The Potential of System Dynamics to Model Patient-Aided Healthcare

The Potential of System Dynamics to Model Patient-Aided Healthcare

DOI: 10.4018/978-1-7998-2653-8.ch005

Abstract

This chapter explores the potential of system dynamics (or SD), a computer-aided methodology for policy analysis and design, to investigate patient organizations' contribution to healthcare. The chapter starts by describing the complexity features of the healthcare sector. Then it illustrates SD building blocks. A literature review of previous system dynamics applications to healthcare care issues categorizes selected papers according to relevant criteria. It emerges that few models incorporate patients' characteristics and perspective, none of them specifically dealing with patients' organizations and patient co-created health. In conclusion, the chapter highlights how SD can be considered a suitable methodology to depict the outcome of patients and their organizations' participation to healthcare processes, filling a gap in literature about both qualitative and quantitative system dynamics.
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Background

Patients’ organizations are actors belonging to the third sector and operating within healthcare. The latter is a system characterized by complexity, because of the relevant number of interrelated actors, variables and processes, involving many aspects, included organizational leadership, management and decision-making (Plsek & Greenhalgh, 2001). Thus, it is opinion of the chapter’ author that studying these organizations cannot neglect the complexity of the health system (Bar-Yam, 2006; Patel & Cohen, 2008; Plsek & Greenhalgh, 2001) in which they are embedded.

Such complexity is exacerbated by new technologies and the increasing consumer expectations and awareness of health issues, which put pressure on practitioners of all disciplines and healthcare organizations to accommodate innovation and simultaneously ensure that their current practices are effective, safe and efficient (Forbes, & Griffith, 2002, p. 141). Notwithstanding, there can be registered failures to adopt innovations even when supported by sound evidence, or, at the contrary, the perseverance of ineffective practices (Sackett et al., 1998). In fact, evidence-based healthcare, although useful to ensure that medical practice is aligned with current best evidence, is affected by limitations, since it just focuses on evidence from randomized controlled trials, neglecting information generated by qualitative research or by the experience of practitioners themselves (Pawson, 2002). According to scholars, evidence-based medicine is not in contradiction with patient-centered practices (Epstein & Street, 2011) and with shared decision making (Hoffman et al., 2014). In this stream, evidence-based medicine and shared decision making are two essential and complementary aspects for the delivery of quality healthcare, albeit the processes of patient integration in the clinical choices are still poorly mapped and scarce attention is devoted to them (Hoffman et al., 2014). However, despite the today’s sensibility to these themes, patients still result substantially excluded by the decisions regarding their health (Barry & Edgman-Levitan, 2012).

On the other side, medicine is traditionally founded on classical reductionism, where a certain problem is broken down into its smallest components, examined, and then the retained information used to draw conclusions about the nature of the larger reality (Tuffin, 2016). Such an approach is successful for linear systems, i.e. systems characterized by relatively low complexity and predictable and proportional behaviors in response to external influences (Plsek & Greenhalgh, 2001). Conversely, the complexity paradigm, based on systems theory, informatics and cybernetics, investigates the healthcare as a complex adaptive system (Fajardo-Ortiz et al., 2015). Far from the reductionist healthcare epistemology, it presents a broad conceptualization to understand complex systems (no matter if they are physical, biological or social), sharing the same characteristics. According to Kannampallil and colleagues (2011) interrelatedness, meaning that system components influence each other, is a key property defining complex systems and the level of their complexity.

Then the complexity of a healthcare system increases with the number of its components, the relations between them, and the uniqueness of those relations. This latter signifies that expanding a system by mere repetition of relations and components does not substantively increase its complexity. From interrelatedness descend other complexity properties, such as non-decomposability, emergence and nonlinear behaviors. Non-decomposability means that a complex system, like the healthcare one, cannot be understood by looking at isolated components, but at substantially interrelated ones. Emergence means that interactions between components of complex systems often lead to systems’ unexpected behavioral properties, resulting from the system’s self-organization processes and complicating system responses to external influences (Fajardo-Ortiz et al., 2015).

Key Terms in this Chapter

Stocks: They are the state variables within a certain system. Their level depends on accumulation and depletion processes resulting from the difference between their inflows and outflows.

Quantitative System Dynamics Modelling: Representation of dynamic problems by mean of models’ mathematical formalization (building of a set of equations representative of decision rules and behavioral relationships), estimations of parameters, testing and calibration. Quantitative SD modelling allows to simulate problematic behaviors and possible scenarios according to different conditions and does not necessarily relate to quantitative researches. Indeed, it is possible to enrich qualitative research design with quantitative SD tools. It is also possible to make statistical analyses on simulated behavior patterns.

Qualitative System Dynamics Modelling: Representation of dynamic problems by mean of causal loops and stock-and-flow diagrams, with no mathematical formalization of the system dynamics models.

Sensitivity to Initial Conditions: A property of complex systems, consisting on the fact that the initial system conditions are able to influence dramatically their behaviors, so that systems with similar structures can witness different behavior patterns (for example exponential growth or decay) due to differences (sometimes slight) in initial parameters.

Emergence: Feature of complex systems, meaning that the interactions between system’s components lead to unexpected behavioral properties, resulting from system’s self-organizational processes.

Uniqueness of Relationships: A factor determining the interrelatedness, and then the complexity, of a system. It means that the complexity of a system does not increase if we just expand its boundaries by mere repetition of relations and components. Then, in order to increase the system’s complexity, the new relations need to be unique, original and substantive.

Interrelatedness: Key property of complex systems defining the level of complexity systems and being the cause of other typical features of complex systems. Interrelatedness means that system components influence each other, so that system complexity increases with the number of its components, the relations between them, and the uniqueness of those relations.

Flows: State variables’ rates of change. In system dynamics, they are represented by pipes, feeding (inflows) or draining (outflows) stock variables.

Causal Loop Diagram: In system dynamics modelling, they are closed causal chains involving relevant variables, whose interactions are responsible for the patterns of behavior taking place within a certain system.

Non-Decomposability: Property of complex systems, according to which the system cannot be fully understood and investigated by just looking at isolated components, but at the interrelations constituting the entire system.

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