Systems Thinking In Health Care Essay

Systems thinking was developed during this SMAC by learning to identify the connections of Information tecnology and the health care system. This SMAC involved the understand of the health care system and the service they provide and how they operates. As a result, while systems thinking allows new and useful methods to improve patient safety, it comes with it its own intangible challenges that, if not documented and addressed, will both slow improvement and present new harm. This SMAC allowed us to see the challenges that healthcare system faces and how they analyzed and apply systems thinking in assessing them.Systems Thinking In Health Care Essay  Understanding the challenges by completion of a SWOT analysis. Which required the development of an action plan of improvement for patient safety by implementing and maintaining new processes and practices such has how can the functions of each component be optimized so that the results of the system are maximized? How can we identify and monitor for unintended consequences? How can we intervene to prevent harm from unintended consequences. It required the development of an action plan of improvement for the future. We have improved our ability to understand how health care system achieve the organizational goal of safe and excellent patient care by developing my ability at systems thinking. The contributions to this SMAC display both the complexity of a systems method to patient safety, and the promise of new ways in thinking about the systems within

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Across the healthcare professions, trainees are expected to provide patient-centered care, and to
do so they must develop competence in systems-based practice (Accreditation Council on
Graduate Medical Education, 2011; American Academy of Physician Assistants, 2012;
American Association of Colleges of Nursing, 2006, 2008, 2011; American Speech-LanguageHearing Association, 2015; Association of American Medical Colleges, 2014; Commission on
Accreditation in Physical Therapy Education, 2016; Liaison Committee on Medical Education,
2016). They are expected to work effectively in various delivery settings; coordinate care in and
across different delivery settings and with inter-professional teams; consider costs and risks vs.
benefits in care decision-making; advocate for optimal delivery and quality; and participate in
error detection and prevention (Canyon, 2013; Johnson, Miller, & Horowitz, 2008; Institute of
Medicine, 2000, 2001, 2003). More broadly, today’s trainees are also expected to improve
healthcare delivery and help transform the health sector from a focus on disease to prevention
and population health (Sandhu, Garcha, Sleeth, Yeates, & Walker, 2013; Trbovich, 2014). Systems Thinking In Health Care Essay
Effective systems-based care requires an understanding of the features and characteristics of a
“system” coupled with an understanding of how to think about that system, analyze it, and
approach enhancing it (Johnson et al., 2008). The foundational construct that needs to be applied
in systems-based practice is systems thinking (Johnson et al., 2008; Miles, 2004; Trbovich,
2014). Systems thinking is a body of knowledge, theory, and techniques applied to enhance
understanding of the interrelationships among elements, patterns of change, and structures
underlying complex situations (Three Sigma, 2002).
This monograph was created to help faculty, in the classroom and clinic, educate healthcare
professionals as systems thinkers who will appreciate, navigate, and improve the systems within
which they care for patients and populations. This monograph provides a basic understanding of
the metacognitive process of systems thinking and some of the tools available to assist you in the
design and assessment of educational activities, courses, and curricula.
This monograph has three specific aims:
1. Facilitate the development of a common definition and understanding of systems thinking
for health professions educators.
2. Provide ideas and approaches to teaching and assessing systems thinking, including
a. A taxonomy of sample topics
b. A taxonomy of learning objectives
c. Sample assessment strategies
3. Present common tools that develop systems thinking ability.
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Chapter 1 discusses what systems thinking is and how it is valuable for understanding complex Systems Thinking In Health Care Essay
environments. Chapter 2 provides concepts for teaching systems thinking in the classroom and
clinical settings. Those chapters are followed by a detailed discussion of the most common
systems thinking tools and ideas about how they may be applied in a healthcare setting. Using
these tools in designing learning activities and assignments will prepare learners to take a more
comprehensive, well-informed systems approach to their problem identification, data collection,
data synthesis, and, finally, decision-making.
The tools are divided into the following five categories:
Chapter 3: Tools to define and describe the system
Chapter 4: Tools to analyze and understand the system
Chapter 5: Tools to measure performance
Chapter 6: Tools to identify and test change
Chapter 7: Tools for simulation, modeling, and games
These chapters do not need to be read in their entirety but serve as a reference for you to draw on
depending on the situation. Care should be taken to select the appropriate tool, one that meets
your specific classroom or clinical objective. In other words, objectives drive tool selection, and
not visa-versa.
Chapter 8 discusses strategies for assessing your learners, again linking back to your overall
objectives. Finally, in Chapter 9, our goal is to help you pull it all together, taking what some
may consider more abstract or theoretical information and applying it to real-world case
scenarios. We provide a typical case scenario learners may encounter at each level of healthcare
delivery. In doing so, we offer exemplary questions and tools they might use to deconstruct and
analyze each case scenario from a systems perspective.\ Systems Thinking In Health Care Essay

Given its crucial role in the welfare and prosperity of societies, health care provision
has become a top priority for many governments. Statistics suggest that in most
countries, a significant fraction of public money is allocated to the health care sector.
Despite these huge investments, health care systems have not yet delivered the
expected improvements and populations are becoming more and more dissatisfied
with the quality of health care services provided.
An important factor, which explains these chronic failures in health care systems
management, is the inadequacy of the tools and methods used to analyse, design, and
implement actions and policies to manage them. While health care systems are
complex and include many interconnected elements, the rules and heuristics
generating managerial decisions are too simplistic to cope with the complexity
involved in such systems. The result is that the well-intentioned decisions, which aim
to improve the performance of these systems, lead generally to completely opposite
results, a syndrome known as “policy resistance”.
A remedy to this syndrome is to change the way of framing, formulating, and
analysing problems within health care systems. There is a crucial need to apply more
holistic approaches, which do not concentrate only on the analysis of a part of the
system, but incorporate all the sub-systems and their interconnections. This is
necessary because an isolated action taken within the context of a part of the system
may upset the current equilibrium of the whole system and cause the other subsystems to resist the action and defeat it. The most suitable tools and techniques to
tackle such situations are Systems Thinking and particularly System Dynamics (SD)
methodology.
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System Dynamics assumes that most problematic situations arise from the fact that
systems are dynamic and complex in nature. It conceptualises systems as being
constructed of complex networks of feedback loops in which time delays and nonlinear relationships are important sources of dynamic complexity and policy resistance.
To address these problems, SD offers a systematic approach relying on a combination
of qualitative and quantitative analyses. This includes mapping systems in terms of
feedback loops and then translating these maps into a rigorous quantitative simulation
model offering the possibility of analysing scenarios and consequences of policies and
actions. Systems Thinking In Health Care Essay
Given that health care systems exhibit high levels of dynamic complexity, SD has been
used extensively to analyse them and help decision-makers design and implement
effective policies. Areas of SD intervention include analysis of infectious disease
spread mechanisms, study of effectiveness of screening programs, design of primary
care systems, and finding the causes of waiting list escalation.
This paper explores the importance of applying Systems Thinking in health care
management. The first section includes the definition of Systems Thinking and policy
resistance. This is followed, in sections two and three, by the definition of dynamic
complexity, its causes, and its effects on decision-making performance. In section
four, the SD methodology is depicted and its steps briefly described. Section five
explains the reasons which make health care systems dynamic and complex and,
therefore, suitable to SD methodology. Some applications of SD in health care are
presented in section six and the paper concludes by presenting a case study of the
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reform of health care systems in the Republic of Georgia reflecting the importance
and usefulness of applying Systems Thinking approaches.
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 (Senge 1990).
Consequently, more often than not, actions taken to address these problems lead to a
breakdown in the system functioning and failure of the policy or strategy adopted,
creating a feeling of helplessness among decision-makers. Even in organisations with
the necessary ingredients for success, failure of policies or strategies is becoming the
rule rather than the exception (Sterman 1994).
A reason advanced to explain these disappointing results is the tendency to simplify
and underestimate the level of the complexity of the problem in question. Most often,
problems are the result of the interaction between a complex set of interconnected Systems Thinking In Health Care Essay
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elements. However, limited cognitive capacity of decision-makers results in a simplistic
analysis of the situation and the problem in question. As a result, the most important
sources of the problem are either missed or overlooked (Sterman 2000). The result is
that the decisions taken to eliminate a problem can have unforeseen consequences and
lead to undesirable outcomes, resulting in what is known as “policy resistance”
(Sterman 1994,2001).
The way to reduce the adverse effects of “policy resistance”, is to adopt a more
holistic view of problems: Systems Thinking (Senge 1990, Forrester 1961). It is
critically important that the decision-makers understand and appreciate that they are
working within systems that include many interconnected and interdependent
elements. Given this, problems should be seen as a result of interactions among the
system’s elements rather than the result of malfunctioning of a single component. This
is the essence of Systems Thinking, “the ability to see the world as a complex system”
(Sterman 2001) comprising many interconnected and interdependent parts. Systems
Thinking allows consideration of the whole rather than individual elements and
representation of time related behaviour of systems rather than static “snapshots”
(Senge 1990). Systems Thinking combines an array of methods and techniques drawn
from disciplines such as engineering, computing, cybernetics, and cognitive
psychology. Systems Thinking allows managers to overcome the feeling of
helplessness when confronted with complex problems. It gives them the necessary
tools to analyse, understand, and influence the functioning of the systems they are
trying to improve.
The disturbances in systems are due to a particular kind of complexity: “dynamic
complexity”. An understanding of “dynamic complexity” is a necessary step in
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understanding the underlying causes of complexity and the importance of Systems Systems Thinking In Health Care Essay
Thinking.
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 (Morecroft 1999, Sterman 1992, Senge 1990,
Richardson and Pugh 1981, Forrester 1961). Understanding dynamic complexity is a
mean to identifying the leverage points in a system to improve its performance and
avoid policy resistance.
There are three drivers of dynamic complexity in systems: (1) presence of feedback
loops; (2) time delays between the cause and effect of an action, and (3) existence of
non-linear relationships among the system’s elements. It is well recognised that
natural and human systems are multi-loop, dynamic, complex, and non-linear systems
(Forrester 1961, Richardson 1995, Blower and Gerberding 1998, Sterman 2000,). We
further expand on the sources of dynamic complexity:
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i). Presence of feedback loops: Most human thinking is based on the event-oriented,
linear, and open loop view of the world (Sterman 2001). Such thinking limits the
explanation of situations being a result of successive events linked by linear causeeffect relationships. However, in reality, such an indefinite linear chain of cause and
effect links does not exist. Any action taken by an agent in a system will only upset
the current system’s equilibrium and trigger reactions from other agents to restore the
system’s balance. These reactions generally affect the initial trigger of the action
establishing a circular loop. This circular relationship, which indicates that an
influence is both a cause and an effect, is known as “feedback” and lies at the heart of
Systems Thinking approach (Sterman 1994, 2000, 2001, Senge 1990, Forrester 1961). Systems Thinking In Health Care Essay
Therefore, from a systems thinking perspective, systems consist of many interrelated

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feedback loops in which actions are just an attempt to alter the equilibrium of some of
these loops. The reaction of the other feedback loops to these actions is the principal
cause for “policy resistance” observed in the real world as the system attempts to
restore its initial equilibrium. Similarly, the counter-intuitive results of many actions
are the result of inadequate understanding of the structure of the feedback loops
present in a system. Further, the so-called “side-effects” of actions are just effects,
which the decision-maker did not predict as a result of flawed and incomplete
conceptualisation of the feedback loops involved in the problematic situation
(Sterman 2000).
There are two types of feedback loops: Reinforcing (positive) loops and self-correcting
(negative) loops. The former describes situations in which any disturbance within the
loop variables is reinforced and amplified causing an exponential growth (or decline)
in the system. The latter represents situations in which any disturbance is resisted as
the system is directed towards a state of equilibrium to achieve a desired goal.
Although it is easy to infer the behaviour of each of these loops in isolation, if a
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system includes many interacting feedback loops, as is often the case, it becomes
impossible to predict how the system will behave. In fact, all the dynamics observed
in systems arise from shifts in loop dominance as the system evolves over time (Ford
1999, Richardson 1995). In this context, actions can be interpreted merely as
influences trying to shift the balance of power among the system’s feedback loops.
ii) Time delays: Commonly, it is assumed that an action immediately follows its trigger.
However, in reality, causes and effects are often not close in time and space (Sterman
2000, Sengupta et al 1999). These delays make systems more dynamically complex as
they slow the learning process by reducing the ability to accumulate experience, test
hypotheses, and apply findings to intervene to improve a particular situation (Sterman
2000). Further, if consequences of actions are not immediately apparent, agents will
continue to take actions to make the system converge to a desired state without giving
it the necessary time to absorb the effects of these actions and respond adequately. Systems Thinking In Health Care Essay
The result is an oscillating behaviour in which systems either overshoot or lag behind
their equilibrium. This behaviour becomes even more dramatic in situations where
some delays are “unobservable”: a context in which effective decision-making based
on intuition or experience becomes an elusive goal. As pointed out by Sengupta et al
(1999), “delays constitute one of the most important characteristics of dynamic tasks,
and the ability to handle them is essential for effective performance in such
environments”.
iii) Non-linear relationships: This source of dynamic complexity means that the
response (effect) of the system to an action (cause) is not always linearly proportional.
The presence of such relationships in a system increases dynamic complexity because
the response of the system to a disturbance will be different, as it will depend on its
current state. The same action may trigger completely unpredictable consequences, as
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the response of the system is contingent upon the current balance of power among its
feedback loops. Non-linear relationships may enable an action to become the trigger
of a shift in dominance from one loop to another, which exacerbates the frequency of
changes of power among the system’s feedback loops, hence increasing its dynamic
complexity.
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 two
reasons are of significant importance.
(a) Bounded rationality (Simon 1979, 1982): The principle of “bounded rationality”
stipulates that humans suffer from two bounds of rationality. The first is due to the
limited information processing capabilities of the human mind. When humans are
faced with the complexity of the real world, they focus on a reduced amount of
information and simplify their mental cause-effect maps by using linear thinking and
ignoring the side effects of decisions. Therefore, their mental models are not an
accurate representation of the real world. The second bound of rationality is due to
the cognitive skills and memory limitations of the human mind. Even if humans have
perfect information about the cause effect maps of a feedback system, they are unable
to work out the consequences of their actions over time in a complete and logical way.
In such situations, only a formal modelling approach can act as a learning catalyst and
improve the decision-making performance. As Sterman (1994) points out: “These two
different bounds of rationality must both be overcome for effective learning to
occur”. Systems Thinking In Health Care Essay
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(b) Misperception of feedback: The principle of “bounded rationality” applies in all
types of decision-making. But its effect is amplified in dynamic situations. It has been
observed that humans perform very poorly, relative to their potential, in situations
involving dynamic complexity. Experiments have shown that the performance of
humans decreases dramatically in the presence of high levels of dynamic complexity
(Sengupta and Abdelhamid 1993, Sterman 1989 a, b). This is even true when subjects
in the experiments have considerable experience or when financial incentives have
been given to reward better performance (Diehl and Sterman 1995, Paich and Sterman
1993). These experiments have been used as an evidence to prove the validity of the
“misperception of feedback” hypothesis, which suggests that mental models used by
people to guide their decisions are dynamically deficient. Humans ignore feedback
structures, do not appreciate time delays between actions and consequences, and are
insensitive to the non-linearities between a system’s elements as the system evolves
over time. (Diehl and Sterman 1995).
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 (Roberts 1978).
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
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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 (Lane and Oliva 1998).
The SD intervention process is divided into three phases (Lane and Oliva 1998,
Forrester 1961)
(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 Systems Thinking In Health Care Essay
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 (Vennix 1996, Vennix and Gubbels 1992, Morecroft
and Sterman 1992).
(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
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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 (Richmond 1987, Richardson and Pugh 1981) 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 (Barlas 1996, Forrester and Senge 1980). 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
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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 (Dangerfield 1999), 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 (Wolstenholme 1993).
Before describing briefly the different areas in which SD modelling has been applied
in health systems and health care management, it is necessary to explore the reasons
that make health systems highly dynamic and complex.  Systems Thinking In Health Care Essay