phase intervention: baseline parallel monitoring (Phase 1) followed by informatics-guided overnight emergency consolidation onto Unit 2 with scheduled power-down of Unit 1 (Phase 2). Data were collected over two phases. In Phase 1 (September-October 2024), baseline energy consumption of Unit 1 was recorded during routine and emergency operations. In Phase 2 (October-November 2024), emergency workload was shifted to Unit 2, with optimization of low-power mode utilization. Institutional electricity tariffs were utilized to calculate cost metrics. Continuous variables were analyzed using paired and independent samples $t$ -tests to evaluate variances. During Phase 1, the newer-generation MRI system (Unit 2) demonstrated a statistically significant 27% reduction in mean daily energy consumption compared to the older system (Unit 1) ( $P<.001$ ), corresponding to projected annual savings of approximately INR 12,70,000 (USD 14,670). Following workflow optimization in Phase 2, despite a significant increase in mean daily scan volume ( $P=.008$ ), departmental energy consumption was further reduced by an additional 4% ( $P=.024$ ). This resulted in incremental monthly savings of approximately INR 1,85,000 (USD 2120), extrapolated to total annual savings of INR 22,00,000 (USD 25,530). Coupling scanner platform modernization with informatics-guided workflow optimization and automated low-power state scheduling significantly curtails non-productive idle energy demand and reduces departmental expenditure in resource-limited healthcare facilities. These findings underscore the importance of integrating energy-efficient technologies and operational practices in radiology departments to promote sustainability.