Maritime safety and emergency coordination constitute a0 highly complex operational environment characterized by real-time decision-making under time pressure, incomplete information, and dynamic and uncertain conditions. The growth of maritime traffic, vessel diversification, regulatory demands, and technological integration have significantly increased the cognitive workload of operators responsible for incident coordination and search and rescue (SAR) operations. Recent research recognises that maritime accidents cannot be explained solely from a technical perspective, but rather emerge from complex interactions between human, organisational, and technological factors, particularly in high-risk operational contexts 1–3. Foundational human factors frameworks remain relevant to conceptualise these interactions, emphasising situational awareness, communication, workload management, and decision-making under pressure 4,5. Despite advances in statistical, Bayesian, and machine-learning-based accident analysis 6–8, a gap persists between data-driven risk modelling and its operational translation into concrete coordination protocols and resource allocation strategies. The main objective of this work is to analyse the role of human factors and decision-making processes in maritime emergency coordination by integrating operational experience with empirical evidence derived from maritime accident analysis. The study focuses on identifying recurring patterns related to human performance in emergency scenarios and on examining limitations in current training and decision-support approaches within coordination centres, with particular attention to SAR operations. This approach is grounded in empirical results obtained from analyses of official maritime accident databases, which show that accident occurrence and severity are associated with interactions between technical, operational, and human variables rather than isolated failures 6. Cluster-based analyses have identified structural vulnerability configurations characterised by non-compliance with minimum safe manning requirements combined with specific vessel types and operational profiles, highlighting the influence of crew size and organisation on accident development and subsequent emergency management. From an operational perspective, such configurations are associated with higher operational demands and increased decision-making complexity in SAR coordination 1,3. This work adopts a qualitative and integrative methodological approach aimed at connecting empirical evidence from maritime accident analysis with the operational reality of maritime emergency coordination. The methodology combines three complementary elements: (i) analysis of results from previous studies based on real accident data, (ii) accumulated operational experience in SAR coordination environments, and (iii) a critical review of current training and decision-support approaches from a human factors perspective. Empirical findings derived from statistical and machine-learning-based analyses of accident databases are interpreted in light of operational practice within coordination centres, allowing the contextualisation of identified risk configurations and their implications for decision-making, communication, and resource management during SAR operations. The integrated analysis confirms that maritime accidents leading to emergency and SAR operations result from interactions among human, organisational, and technical factors rather than isolated failures. Accident data analysis reveals recurring risk configurations that systematically condition both the evolution of incidents on board and the operational burden transferred to SAR coordination phases. From an onboard perspective, scenarios involving non-compliance with minimum safe manning requirements—combined with specific vessel types and operational profiles—are associated with increased workload, reduced functional redundancy, and limited capacity to manage concurrent critical tasks during the early phases of an incident. From a coordination perspective, these configurations often lead to delayed incident detection, incomplete communications, and high levels of initial uncertainty, increasing cognitive workload and complicating early decision-making and resource allocation processes 1,7,9. This work proposes an integrative framework for analysing maritime emergency coordination that connects empirical evidence from accident analysis with the operational reality of SAR operations. The main contribution lies in providing a human factor–oriented perspective for interpreting accident analysis results and their implications for decision-making within coordination centres. From an applied standpoint, the proposed approach supports the early identification of high-demand operational scenarios and informs the design of training strategies better aligned with the real needs of SAR coordination. Finally, it establishes a quantitative and operational basis for future data-driven decision-support tools conceived as complementary aids to human judgment.
Candelaria Maceiras Tajes (Mon,) studied this question.