The SD approach and the complexity theory methodology (Sterman, 2000) was adapted to
hypothesize and investigate the causative effects of the challenges, the relationships among variables and the dynamic complexities arising in the health system. This qualitative SD research approach was used to critically examine attitudes, opinions and behaviour in the KZN DOH ecosystem (Kothari, 2004).
3.3.1 Research Technique
The research technique used was focus group (FG) interviews, with dialogue to specifically identify variables and the verification of data collected. The FG understanding of the challenging situations and transformative interventions were sought using the SD approach (Sterman, 2000). According to Reynolds (2010), the SD approach involves systemic processes like rational thinking about organisational behaviour and simulating challenging circumstances, through developing causal loop diagrams (CLD) that enhance visualising how the elements fit together, interact and change over time.
Thus this SD approach was used to deepen our understanding of the problematic situation in the KZN tertiary hospitals. Feedback loops in CLD; feedback loop structures and time delays that affect dynamic system behaviour, have been utilised to understand the context, challenges, systemic relationships, decision-making processes which impact of dynamic complexity and the leverage points for organisational transformation (Sterman, 2000; Meadows, 1997).
3.3.2 Dynamic Hypothesis
As organisational resource and policy challenges are central to my study, I surveyed literature for theories that explained system expansion or limitation and its effects on KZN DOH dynamics and growth. Morecroft (2015) identified that most organisational limitations occur internally and are enforced by managerial policies or lack of coordination. In addition, he described organisational expansion as growth in resources or increased distribution of services. The lack of coordination among these organisational elements has been developed into a systemic framework which Morecroft (2015) calls the Growth and Underinvestment Model. Thus, the dynamic hypothesis used in my study was based on this Morecroft Growth and Underinvestment Model (Morecroft, 2015).
Figure 9: Growth and Underinvestment Feedback Structure (Adapted from Morecroft, 2015) In Figure 9, at the top of the Morecroft’s growth and underinvestment model is a reinforcing growth feedback loop formed by the links between demand and growing action.
In this current study, application of Morecroft’s (2015) growing action as described, has been observed in KZN DOH as the expansion of services, which led to increased patient demands, affecting health systemic behaviour and in turn justifying further expansion, for example, of PHC.
Further explanation of this model is on the right in the figure, a limiting process that depends on performance of services and which indicates value or lack thereof, such as patients’ perceptions of PHC delivery in this province. Moreover, growing performance of services correlates with access, quality, reliability, value for money, and patient satisfaction; as a result, it attracts more patients.
Service performance and patient satisfaction depend on the balance between demand and HR
capacity. When an organisation’s capacity to provide services decreases, for instance, and is deficient in-patient demand, then performance will deteriorate. In time, service performance as perceived by patients will drop too and therefore limit demand. Conversely, if there is suitable HR capacity then, demand will carry on growing under the influence of the growth feedback loop. Similarly, a balancing loop for capital investment is shown on the lower left of Figure 9. This loop connects organisational performance, management’s perceived need to invest, investment in capacity and developing capacity. Thus, this balancing loop nurtures growth by striving for sufficient capacity to maintain satisfactory health service performance (Morecroft, 2015).
Observations by the FG interviews noted that dynamic complexity of organisational structures and governance sub-systems is entrenched in the KZN DOH, which influences the system behaviour and
Growth
DELAY DELAY
DELAY Floating
Goal Limiting
Process
Capital Investment
inter-relationships among actors. In this turbulent and evolving health ecosystem, the dynamic complexity revealed a high number of interactions among the sub-systems, which illustrated that the more complex the system is, the higher degree of dynamic complexity the system displays (Sterman, 2000).
3.3.3 Sampling
A purposive sampling approach (Kothari, 2004), consisting of a wide variety of actors, including both decision-makers and policymakers representing various roles, was used. Currently the number of actors varies among decision-makers and policy makers, which is approximately twenty to thirty. The role of the actors in the formulation and implementation of policy and health strategy was the criterion used to determine sample size, which constituted eighteen actors.
Figure 10: Diagrammatic Representation of the Sample
Figure 10 represents the following roles in the sample: the decision-makers and policymakers consisted of two representatives from KZN DOH and one from UKZN; one from HR Planning and one from HR Practice; two from Finance; four Clinicians/Heads of Department representing Critical Care, Paediatrics, Obstetrics and Gynaecology and Oncology respectively, and seven Senior
UKZN – University of KwaZulu Natal HR – Human Resources
CC – Critical Care PAEDS - Paediatrics
O&G – Obstetrics & Gynaecology ONCO - Oncology
Mx. - Management TH – Tertiary Hospitals
Managers from the four Tertiary Hospitals. Thus the total number of participants in the focus group was eighteen. Ethical approval was obtained from KZN DOH Research Review Committee. Being mindful of my ethical responsibility to my research participants, consent forms were prepared to protect and ensure the confidentiality, dignity and welfare of all. Participants were asked to sign a consent form and were informed that participation was voluntary and that they could opt out at any time. Likewise, to ensure anonymity, initials rather than names of individuals were used. Ten focus group discussions (FGD) were facilitated by the researcher, and completed the questionnaire referred to in appendix 3.
3.3.4 Data Collection
These FGDs, involving actors from the sample, were held in the four tertiary hospitals and at the provincial office, as well as formal meetings with minutes, or organized as interactive sessions were conducted. FGDs were facilitated to collect data on the health ecosystem context, policy processes, HR strategies, decision-making processes and relationships among actors, policy implementation and the system behaviour. This data, which was collected from the FGs, where the conversations and dialogues were noted, and systemic issues was used to codesign CLD. The group’s awareness of policies and CLD using identified variables, linkages and flow of information among actors from these FGs, were developed. The FG verified the CLD and iterations, where needed, were redeveloped.
Data collected were associated with Sterman’s (2000) SD model, which showed that positive
feedback between expectations and perceptions inhibited the acknowledgment of anomalies, and that emergence of new paradigms became evident.
The case study method to identify the source of the problem, and reflect on the mental models of the actors and the information gathered, was used to compare their understandings of the research problem (Kothari, 2004).
HR policies were used to qualitatively analyse data (Creswell, 2014) from the HR officials’ mental models. For example, it was noted that these policies were misinterpreted and that the mental model influenced the source of the problem, that is, the shortage of specialists in KZN DOH. Reflections on the mental models of the actors and the information gathered in CLD were used to compare their understandings of the problem (Kothari, 2004).
3.3.4.1 Counter-intuitive Behaviour
Another observation made in the FG was counter-intuitive behaviour amongst stakeholders. For example, HR officials and specialists had differing understandings of the HR investment capacity.
These opposing worldviews among actors result in counter-intuitive behaviour, that can be described
as provoking reactions by other actors and leading to the emergence of dynamic equilibria (Forrester, 1975). Counter-intuitive behaviour involves differing paradigms affecting decision-making, policy resistance, delays and dynamic systemic feedback (Meadows, 1982). Data generated from FGD reflected that these actors’ counter-intuitive behaviour, event-oriented worldview, and participation in feedback structures and behaviour, informed the development of CLD. In KZN DOH, counter- intuitive behaviour is eminent, as the intense reactions by managers seeking to restore the balance in the health system, when making decisions to decentralize health services or address the need for public health specialists at district level to implement the NHI, for example, have been met with resistance among these specialists.
3.3.4.2 Database
We also codesigned CLD to compile and analyse the qualitative data. An archival database was derived from the register of medical professionals, that is, the Health Professional Council of South Africa (HPCSA), Personnel Salary (PERSAL), UKZN academic registrations and Tertiary Hospital data records of specialists employed. Additionally, population projections and general mortality rates from the National Institute of Statistics were used to determine ratio of full-time equivalent doctors per 100 000 population. These descriptive statistics were used to explore the quantitative data (Morecroft, 2015).
3.3.5 Data Analysis
The data analysis of CLD and stock and flow diagrams of the four Tertiary Hospitals were verified by the FGD. These CLD were used to interpret and communicate dynamics or performance through time (Sterman, 2000; Morecroft, 2015).
3.3.6 Testing
Testing was done by comparing through triangulation with actual behaviour observed in the system.
Triangulation of data refers to the consistency of data that was collected through multiple sources, which included FG interviews, observations and document analysis (Creswell, 2014). As described above in 3.3.4, FG interviews were recorded on the questionnaire, in minutes of meetings, and from policies reviewed. Policy document analysis, for example, reviewing HR policies, involved the HR research participants and clinical staff, who analysed the policy processes, procedures and systemic effects of policy implementation. This data was used to test the pragmatic assumptions observed in the KZN health ecosystem behaviour, and from which variables were identified and which informed codesigning the CLD.
3.3.7 Results and Interpretation
Results and interpretation of this study occurred through the use of the iThink and VUE computer software. VUE is computer software comprising components which help in abstracting CLD. Data, in the form of variables derived from FGD, are captured onto this software and the component provides organisation and encapsulation into feedback loops (Sterman, 2006). CLD visually conveys complex information which enables recognising systemic patterns and relationships among variables, and shapes qualitative discussion about feedback effects. Data presented in CLD simulate interactions and allow for analysing these relationships. Mental models, contextual variables, environmental
projections, organisational behaviour and interactions among people, sub-systems and strategies, can be visually represented using VUE (Williams, 2010). iThink software makes it possible to capture our mental models in a diagram, by drawing a map of the interconnections and relationships in a system.
The iThink simulation capabilities make it possible to study the dynamics that result from those interconnections (Kreutzer, 2018).
3.3.8 Reliability and Validity
Reliability and validity of the study is aligned with SD methodology (Sterman, 2000). That is validation of the CLD was conducted with the experts in the sample. A pilot study to pre-test and modify the questionnaire was also conducted. This pilot study, in the form of management meetings, managing by walk about and debriefing sessions, was conducted at King Edward Hospital, one of the tertiary hospitals identified in this study. Ten representatives on this pilot study group involved the same category of actors as described in the sample on page 28 of this study. No changes were made to the questionnaire after the pilot study was conducted.
3.3.9 Bias
Researcher bias, according to Sterman (2006), is influenced by policies to promote public health and welfare, and often fails or worsens the problems they are intended to solve. This, he claimed, was evident in decision-makers who often continued to intervene to correct apparent discrepancies between the desired and actual state of the system, even after sufficient corrective actions have been taken to restore equilibrium. Furthermore, Sterman (2000, p 10) notes that:
“Policy resistance arises from a narrow, reductionist worldview, because we do not
understand the full range of feedbacks surrounding and created by our decisions. Delays also create instability and fluctuations that confound our ability to learn. Decision-makers often continue to intervene to correct apparent discrepancies between the desired and actual state of the system even after sufficient corrective actions have been taken to restore equilibrium.
Evidence-based learning should prevent such policy resistance by using the system dynamics
approach and testing. System dynamics approach is an iterative process that also helps in reducing bias”.