Simulation for Complex Healthcare Management

Simulation for healthcare management is not just a training technique, but a powerful social technology capable of addressing the complexity of healthcare systems. Integrated into decision-making and governance processes, it allows observation, learning, and adaptation. This approach, supported by complexity science and Safety II principles, promotes organizational resilience and continuous improvement, representing a real evolutionary advantage for those called upon to manage the healthcare of the future.

1+1 does not always equal 2. In healthcare, reality often defies linear logic: what seems obvious on paper clashes with a daily reality made up of unpredictable variables, complex dynamics, and decisions made in high-pressure contexts. In critical contexts in particular, even seemingly simple clinical cases can quickly turn into complex scenarios, where cognitive biases and system interactions challenge our certainties.

This feeling comes back strongly during clinical audits: what seems impossible on paper happens in reality. And often, what is surprising is not so much the error itself, but the fact that in most cases, despite everything, the system works. It is precisely in these moments that the need for tools capable of exploring complexity and facilitating learning emerges.

In this sense, simulation is not just a training technique, but can be seen as a true social technology: a protected space where we can observe, act, reflect, and adapt, in line with the vision of healthcare as a complex adaptive system.

#14 Sharon Weldon: Driving Change, Innovation, and Collaboration in Healthcare Simulation

Complex adaptive systems and simulation: a new vision for healthcare management

The science of complexity can help us broaden our understanding of events by encouraging us to abandon rigid linear reasoning and the resulting attitude of astonishment in favor of a renewed and genuine curiosity to understand “how” things happen. This change in perspective transforms unexpected events into learning opportunities and recognizes the aspects that emerge from the dynamic interactions between actors and factors as intrinsic elements of clinical and organizational practice.According to the authors of Complexity Science as a Frame for Understanding the Management and Delivery of High Quality and Safer Care (1), the principles of complexity science offer a conceptual framework for understanding and improving the management and delivery of high-quality and safer healthcare. From this perspective, the model of complex adaptive systems (CAS) emerges, of which the healthcare system is an exemplary paradigm. These systems are characterized by diverse elements interconnected by nonlinear relationships. In the healthcare context, these elements include teams and individuals (healthcare professionals, patients, caregivers, administrative and auxiliary staff), as well as the resources available to them (physical and digital infrastructure, medical devices, equipment, protocols, and procedures). The dynamic interactions between these elements give rise to complex and adaptive behaviors at the systemic level. Some characteristics of these systems are:

• non-linear relationships: small variations in one part of the system can generate disproportionate effects on the entire system, making it difficult to predict the consequences of a single intervention;
• self-organization capacity: through local interactions between the elements of the system, a spontaneous capacity for adaptation and regulation emerges in response to information from the environment;
• emergence phenomenon: the dynamics between the individual elements of the system give rise to properties or behaviors that do not correspond to the simple sum of individual characteristics and cannot be predicted by analyzing the individual components in isolation;
• principle of action ecology (Edgar Morin) according to which the effects of an action in a complex system depend not only on the initial intentions of the actor, but also on all possible intro-retro actions of the environment and, in the long term, are unpredictable (2).

For further information on the characteristics of CAS, please refer to the text already mentioned and the other bibliographical references. But let’s get to the point: given that the system in which I operate is complex, whether I am a clinician or a manager, what strategies can I adopt to help make the provision of services and care increasingly safe and reliable? Can I control processes better?

In his essay Decalogo della complessità (Decalogue of Complexity) (2), De Toni offers a powerful metaphor to explain the difference between dealing with a complicated problem and a complex one, while also suggesting a framework for governing complex systems. The author asks us to imagine the flight of an aircraft from a point of departure to a point of arrival. If you fly in a motorized aircraft, the trajectory (the problem-solving model) is mapped out in advance, i.e., on paper before the flight, and the pilot will follow this trajectory. In this case, the pilot’s course of action is consistent with that of the traditional managerial approach: analysis-planning-implementation.

Conversely, if the flight is in a glider, the trajectory can only be reconstructed ex post, because the pilot must continuously adjust the controls to observe and learn how the glider responds to the wind and, based on these observations, adapt his response to the environment in order to reach the destination. The pattern of action in this case is action-learning-adaptation.

De Toni suggests that the governance of complex systems is more like flying a glider, where there is no optimal solution, but rather contextual solutions related to the here and now of the context. A CAS cannot therefore be controlled in the common sense of the term, but must be addressed by maintaining a high capacity to observe the effects of actions in order to recognize emerging patterns of behavior and characteristics, with a view to learning and adapting the intervention strategy accordingly. The action-learning-adaptation governance model appears to be the most suitable in complex contexts. From this perspective, the importance of the first step, acting, emerges. Only by acting can we learn and therefore adapt.

Furthermore, according to De Toni, the management of a complex project requires not only the expertise of qualified professionals, but also the contribution of “managers capable of ‘social technologies’ that encourage team initiative in reading the situation, taking risks, tolerating mistakes, individual and organizational learning, knowledge sharing, organizing collective memory, developing mutual trust, and spreading cohesion and cooperation between people and between operational units” (2, p. 55).

Simulation as social technology in complex healthcare management

In line with De Toni’s description, could simulation perhaps be interpreted as a form of “social technology”? And shouldn’t this form of “social technology” become part of the cultural heritage of healthcare managers, who are called upon to keep the healthcare system adaptive and resilient?

You can easily imagine my answer. I consider simulation a valuable form of ‘social technology’ that allows complex processes to be carried out in a protected environment and, through debriefing, to observe emerging dynamics and learn, in order to share individual and collective adaptive strategies.

Integrating the perspective offered by complexity science into our daily modus operandi can generate a series of consequences that may surprise us.

An example of such integration is the Safety II approach. In classic risk management, the adoption of a predominant methodology is recognized: Safety I. This is a reactive approach based on the linear causality model, which focuses solely on the analysis of negative events and asks why things went wrong. It searches for the chain of causes/events that led to the negative outcome, assuming that by identifying and eliminating the cause, the event can be prevented from happening again. However, with the increasing complexity of healthcare environments, some authors argue that this paradigm is no longer sufficient. From the science of complexity, which considers our healthcare environments as complex adaptive systems, a new approach has emerged, known as Safety II, which shifts the focus from the analysis of negative events to that of successful events, with a view to sharing the adaptive strategies necessary for building resilient systems (3).

Recognizing and valuing the contribution of healthcare professionals also means understanding the difference between work-as-imagined (work as described in protocols) and work-as-done (actual work, with all its unpredictable variables). In a context where emotional exhaustion and loss of meaning among healthcare professionals are on the rise, this paradigm shift can lead to greater appreciation of their role, more effective staff support strategies, and ultimately improved worker well-being (4).

From the perspective of frontline professionals (clinicians, nurses, healthcare workers), this approach can contribute to a reconciliation with the system, confirming enthusiasm for a form of training that is now essential.

Integrating simulation and governance for resilient healthcare management

For healthcare management, integrating this perspective means rethinking, for example, investments and how to allocate resources, not only for advanced healthcare technologies, but also to introduce and maintain one of the most powerful social technologies available: simulation.

Some authors, however, have highlighted difficulties in implementing the Safety II perspective, emphasizing the need to develop tools that promote the balanced and complementary integration of Safety I and Safety II perspectives in everyday practice (5).

Management that adopts simulation as a social technology tool, integrating it with traditional clinical governance tools, not only for training but also for analyzing and testing complex processes and procedures, could significantly contribute to improving system resilience through the sharing of adaptive strategies and ultimately contribute to improving health outcomes. A system that is able to adapt, learn, and constantly improve undoubtedly gains an “evolutionary advantage” in the long term, becoming increasingly capable of effectively addressing the challenges and complexities of the contemporary healthcare context (6).

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