The NHS contributes more than 5% of the United Kingdom’s (UK) national greenhouse gas emissions (NICE, 2023) and therefore has a vital role to play in the mitigation of climate change (Tennison et al., 2021).
For conventional computerised tomography (CT) scans, contrast media (which usually contain iodine) are required to distinguish different types of soft tissues. Typically, iodinated contrast media is administered through injection to distinguish different types of soft tissues for CT scan imaging, which may result in negative consequences for the environment and for patients (Chandrashekar et al., 2023; Dekker et al., 2022; McCullough & Sandberg, 2003).
AiSentia provides an alternative non-invasive solution for conducting contrast CT scans by utilising artificial intelligence (AI) which can extract information about different types of soft tissues from a routine CT scan, without the need to use contrast media (SBRI Healthcare, 2023).
Unity Insights were commissioned by AiSentia to estimate the potential environmental impacts of an assumption-driven, prospective clinical implementation of the AiSentia software as a medical device (SaMD). We conducted an environmental impact assessment, based on relevant literature and stakeholder discussions, and found that utilising AiSentia instead of contrast media injections could potentially result in a carbon reduction through a reduction in consumables, energy consumption, and reduction in iodinated contrast media.
The consequential life cycle analysis (CLCA) methodology was selected for our environmental impact assessment because only the marginal gains from the change in the number of scans was within the pre-implementation scope of this project (i.e., the number of scanners and the patient throughput was held consistent for the purposes of this model). Real-world implementation will be required to better understand effects on key efficiency metrics.
Our estimates, based on figures from literature and CLCA methodology, indicate that the transition from conventional contrast CT scan process to utilising AiSentia could yield environmental reductions of 0.52 kilograms of carbon dioxide equivalent (kgCO2e) per scan.
Forecasting this per scan estimate from our model could result in positive environmental impacts for the opportunity case (100% adoption across the UK) and the likely use case (80% adoption of the solution across the UK; O’Dowd, 2016). These two scenarios estimate a monetary carbon value of £437k and £350k per year, respectively.
In addition, it is hypothesised that AiSentia could result in an increase in patient throughput as the digital contrast scan is performed quicker than the conventional scan. Unity Insights employed an attributional life cycle analysis (ALCA) methodology to crudely estimate that an increase in 25% and 50% of scanning efficiency with AiSentia could result in a reduction of 1.5 kgCO2e and 2.5 kgCO2e per scan, respectively.
The ALCA methodology was selected for this potential environmental impact because this approach better articulates scanning efficiencies should the real-world implementation of AiSentia change patient throughput. It should be noted that whilst efficiencies per scan could be improved, the overall energy consumption will increase as patient throughput increases, holding the number of scanners constant.
The potential wider impact of implementing AiSentia in secondary care is an improved patient experience, through avoidance of invasive IV injections, and improved environmental effect of CT scans, through removing the need to release iodine into water systems or dispose of single-use consumables, which both yield savings for the NHS and broader societal benefits.