Abstract
BACKGROUND: Delays in organ retrieval following brain death (BD) can compromise organ viability, increasing the risk of post-transplant complications. In 2021, the Transplant Authority of Tamil Nadu, India, implemented an artificial intelligence (AI)-based application aimed at expediting data verification to reduce delays and improve transparency in organ procurement. This retrospective observational study evaluated the effect of this intervention and identified key factors contributing to delays. METHODS: Data were collected from organ donors declared dead by neurological criteria (DND) between January 2018 and December 2023. Donors were categorised into two groups: pre-AI implementation (P1) and post-AI implementation (P2). Factors leading to delay were classified into four domains: family-related, physician-related, institution-related and government-related domains. A fishbone analysis was used to identify root causes. RESULTS: A total of 45 DND cases were analysed. The median time from the first apnoea test to organ procurement was 1657 (IQR, 1499-1899) min. A statistically significant increase in the retrieval time was observed at P2: 1587 (IQR, 1328-1779) min at P1 vs 1660 min (IQR, 1556-1959) at P2 (p=0.04). This increase was primarily driven by longer delays in transferring patients to the operating room after legal verification, which rose from 125 (IQR, 96-231) to 384 (IQR, 186-457) min (p=0.002). CONCLUSION: This study underscores critical factors affecting organ retrieval timelines in a low-income to middle-income setting. While the AI-based protocol enhanced data verification and transparency, it also introduced unanticipated procedural delays. Ongoing evaluation and iterative refinement of AI tools are essential to optimise organ procurement efficiency and clinical outcomes.