Mapping the flow of digital data in clinical trials and assessing its carbon footprint

BackgroundHealthcare contributes 5% of global carbon emissions, with clinical trials forming a meaningful share. Existing trial emission frameworks inadequately capture and account for data storage and analysis emissions. Objective(i) Map the end-to-end flow of AstraZenecas (AZ) trial data and identify emissions hotspots; (ii) estimate emissions from the hotspots and assess their materiality to overall clinical trial emissions. DesignA top-down assessment of enterprise-level trial data volumes and associated emissions, and trial-level data-related emissions analysis of two representative trials. ResultsData flow mapping highlighted three hotspots: (a) data analysis in a statistical environment (entimICE) (b) Trial Master File (TMF) storage, and (c) short-term and long{-}term data storage by clinical research organisations (CROs). Within entimICE, the total volume of trial data stored for analysis across all active and recently completed trials at AZ was 100-125 TB, stored across four servers in Sweden, generating 80-100 tonnes COzeq annually. TMF storage emissions fell below measurable thresholds. CROs stored substantial data volumes, but per-trial emissions were likely insignificant due to economies of scale afforded by large data centres. ConclusionTo our knowledge, this is the first comprehensive study to assess the carbon footprint of industry-sponsored clinical trial data storage and analysis. The study highlighted the complex network of nodes and junctions involved in managing trial data. The results suggest that carbon emissions from trial data management form a small proportion and are unlikely to materially impact trial-related emissions. Future research should confirm these results in trials that employ energy- and data-intensive operations, such as artificial intelligence (Al). Article summaryO_ST_ABSKey highlightsC_ST_ABSO_LIThis is the first comprehensive study to assess the carbon emissions from clinical trial data storage and analysis. C_LIO_LIThe data flow hotspots identified in this study include (i) the statistical analysis environment, (ii) trial master file storage, and (iii) data stored by third-party clinical research organisations C_LIO_LIThe emissions from the first two hotspots did not significantly impact overall trial emissions. CROs store large volumes of trial data, but the per-trial emissions are likely to be insignificant due to the economies of scale offered by hyperscale data centres C_LIO_LIEmissions from clinical trial data storage and analysis form [a] small portion of overall trial emissions. C_LIO_LIFurther research must include the emissions associated with Al in the clinical trial data emissions framework. C_LI LimitationsO_LIEnergy-consuming components beyond data storage that make up [a] typical data server, such as backup servers, networking, and cooling systems, were not considered because isolating their functions for clinical trial data analyses was infeasible. C_LIO_LIThe complexity of cloud data server infrastructure, confidentiality concerns regarding server infrastructure, and gaps in publicly available data made it infeasible to access numerous data points that would have improved the accuracy of the calculations. C_LI