Mapping the flow of data in clinical trials and assessing its CO2 emissions

Background: Healthcare contributes 5% of global carbon emissions, with academic and industry-sponsored clinical trials forming a meaningful share. Existing trial emission frameworks inadequately capture data storage and analysis emissions, leaving their contribution to overall trial carbon footprint poorly understood. Objective: (i) Map the flow of trial data from the investigator site to the final trial documentation and study report in the trial master files (TMF), and identify emissions hotspots (ii) estimate emissions from the hotspots and assess whether they materially impact overall clinical trial emissions. Design: A top-down assessment of enterprise-level trial data volumes and associated emissions, and trial-level data-related emissions analysis of two representative trials. Results: The data flow mapping highlighted three carbon emission hotspots: (a) data analysis in a statistical environment (e.g., entimICE) (b) Trial Master File (TMF) storage, and (c) short-term and long-term data storage by external 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 100-125TB, stored across four servers in Sweden, generating 80 to 100 tonnes CO2eq 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 in large data centres. Conclusion: To our knowledge, this is the first study to comprehensively 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 storage and processing are currently a small proportion and unlikely to materially impact overall trial-related emissions. Future research should confirm these results with clinical trials that employ emerging data-intensive computational operations, such as the integration of artificial intelligence (AI).