## 1 Introduction  
In healthcare, as also in other business contexts, teams act in very complex environments and present very peculiar features [1; 2; 3]. Indeed, they are multidisciplinary, work under quickly changing conditions, and often stay together for a short period with a dynamically fluctuating team membership [4]. These aspects are particularly emphasised in complex and dynamic health operational contexts, e.g., in surgery, emergency departments, or intensive care units. Given the growing number of care activities delivered by teams and their potential societal impact, the optimal management of teams plays a relevant role in providing effective and efficient care for the entire health system [5; 6]. Thus, in the broad discussions about the future of healthcare, the strategy for improving the collaboration of medical teams is becoming a central topic. The increasing availability of data, models, and smart tools can help health managers to evaluate team collaboration dynamics, teamwork, and team performance determinants. However, the analysis of teamwork, particularly during the real operation of healthcare teams, remains a non-trivial task. Teams are highly complex and dynamic systems that are generally influenced by a large number of variables and evolutionary paths, e.g., the interactions between team members (individual agents) and their social structures; thus, they are driven to the final outcomes by the unpredictable patterns that emerged from these interactions [7; 8; 9]. Such characteristics of healthcare teams make them social complex systems; therefore, investigating team dynamics through the lens of complexity theory is a valuable and still underexplored research perspective [10]. Complexity theory regards the identification and analysis over time of complex systems--including health teams--in which the constituent elements give rise to the collective behaviours of the system [11; 12]. Such complex systems, as teams, can be described through their structural characteristics (e.g., team member features, behaviours, and interaction dynamics) and modelled as networks of interacting entities [13; 14; 15]. Hence, network analysis can be useful to describe and analyse the structure and behaviours of several complex systems found in the real world, as healthcare teams systematically explore performance drivers by exploiting concepts such as emergence, adaptability, self-organisation, resilience, and flexibility. In this aim, the rapidly increasing mass of data that has become available in many different healthcare domains contributes to making the empirical investigation of such complex system more and more suitable at affordable efforts [16]. In an attempt to explore this novel direction, this paper aims to discuss the different viewpoints about the application of complexity science and, in particular, the application of network science to teamworking in healthcare. Specifically, we highlight the potential benefits deriving from network science-enabled analysis and we stimulate the discussions regarding future implications and challenges in health environments. This work also tries to shed a light on the applications and potential impacts--in terms of efficiency and effectiveness--of network analysis during health service delivery. The intention is to pave the way for future research in this context by suggesting potential advantages and recognising the challenges and the threats of such an approach. We corroborate our discussion by providing a real case study in the Breast Unit of an Italian University Hospital. The network analysis of breast surgery teams permitted to understand and assess the influence of teams' structure and communication on the care performance in the operating rooms, in terms of occurrence of surgical glitches. Case results show that teams should adjust their communication and structure to meet changing situational needs when they face uncertain situations that have varying levels of complexity. The paper is organised as follows.