Peters Health Research Policy and Systems 2014, 12:51http://www.health-policy-systems.com/content/12/1/51

COMMENTARY Open Access

The application of systems thinking in health:why use systems thinking?David H Peters

Abstract

This paper explores the question of what systems thinking adds to the field of global health. Observing thatelements of systems thinking are already common in public health research, the article discusses which of the largebody of theories, methods, and tools associated with systems thinking are more useful. The paper reviews theorigins of systems thinking, describing a range of the theories, methods, and tools. A common thread is the ideathat the behavior of systems is governed by common principles that can be discovered and expressed. They eachaddress problems of complexity, which is a frequent challenge in global health. The different methods and toolsare suited to different types of inquiry and involve both qualitative and quantitative techniques. The paperconcludes by emphasizing that explicit models used in systems thinking provide new opportunities to understandand continuously test and revise our understanding of the nature of things, including how to intervene to improvepeople’s health.

Keywords: Complex adaptive systems, Complexity, Methods, Systems thinking, Theory, Tools

BackgroundIn the rapidly changing field of global health, it is hard toknow whether the recent attention to systems thinking isjust another fad, or something more durable that offersusable insights for understanding and action. Some seesystems thinking as providing a powerful language tocommunicate and investigate complex issues, while othersare confused by the sizable and amorphous body of theor-ies, methods, and tools involved. Time will tell, of course,but in the meantime, it is helpful to consider why wewould use systems thinking in a field that already drawsupon a rich collection of theories, methods, and toolsfrom the health sciences, social sciences, engineering,mathematics, and other disciplines.

From mental models to explicit onesAt its core, systems thinking is an enterprise aimed at see-ing how things are connected to each other within somenotion of a whole entity. We often make connectionswhen conducting and interpreting research, or in our pro-fessional practice when we make an intervention with an

Correspondence: [email protected] of International Health, Johns Hopkins University BloombergSchool of Public Health, Room E8527, 615 N Wolfe St, Baltimore, MD 21205,USA

© 2014 Peters; licensee BioMed Central Ltd. ThCommons Attribution License (http://creativecreproduction in any medium, provided the orDedication waiver (http://creativecommons.orunless otherwise stated.

expectation of a result. Anytime we talk about how someevent will turn out, whether the event is an epidemic, awar, or other social, biological, or physical process, we areinvoking some mental model about how things fit to-gether. However, rather than relying on implicit models,with hidden assumptions and no clear link to data, sys-tems thinking deploys explicit models, with assumptionslaid out that can be calibrated to data and repeated byothers. The word system is derived from the Greek sunis-tánai, meaning “to cause to stand together.” If we considerthat a system is a perceived whole, made up of parts thatinteract toward a common purpose, we recognize that theability to perceive, and the quality of that perception, isalso part of what causes a system to stand together. Sys-tems thinking is intended to improve the quality of thoseperceptions of the whole, its parts, and the interactionswithin and between levels.Every interpretation of a research result involves a

model, whether it is a physical model used for experimen-tation, a statistical model used to estimate the relation-ships between variables, or a conceptual model about howelements are connected. A model is simply a way we com-pactly represent and understand an object, phenomenon,or system. As much as research involves observation andexperimentation, I would argue that good research is also

is is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andiginal work is properly credited. The Creative Commons Public Domaing/publicdomain/zero/1.0/) applies to the data made available in this article,

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about building and using explicit models rather than im-plicit ones. The real question is not whether we should beusing systems thinking, as broadly described here, but ra-ther, which of the many theories, methods, and tools cur-rently associated with the field of systems thinking aremost useful in particular settings.For example, where individual people interact directly

with one another (e.g., transmitting disease) while movingabout in an explicit space such as a city, agent-based mod-eling [1,2] may be especially powerful. In modeling howdifferent agencies within a large public health systeminteract, social network theory [3] could be more directlyrelevant.

OriginsSystems thinking has largely developed as a field ofinquiry and practice in the 20th century, and has multipleorigins in disciplines as varied as biology, anthropology,physics, psychology, mathematics, management, and com-puter science. The term is associated with a wide varietyof scientists, including the biologist Ludwig von Berta-lanffy who developed General System Theory; psychiatristRoss Ashby and anthropologist Gregory Bateson who pio-neered the field of cybernetics; Jay Forrester, a computerengineer who launched the field of systems dynamics; sci-entists at the Santa Fe Institute, such as Noble LaureatesMurray Gell-Mann and Kenneth Arrow, who have helpeddefine complex adaptive systems [4]; and a wide variety ofmanagement thinkers, including Russell Ackoff, a pioneerin operations research, and Peter Senge, who has popular-ized the learning organization. Much of the work in sys-tems thinking has involved bringing together scientistsfrom many disciplinary traditions, in many cases allowingthem to transfer methods from one discipline to another(inter-disciplinarity), or to work across and between dis-ciplinary boundaries, creating learning through a wide var-iety of stakeholders, including researchers and thoseaffected by the research (trans-disciplinarity).

Theories, methods, and toolsIf there is a jungle of terminology used to describe scien-tific endeavor, it gets even thicker in the area of systemsthinking, perhaps because of its diverse heritage. Giventhe varied disciplines and trans-disciplinary traditions in-volved, it is easy to see why people often talk aboutbroader “approaches”, “perspectives”, or “lenses” when ap-plying systems thinking. Systems thinking models andframeworks are sometimes grand and widely applicable,such as General System Theory, and at other times veryspecifically applied to particular phenomena, such as thetheory on critical points in physics, which is used to ex-plain the point at which a material behaves as neither li-quid or gas (or solid). Systems thinking can involve a widerange of theories, which are rational sets of ideas or

principles intended to explain something. It is based on awide variety of scientific methods used to investigate phe-nomena and acquire knowledge. It uses an even largerarray of instruments or tools – the hardware and softwareused to conduct experiments, make observations, or col-lect and analyze data. The use of these terms is not con-sistent across or within scientific fields, including systemssciences, and the continuum from tool to method to the-ory and framework is often blurry.Rather than attempt to sort out semantic nuances be-

tween these terms, the utility of systems thinking can bebetter appreciated by a brief look at some of its more com-monly used theories, methods, and tools (Table 1). Thetheories and methods in systems thinking are each de-signed to address complex problems. They are complex be-cause they involve multiple interacting agents, the contextin which they operate keeps changing, because the mannerin which things change do not conform to linear or simplepatterns, or because elements within the system are able tolearn new things, sometimes creating new patterns as theyinteract over time. Many of the challenges in global healthare now recognized as complex problems where simpleblueprint approaches have limited success [5,6].Systems thinking tools have a wide variety of applica-

tions. Some tools are intended as means of facilitatinggroups of people to have a common understanding aboutan issue to prompt further inquiry and action. For example,“systems archetypes” help teams to understand generic pat-terns of interaction that can be applicable to their “story”[24]. Rather than use the pre-existing templates of systemsarchetypes, causal loop diagrams (CLD) are created with-out a template, and involve drawing out people’s under-standing of how elements of a problem are related to eachother [19]. They usually begin as qualitative descriptionsoutlining how one thing causes another in either a positiveor negative direction. Typically, feedback loops are identi-fied between the different elements. They can be reinfor-cing or positive feedback loops, where A produces more Bwhich in turn produces more A, such as the vicious cycleof under-nutrition and infection. They can also be balan-cing or negative feedback loops, where a positive change inone leads to a push back in the opposite direction, such aswhen increasing body temperature produces sweating,which in turn cools down the body. In this supplement, anumber of studies use CLDs that describe relationships be-tween different elements of a health system to explain phe-nomena such as dual practice of health workers in Uganda[25], provider payment systems in Ghana [26], and child-hood vaccination coverage in India [27].The elements of a CLD might also be converted into a

quantitative systems dynamics model by classifying theelements as “stocks”, “flows”, or “auxiliary” variables, andusing equations to describe the relationships between in-dividual variables in one of many available systems

Table 1 Systems thinking theories, methods, and tools

Name Purpose and description Key reference

Theories

Catastrophe theory A theory in mathematics and geometry to study how small changes in parameters of a non-linear system can lead to sudden and large changes in behavior of a system.

Poston & Stewart [7]

Cybernetics Historically used as a synonym for systems theory, it is a field of study of the communicationand control of regulatory feedback in both living and non-living systems (e.g., organizations,machines).

Ashby [8]

Chaos theory A field of study in mathematics with applications in a wide number of disciplines to explain adynamic system and that is highly sensitive to the initial conditions, so that small changes ininitial conditions produce wildly different results. The changes occur through fixed rules aboutchanging relationships, and without randomness.

Strogatz [9]

General systems theory Less of a theory than a way of finding a general theory to explain systems in all fields ofscience. It was not intended to be a single theory of systems, but more of a systematic inquiryinto different domains of philosophy, science, and technology.

van Bertalanffy [10]

Learning organizationstheory

A description of organizations that facilitate learning by its members and continuouslytransforms itself. Systems thinking approaches are the conceptual basis for understanding theorganization in its environment, and provides a basis for other key characteristics, namely aprocess of learning (personal mastery), the challenging and building of mental models, andthe development of a shared vision and team learning.

Senge [11]

Path dependency theories Occurs in economics, social sciences, and physics, and refers to the explanations for whyprocesses can have similar starting points yet lead to different outcomes, even if they followthe same rules, and outcomes are sensitive not only to initial conditions, but also tobifurcations and choices made along the way.

Arthur [12]

Punctuated equilibrium (insocial theory)

Theory inspired from evolutionary biology [13] to explain long periods of stasis interrupted byrapid and radical change, particularly as applied to the evolution of policy change or conflict.

Baumgartner &Jones [14]

Methods

Agent-based modeling(ABM)

ABMs are used to create a virtual representation of a complex system, modeling individualagents who interact with each other and the environment. Although the interactions arebased on simple, pre-defined rules, in a complex system these simulations allow for the identi-fication of emergence and self-organization.

Epstein [15]

Network Analysis (or SocialNetwork Analysis)

Network analysis uses graphical methods to demonstrate relations between objects.Grounded in computer science, it has applications in social, biological, and physical sciences.Social network analysis involves application of network theory to social entities (e.g., people,groups, organizations), demonstrating nodes (individual actors within a network), and ties (thetype of relationships) between the actors, and uses a range of tools for displaying thenetworks and analyzing the nature of the relationships.

Newman [3];Valente [16]

Scenario planning This is a strategic planning method that uses a series of tools to identify and analyze possiblefuture events and alternative possible outcomes. These can involve quantitative projectionsand/or qualitative judgments about alternatives. The value lies more in learning from theplanning process than the actual plans or scenarios.

Schoemaker [17]

Systems dynamics modeling Not a single method, but an approach that uses a set of tools to understand the behavior ofcomplex systems over time. The methods focus on the concepts of stocks and flows andfeedback loops. They are designed to solve the problem of simultaneity (mutual causation) bybeing able to change variables over small periods of time while allowing for feedback andvarious interactions and delays. The common tools include causal loop diagrams and stockand flow diagrams.

Forrester [18]

Tools

Causal loop diagrams (CLDs) CLDs are a system dynamics tool that produces qualitative illustrations of mental models,focused on highlighting causality and feedback loops. Feedback loops can be eitherreinforcing or balancing, and CLDs can help to explain the role of such loops within a givensystem. CLDs are often developed in a participatory approach. The drawings can be furtherdeveloped by categorizing the types of variables and quantifying the relationships betweenvariables to form a stock and flow diagram.

Williams &Hummelbrunner[19]

Innovation (or changemanagement) history

Innovation or change management history aims to generate knowledge about a system bycompiling a systematic history of key events, intended and unintended outcomes, andmeasures taken to address emergent issues. It involves in-depth interviews with as many keystakeholders as possible to build an understanding of the performance of the system from anumber of different points of view.

Douthwaite &Ashby [20]

Participatory ImpactPathways Analysis (PIPA)

PIPA is a workshop-based approach that combines impact pathway logic models and networkmapping through a process involving stakeholder engagement. PIPA workshops aim to help

Alvarez [21]

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Table 1 Systems thinking theories, methods, and tools (Continued)

participants to make their assumptions and underlying mental models about how projectsrun explicit and to reach consensus on how to achieve impact.

Process mapping A set of tools, such as flow charts, to provide a pictorial representation of a sequence ofactions and responses. Their use can be quite flexible, such as to make clear current processes,as a basis for identifying bottlenecks or inefficient steps, or to produce an ideal map of howthey would like them to be.

Damelio [22]

Stock and flow diagrams Stock and flow diagrams are quantitative system dynamics tools used for illustrating a systemthat can be used for model-based policy analysis in a simulated, dynamic environment. Stockand flow diagrams explicitly incorporate feedback to understand complex system behaviorand capture non-linear dynamics.

Sterman [23]

Systems archetypes Systems archetypes are a number of generic structures that describe common behaviorsbetween the parts of a system. They provide templates to demonstrate different types ofbalancing and reinforcing feedback loops, which can be used by teams to come to adiagnosis about how a system is working, and particularly about how performance changesover time.

Kim [24]

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dynamics software environments. In this supplement,Rwashana and colleagues use systems dynamics models toexamine neonatal mortality in Uganda [28], while otherauthors use systems dynamics models to examine the ef-fects of policy interventions [29].There are number of other tools that are used to map

out events or how things are connected. Network mapping,social network analyses, and process mapping involve arange of tools to illustrate and analyze connections betweenpeople, organizations, or processes in both qualitative andquantitative ways. In this supplement, Malik et al. map outthe network of actors involved in physician’s seeking advicein Pakistan [30]. The flow chart is one of the more com-mon tools used to draw a process or a system. Innovationhistory (or change management history) is used to compilea history of key events, outcomes, issues that have croppedup along the way, and measures taken to address problems.In this supplement, Zhang et al. [31] look back over the last35 years of the development of the medical system in ruralChina. Participatory Impact Pathways Analysis involvesworkshops and a combination of tools to clarify the logic ofinterventions and a mapping of the network [21]. It isintended to enhance understanding through participationwith beneficiaries, implementers, and other stakeholdersin a project. Several papers in this supplement use similarapproaches for a variety of situations, including to buildleadership capacity for health systems in South Africa[32], to develop sustainable physical rehabilitation pro-grams in Nepal and Somaliland [33], and to build sustain-able maternal and child preventive health services inNorthern Bangladesh [34].Agent based modeling takes advantage of a wide variety

of theories, methods, and tools to build computer modelsthat simulate the interaction of agents (e.g., individuals ororganizations) to see how real world phenomena “grow”and affect the system as a whole. The models involve mul-tiple individual agents that work at different scales, somedecision-making rules (e.g., simple rules on how they

reproduce, interact with others or pursue objectives), pro-cesses for adaptation, and a space in which the agentsoperate.In global health, we are concerned with both theory and

practice, and are in need of models that match the com-plex conditions in which we work. A common thread ofall these theories, methods, and tools is the idea that thebehavior of systems is governed by common principlesthat can be discovered and expressed. They are all helpfulin trying to conceptualize the systems in place. Some aremore focused on ways to change the system to producebetter outcomes. In using these theories, methods, andtools, we are reminded by the statistician George EP Boxthat “all models are wrong, but some are useful” [35]. It isto these uses that we now turn.In much of public health and medicine, we use research

evidence on the efficacy of interventions to inform deci-sions with an expectation about their future effect. Somesystems thinking methods and tools, such as scenarioplanning, can also be used to explicitly forecast futureevents. However, even then, such methods are intended tobe used for identifying possible outcomes to provide in-sights on how to prepare for them rather than fixing onany particular outcome.In his landmark address on “Why Model?”, which pro-

vided inspiration for this essay, Joshua Epstein identified16 reasons other than prediction on why to model [36].Most of these reasons are applicable to systems thinkingmore broadly. Many of these specific reasons relate to be-ing able to explain how things work, and systems thinkingis particularly useful to explaining how complex systemswork. Many of models can be used for testing the viabilityof policy interventions in a safe and inexpensive way –agent based models, systems dynamics models, and sce-nario planning are particularly useful for these purposes.In this journal supplement, for example, Bishai et al.present a very simple systems dynamics model to illustratethe trade-offs and unintended consequences of policy

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Published: 26

choices related to allocation to preventive and curativeservices [29].Systems thinking approaches can also provide guidance

on where to collect more data, or to raise new questionsand hypotheses. The methods and tools help us to makeexplicit our assumptions, identify and test hypotheses, andcalibrate our models against real data. One of the frustra-tions of health planners and researchers has been the as-piration that interventions shown to be effective at smallscale or in a research setting cannot be simply replicatedat large scale or to reach populations that are most vulner-able. Systems thinking methods and tools are increasinglybeing used to explain epidemics and to inform program-matic expansion efforts [5,6].One of the more compelling reasons to use systems

thinking approaches is to inspire a scientific habit of mind.Beyond the contributions of any particular theory,method, or tool, the practice of systems thinking canreinforce what Epstein calls a “militant ignorance”, orcommitment to the principle that “I don’t know” as a basisfor expanding scientific knowledge. Systems thinking addsto the theories methods and tools we otherwise use in glo-bal health, and provides new opportunities to understandand continuously test and revise our understanding of thenature of things, including how to intervene to improvepeople’s health. And for those who value thinking anddoing in global health, that can only be a good thing.

Competing interestsThe author declares that he has no competing interests.

AcknowledgementsThis Commentary is part of the Thematic Series entitled: “Advancing theapplication of systems thinking in health”. The Series was coordinated by theAlliance for Health Policy and Systems research, World Health Organizationwith the aid of a grant from the International Development Research Centre,Ottawa, Canada. The author also gratefully acknowledges support from theFuture Health Systems Research Programme Consortium through a grantprovided from the Department for International Development (UnitedKingdom). I also appreciate the comments received from Josh Epstein.

Received: 23 May 2014 Accepted: 18 August 2014

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doi:10.1186/1478-4505-12-51Cite this article as: Peters: The application of systems thinking in health:why use systems thinking? Health Research Policy and Systems 2014 12:51.

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