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Aims and Objectives Chest pain accounts for a major proportion of emergency department (ED) attendances, making the rapid and accurate exclusion of acute coronary syndrome (ACS) a key clinical priority. High-sensitivity cardiac troponin assays are crucial diagnostic tools, and their optimisation can enhance patient care and hospital efficiency. This study aimed to evaluate the impact of implementing a new high-sensitivity cardiac troponin I (hs-cTnI) assay on ED length of stay (LoS) and admission-to-discharge ratios.Method and Design A single-centre retrospective cohort study was conducted in an inner-city hospital ED in southwest England between December 2022 and February 2024. Patients presenting with chest pain who had a troponin test were included, excluding those with incomplete patient journey data. The study compared outcomes between high-sensitivity cardiac troponin T (hs-cTnT) and hs-cTnI assays across two separate three-month winter periods. The primary outcome was ED LoS, with secondary outcomes including admission-to-discharge ratios. Statistical differences in LoS were assessed using two-sample t-tests between assay groups.Results and Conclusion Of 7,604 eligible patients, 5,638 were included in the analysis. Among patients discharged after the first troponin assay, mean LoS was reduced by 15 minutes when the hs-cTnI assay (331.75 minutes, n=1,169) was used, compared with hs-cTnT (347.17 minutes, n=900) (p=0.02). For patients requiring two samples, mean LoS was reduced by 80 minutes with the hs-cTnI assay (545.28 minutes, n=139) compared with hs-cTnT (625.21 minutes, n=99) (p<0.03). Implementation of the hs-cTnI assay in the ED was associated with a meaningful reduction in LoS, particularly among patients requiring two-sample testing, supporting faster and safer exclusion of ACS. The new assay facilitated quicker decision-making and discharges, with associated time savings contributing to improved patient flow and resource utilisation. These findings support broader adoption of hs-cTnI assays in ED ACS exclusion pathways for enhanced departmental efficiency.