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The carbon footprint of hospital diagnostic imaging in Australia
Scott McAlister1, Forbes McGain2, Matilde Petersen3
1The Centre for Health Policy, The University of Melbourne, Australia, Wiser Healthcare and Faculty of Medicine and Health, The University of Sydney, Australia, and Department of Critical Care, The University of Melbourne, Grattan St, Parkville, VIC 3010, Australia.
Insights
Diagnostic imaging contributes significantly to healthcare
Area of Science:
- Environmental Science
- Healthcare Sustainability
- Medical Imaging Technology
Background:
- Diagnostic imaging and pathology testing account for 9% of healthcare's carbon footprint.
- The carbon footprint of pathology testing is known, but the environmental impact of common imaging modalities was previously unclear.
Purpose of the Study:
- To quantify the carbon footprint of five common diagnostic imaging modalities.
- To compare the greenhouse gas emissions of different imaging techniques using life cycle assessment.
Main Methods:
- A prospective life cycle assessment was conducted at two Australian health services.
- Five imaging modalities were evaluated: chest X-ray (CXR), mobile chest X-ray (MCXR), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US).
- Both attributional and consequential life cycle assessment methods were used, considering scanner electricity, consumables, and waste.
Main Results:
- Mean emissions varied significantly: MRI (17.5 kg CO2e/scan), CT (9.2 kg CO2e/scan), CXR (0.8 kg CO2e/scan), MCXR (0.5 kg CO2e/scan), and US (0.5 kg CO2e/scan).
- Standby energy use contributed substantially to emissions.
- Consequential analysis, excluding standby power, showed lower impacts: MRI (1.1 kg CO2e/scan), CT (1.1 kg CO2e/scan), CXR (0.6 kg CO2e/scan), MCXR (0.1 kg CO2e/scan), and US (0.1 kg CO2e/scan).
Conclusions:
- Clinicians can reduce imaging's carbon footprint by minimizing unnecessary scans and choosing lower-impact modalities (X-ray, US) over higher-impact ones (MRI, CT) when appropriate.
- Reducing scanner standby time by turning off equipment and increasing scanner utilization rates can significantly lower per-scan emissions and improve resource efficiency.
Background:
Pathology testing and diagnostic imaging together contribute 9% of healthcare's carbon footprint. Whilst the carbon footprint of pathology testing has been undertaken, to date, the carbon footprint of the four most common imaging modalities is unclear.
Methods:
We performed a prospective life cycle assessment at two Australian university-affiliated health services of five imaging modalities: chest X-ray (CXR), mobile chest X-ray (MCXR), computerised tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US). We included scanner electricity use and all consumables and associated waste, including bedding, imaging contrast, and gloves. Analysis was performed using both attributional and consequential life cycle assessment methods. The primary outcome was the greenhouse gas footprint, measured in carbon dioxide equivalent (CO2e) emissions.
Findings:
Mean CO2e emissions were 17·5 kg/scan for MRI; 9·2 kg/scan for CT; 0·8 kg/scan for CXR; 0·5 kg/scan for MCXR; and 0·5 kg/scan for US. Emissions from scanners from standby energy were substantial. When expressed as emissions per additional scan (results of consequential analysis) impacts were lower: 1·1 kg/scan for MRI; 1·1 kg/scan for CT; 0·6 kg/scan for CXR; 0·1 kg/scan for MCXR; and 0·1 kg/scan for US, due to emissions from standby power being excluded.
Interpretation:
Clinicians and administrators can reduce carbon emissions from diagnostic imaging, firstly by reducing the ordering of unnecessary imaging, or by ordering low-impact imaging (X-ray and US) in place of high-impact MRI and CT when clinically appropriate to do so. Secondly, whenever possible, scanners should be turned off to reduce emissions from standby power. Thirdly, ensuring high utilisation rates for scanners both reduces the time they spend in standby, and apportions the impacts of the reduced standby power of a greater number of scans. This therefore reduces the impact on any individual scan, maximising resource efficiency.
Funding:
Healthy Urban Environments (HUE) Collaboratory of the Maridulu Budyari Gumal Sydney Partnership for Health, Education, Research and Enterprise MBG SPHERE. The National Health and Medical Research Council (NHMRC) PhD scholarship.
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