## 1 Introduction
However, in practice, this gap cannot be completely abated by aligning WAD and WAI [26]. Thus, safety managers have to be aware of this discrepancy fostering adaptations that can allow the achievement of good outcomes [27]. Needless to say, the Learning principle is strictly related to the third principle, Awareness, as learning lessons from the analysis of adverse events or normal work can augment individual and team awareness of safety [15]. Based on the above considerations, this study aims to investigate the WAD-WAI discrepancies in two case studies related to the use of medical devices. For this purpose, the Functional Resonance Analysis Method (FRAM) [28] was utilized, as it represents one of the most well-known tools for systemic safety assessment in complex and dynamic domains [29]. FRAM is a performance-based risk identification tool to model socio-technical systems, which enables the characterization of these systems by providing a detailed description of their functional relationships [30]. The FRAM application can successfully allow safety managers to augment their awareness of medical device management [16]. As outlined by [PERSON] et al. [31], FRAM is mainly used for both retrospective and prospective risk analyses: the former use consists in the analysis of past accidents and, based on the reporting of these events, aims to examine how an adverse outcome propagated and how its causes or effects were amplified across the socio-technical system. In the latter case, instead, FRAM is used to model the behavior of the socio-technical system to better identify and manage its risks. According to [PERSON] et al. [32], healthcare is the most common sector that has employed FRAM for risk assessment purposes. The number of these studies can be considered significant,and this confirms the fact that hospitals are dynamic, partially described, and underspecified socio-technical systems, which need to be analyzed by means of a resilience engineering approach [2]. Most studies using FRAM in healthcare focus on the analysis of work procedures such as the discharge process of frail patients [33], the management of primary care [25], preoperative anticoagulation [34], and medication administration [35; 36; 37]. Other studies used FRAM for the analysis and improvement of intensive care units by focusing on the transition process of critically ill patients [38], the recovery following intensive care treatment [39], or the co-administration of multiple medicines [40]. Moreover, several studies aimed at the improvement of guidelines and safety procedures, for example, the provision of indicators to better manage and monitor the blood sampling process [41]; the identification of characteristics, skills, preconditions, and resources necessary for safety investigations [42]; and the implementation of a clinical decision support system to reduce the gap between WAI and WAD [43]. In contrast, in a few studies, FRAM was applied to analyze accidents/incidents in the healthcare context. For example, [PERSON] et al. [44] investigated incidents in blood sampling activities with the goal of bringing to light the most influential factors affecting the variability of the blood sampling process. [PERSON] et al. [45], by means of interviews with nurses and physicians, identified several incident cases and applied FRAM to create an instantiation and identify areas of variation. FRAM was also used to investigate an accident where surgical materials were left in a patient's abdomen during a surgical procedure [46]. However, compared to other sectors such as maritime and air transportation [47; 48; 49; 50], manufacturing plants [51], or offshore drilling units [52], the use of FRAM for accident/incident analysis in the healthcare context appears scarce. At the same time, the risk analysis of the use of medical devices by means of FRAM is also infrequent, although this method can be applied both prospectively and retrospectively for effective the risk assessment of interoperable medical device systems, as stressed by [PERSON] and [PERSON] [53].