Mammogram energy use: Metering to map optimization strategies.

e12714 Background: Mammography is a critical component of screening and diagnosis in breastcancer. A key component of cancer prevention also involves mitigation and avoidance of environmental risk factors. Radiology is an energy-intensive field, with opportunities to impact multifactorial patient wellbeing – providing necessary cancer screening and diagnosis while optimizing clinical operations to reduce energy consumption and minimize associated environmental risks. Methods: We conducted prospective metering of two digital breast tomosynthesis (DBT)machines at a large academic medical center. Unit 1 was metered for 16 days, while Unit 2 was metered for 24 days, from April–May 2025. Scanning volume during the study period was 7-10 scans/device/weekday, with no scans on weekends. Power was measured in kilowatts (kW), and energy was calculated as kilowatt-hours (kWh). DBT units operated in four power modes – ready-to-scan, scan, low-power, and off – defined by EnergyStar and COCIR standards. Usingthe time and power associated with each mode, we calculated the contribution of each mode to total DBT energy consumption. Results: While scan mode consumed the most energy and power in both DBT units, the greatest share of energy consumption for both units over the study period came from ready-to-scan mode (68% Unit 1, 45% Unit 2). The biggest discrepancy between time spent across modes for the two units was low-power mode, with Unit 2 spending 66% of time in low-power (24.61 kWh) and Unit 1 spending 18% (1.58 kWh). Extrapolating to annual energy consumption, Units 1 and 2 were estimated to consume 892.88 and 1,641.97 kWh, respectively (Table 1). Conclusions: These findings reveal opportunities for actionable changes at the oncology practice level to reduce non-productive mammography energy consumption. Downstream, this may both reduce the health impacts of energy-associated pollutant emissions and liberate financial resources for improving patient care. Energy optimization strategies could include reducing turnover time between patients, decreasing duration in energy-intensive ready-to-scan mode, andpowering off machines overnight and on weekends. Future quality improvement studies should evaluate the environmental and cost savings of such interventions. DBT Unit Mode Power (mean kW +/- SD) Power (median kW) Duration (minutes (% of total)) Energy Consumption (kWh (% of total)) Annual Projected Energy Consumption (kWh) Total (Unit 1) - - 23,040 (100%) 40.8 (100%) 892.9 Scan 0.57 ± 0.12 0.58 1190 (5%) 11.4 (28%) 250.9 Ready-to-Scan 0.30 ± 0.013 0.3 5369 (23%) 26.9 (68%) 606.5 Low-Power 0.023 ± 0.057 0.0059 4214 (18%) 1.6 (4%) 35.6 Total (Unit 2) - - 34,560 (100%) 91.3 (100%) 1,642.0 Scan 0.57 ± 0.099 0.5 2005 (6%) 18.5 (18%) 300.2 Ready-to-Scan 0.42 ± 0.046 0.4 6951 (20%) 48.2 (45%) 743.2 Low-Power 0.10 ± 0.018 0.1 22724 (66%) 24.6 (36%) 598.6