inappropriate organisational structures at both national and provincial health departments. These systemic delays among interacting variables result in the inability to deal with the health workforce crisis (SAHR, 2016). Furthermore, Sterman (2000) noted that our inability to understand the structure and dynamics of complex systems is hindered by SD application failure and the misperceptions of feedback. All dynamic complex systems are made up of networks of positive and negative feedbacks interacting with one another (Sterman, 2000).
4.2.3 System Dynamics Approach and Feedback Processes
The System dynamics (SD) approach provides health care planners with insight into the elements’
interactions, the relationships among these, the nature of the feedback and effect of changes in the healthcare system (Olmen et al., 2012). Thus, health care planners can make informed decisions associated with healthcare systems and sustainability challenges. Sterman (2000) described the system dynamics approach which acknowledges learning, as a relational feedback process. Moreover, he referred to feedback from the health ecosystem to decision-makers also provides both quantitative and qualitative information. This systems thinker and SD researcher explained that information feedback is interpreted by existing mental models which are also referred to as single-loop learning. Similarly, this single-loop learning feedback works in an environment of existing policies, in which decisions rule, and strategies, culture, and institutions inter-relate and are a consequence of our mental models (Sterman, 2000). In his seminal work, Forrester (1961) supplemented this notion of feedback learning processes by emphasising that all decisions are based on our mental models.
4.3 IMPACT OF MENTAL MODELS ON HEALTH SYSTEM
are described and studied. By applying Sterman’s (2000) explanation of SD approach, the boundary in this study is identified as the causal structure that is represented by a variety of variables
in CLD which determines the boundary of the model.
Figure 15: Causal Loop Diagram Mental Models (Adapted from Sterman, 2000) 4.3.2. Mental Models and CLD
In Figure 15, the CLD of mental models was adapted from Sterman (2010) to show that CLDs are appropriate for the actual expansion of the boundary of thinking and for communicating significant interdependencies. From the FGDs, it was recognized that the shift in thinking styles, for example
“rule-book” reductionist thinking (Reynolds, 2010) and linear, hierarchical decision-making processes to feedback system thinking (Morecroft, 2015), will result in harmony between thoughts, words and deeds, leading to congruency among managers’ thinking style, decision-making and policy
implementation. Expanding mental models positively affect managers’ productivity, increase job satisfaction, and decrease attrition. The change in mental models also produces an improved understanding of the interdependencies among the various elements in the health ecosystem.
Moreover, boundary judgements are interdependent and reflect the evolution of one’s thinking.
Revising a boundary judgement constitutes reflecting on one’s internal reference system. Changes in mental models can be roused by contextual feedback that is derived from physical, psychosocial or cultural domains. These changes which disturb mental models are referred to as double-loop learning.
This type of learning involves acquiring improved understanding or redefining a situation that in turn leads to reviewed goals, innovative decision rules and policy reforms (Atkinson, 2015).
Core behaviours in the learning feedback cycle can be unsuccessful due to dynamic systemic complexity, inadequate tangible evidence of ecosystem changes, confusing and vague description of variables, reduced systematic cognitive abilities, distrustful practices, and unforeseen obstacles to effect vibrant group interaction (Sterman, 2000).
4.3.3 Dynamic Complexity Feedback and Actor’s Mental Models
Dynamic complexity feedback in the KZN DOH occurs as a result of the interactions among diverse actors’ mental models, stereotypes in multiple interacting perceptions leading to vague variables, inadequate health human resource information, limited systemic thinking skills and organisational
“top-down” distrustful practices (Reynolds, 2010). The real world is complex. Forrester (1961) described the character of the feedback systems in system dynamics as multiloop, multistate and non- linear. Multiloop feedback results from the various interactions among various actors within and outside the health system. Diverse mental models among various actors result in complex non-linear feedback. One of the many feedback loops is influenced by the decisions of any one actor that functions in a particular system.
Decision-makers’ mental models and actions influence these feedback loops to react in both predicted and unexpected ways (Sterman, 2000). Applying Forrester’s (1961) description of feedback loops, which may be positive or negative, these loops will comprise various stocks or variables and several non-linearities in the system, as illustrated in Figure 16 below.
Figure 16: Stock and Flow Actors Network for Recruitment of Specialists (Adapted from William, 2010)
Describing stock and flow actors’ network for the recruitment of specialists in KZN DOH includes, for example, the main actors participating in the recruitment; training, development and appointment
Recruitment of
Registrars Qualified Specialists
Academic and Clinical Instructors
of specialist, namely, are registrars, academic and clinical instructors and specialists. The stock flow diagram in Figure 16 displays the interactions among these actors.
Figure 17: Actors in the Health System (Focus Group)
Other actors interacting in the healthcare system as shown in Figure 17, include patients, specialists, nurses, hospital managers, members of healthcare organisations, pharmacies, government regulatory groups, licensing and funding agencies, private health and insurance companies. Thus, various actors and numerous interactions occurring in the health ecosystem contribute to the behavioural complexity in the health care system. This relational complexity was acknowledged in the FGDs with the
resultant understanding that a lack of representation, consultation and dialogue among the actors causes counter-intuitive behaviour in the KZN health ecosystem. Counter-intuitive behaviour among the actors focuses on symptoms of difficulties like the high attrition of specialists, rather than understanding the underlying cause of inability to recruit and retain specialists, for example, in decision-makers’ mental models and policy interpretation.