Question

Is Systems thinking is important for health care administration leaders to gain understanding into health care quality. The internal structures, processes, and outcomes, as well as the external environment, have significant and sometimes predictable effects on the delivery of cost-effective and quality health care.

Describe the organization that I could selected and Explain the input, throughput, output, outcomes, and feedback from a systems-level perspective for the organization you selected.

and Draw a diagram representing the system of the organization that I could selected.

Explain why it is important to understand systems thinking in health care organizations. Be specific and provide examples.

Answer 1

Systems thinking is a problem-solving approach that analyses a problem within its system - surrounding elements that interact with the problem or are affected by it, and together form a process that achieves the goal of the system. A systems thinking concept is often used in management and operations science across the range of industries, from agriculture to telecommunications. In healthcare, it is also known as systems-based practice.

1. Importance and role of Systems Thinking
The external and internal environments within which organisations, health systems and the society operate have become very dynamic and complex. Such dynamism and complexity brings problems and opportunities and requires responsive organisations and systems that are able to adjust to the changes. Ability to respond depends on an ability to understand both the external and internal environments. Traditionally applied tools and procedures are inadequate to understand these complexities, solve emerging problems and capitalise on opportunities. To manage the complexities and problems arising from a rapid pace of change, managers need to absorb a vast quantity of information, often beyond their capability, understand a complex web of interdependence among systems’ elements and the problems in question, and keep pace with the constantly changing situations.

2. Dynamic complexity
An underlying reason for poor decision-making in complex systems is that most managers focus on “detail complexity” that refers to a type of complexity in which the decision depends on choosing an alternative from a large number of static options. Given the large number of options, the selection of a single option may be difficult, but decision making can be aided by mathematical modelling and computing. However, system failure is often due to the inability of managers to manage “dynamic complexity”. Dynamic complexity arises when: (a) the short and long term consequences of the same action are dramatically different; (b), the consequence of an action in one part of the system is completely different from its consequences on another part of the system, and; (c) obvious well-intentioned actions lead to nonobvious counter-intuitive results

3. Effect of dynamic complexity on decision-making performance
High levels of dynamic complexity adversely affect human decision-making. Indeed, often the decisions do not generate optimal, or even reasonable outcomes. There are many reasons for such under-performance in dynamically complex situations, but tworeasons are of significant importance.

4. System Dynamics methodology
System Dynamics (SD) was developed at the end of the 1950s and the beginning of the 1960s at the Massachusetts Institute of Technology’s Sloan School of Management by Professor Jay Forrester who tried to apply the principles of engineering feedback control principles and techniques to management and social systems. The principal philosophical basis of System Dynamics method is that the behaviour (time history) of a system is principally caused by its internal structure. In this context, SD assumes that the system structure is essentially composed of feedback loops in which delays and non-linearities are important drivers of a system’s behaviour. SD aims to model and predict possible responses of such complex systems to different decisions so that their leverage points are identified or their structures are redesigned to eliminate undesirable behaviour.

The SD intervention process is divided into three phases
(i) Definition of a study purpose: Any SD model should have a purpose, a defined problem, or an undesirable behaviour to be corrected. The variables of interest are described in a reference model that is a graphical representation of their observed history path. The factors believed to cause the behaviour are identified and the relationships between them described and modelled in the form of causal loop diagrams (CLDs). The relationship between the causal structures and the observed behaviour is called the “dynamic hypothesis”: an initial possible explanation of how a system’s structure is causing the observed behaviour. A parallel description of the decision-making process is conducted to determine how agents in the system transform information into decisions in order to include the information flows in the CLDs. This phase is essentially the conceptual qualitative phase of the intervention. It is important to emphasize here that this phase should not be conducted by the “SD modelling expert” alone. Recent developments in SD demonstrate the importance of involving the people in the problematic situations early into the mapping process in order to “capture” their mental models and elucidate their knowledge about the possible causes of the problem.

(ii) Model building: Once the qualitative structure describing the problem situation has been framed into CLDs, the next stage is to build a computer-based behavioural model which reflects the qualitative structure. The stocks (variables subject to accumulation and depletion processes over time) and the flows (which determine the time related movement of units from one stock to the others) are determined and the relationships between them defined. In this phase, a link is established between the variables and their dynamic behaviour. The quantitative nature of this phase makes it the most important one in terms of generating insights about the situation. It is important to notice here that many specialist software programmes have been written for SD modelling to make the process easy and accessible to people even without strong computational background.

(iii) Using the model in the problem situation: Before the model is used for the purpose of policy analysis, it is necessary to built confidence into it. This process is called validation of the model. Because a model is a trial to “replicate” the reality, it is necessary to make sure that it can replicate, at a satisfactory level, the time path of the variables in the system. Many procedures are described in the literature to test model validity and build confidence into it. Once the model is validated, it can be used for different purposes. This may include, testing the impact of different policies, exploring what-if scenarios or optimising some substructures in the system. Ultimately, the model is used as a base to derive policies or structural changes.

5. Suitability of System Dynamics modelling for health care systems Health systems are complex. This may explain the disappointing results of policies to improve the performance of health systems. From an SD point of view, they exhibit high levels of dynamic complexity and are, therefore, subject to counter-intuitive behaviour and policy resistance. Although a significant fraction of many governments’ budgets are allocated to health, results have hardly matched expectations as many health system performance indicators have shown limited improvement. In this context, SD modelling can be an effective tool to address many of these concerns and contribute towards improved health system performance or better health care provision. This contribution can be significant as the SD modelling methodology can deal effectively with strategic and tactical problems involving aggregate flows of patients and resources, and key elements in a health system. SD modelling offers a unique opportunity to improve decision-makers’ understanding of the sources of their systems’ under-performance as it allows both qualitative and quantitative analysis, which lead more easily to consensus building, improved shared understanding, and enhanced organisational learning.

Reasons that make health systems highly dynamic and complex.

(a) Health systems involve many interacting feedback loops: These loops occur as many elements in the health systems interact and have mutual influence on each other. Such interactions cannot be adequately captured by linear representation as they are inherently a circular chain of cause and effect relationships.

(b) Health systems decisions involve many delays: This means that cause and effect in these systems are not close in time and space. This renders management of such situations problematic because if consequences of actions are not immediately visible, decision makers tend generally to take dysfunctional actions while trying to restore the system to a desirable state.

(c) Health care systems involve many non-linear relationships: This means that the response of an element in the system to an input (action) can be completely different from what may be intended or predicted because the response will depend on the system’s current conditions. This indicates that the effects of the same managerial action can be different as they are contingent upon the state of the system when the action is taken.

(d) Health systems involve “hard” and “soft” elements: Making decisions on the basis of the information on “hard” variables is not difficult as this information is easily available, understood, and not subject to much argument. However, health systems involve a strong human element and the “soft” variables that represent aspects of human behaviour and responses must be taken into account.

6. SD applications in health care management
SD offers many advantages in terms of modelling and analysis of health systems and has been widely applied to aid health care management decisions for a multitude of problems ranging from simple and well focused health care delivery programmes to larger and more complex socio-technical issues. The most important areas in which SD modelling has been applied include,:

• Disease transmission and public health risks assessment.
This stream of research includes the modelling of infectious diseases and the impact of different intervention strategies to limit their spread in human populations. Given the dramatic consequences of such diseases on public health and the economic and social costs associated with them, developing effective policies to contain them while ensuring a best use of the available resources is crucial.

• Screening for disease.
The performance of different screening policies as well as their cost-effectiveness has constituted an important area of application of SD modelling in health care. Given the importance of screening as a tool to detect a disease before it causes harm, and its impact on disease transmission mechanisms, it was important to evaluate the medical, social, and financial consequences of different screening strategies.

• Managing waiting lists.
Waiting lists are a “hot” political issue. It is not surprising, therefore, that the problem has attracted a great deal of SD modelling. The dynamics of waiting lists have been studied in different contexts and many models have been built to analyse variables influencing their size and length as well as the impact of policy decisions.