Practicing Positive Psychiatry & Positive CBT

Complex is not (the same as) difficult (1)

Pieter Jansen and Fredrike Bannink


The solution-focused model


We often see unpredictability and complexity as difficult or tough. We prefer to control and understand things. This also applies to health care. Having control and understanding is, however, not always possible. How do we act in case of uncertainty?
In our opinion, there is (too) little attention for this topic. We advocate the use of the solution-focused model, an amazingly practical way of working. And we connect this method with science that deals explicitly with unpredictability. System theory and information theory are fascinating fields and have a lot to offer. In other domains (artificial intelligence and computer science), these branches of science are self-evident.
It is about time to add a bit more complexity to our profession.


You get into a taxi and the taxi driver asks: “Where do you want to go?”. He does not ask: “What do you want to leave behind?” Or “Where do you come from?”. This is the core of the solution-focused approach.

In the 1980s, a group of psychotherapists in the US, including De Shazer and Berg, were dissatisfied with the results of their approach. They filmed their sessions and saw that talking about problems increased the focus on these problems. With this, the patients got further away from the solution. They noticed that ‘problem talk’ creates problems; whereas ‘solution talk’ creates solutions.

They opted for a radically different approach. They chose the preferred outcome as a starting point and laid the foundation for Solution-Focused Brief Therapy (SFBT). It is a fundamentally different approach than the conventional reductionist medical model, in which practitioners first examine the problem, make a diagnosis and determine the treatment accordingly. In the solution-focused functional approach, examination and diagnosis are often not necessary. SFBT does not ask ‘why’, but asks ‘how’.
Once the preferred outcome is described in detail, practitioners and patients find out how far the patient has already progressed and what the next signs of progress might look like. The strengths, possibilities and resources of the patients are optimally used. This approach has been elaborated and is applied in a much wider context.
In addition to the use in psychotherapy, it is used in health care and in education, mediation, coaching, management, leadership and sports. A more detailed description can be found in our book   .  Positieve Gezondheidszorg. Oplossingsgericht werken in de huisartsenpraktijk [Positive Health Care. The solution-focused model in primary health care].


The solution-focused model is developed by practitioners based on experiences in their work: it is a pragmatic approach. There are many striking parallels with system theory and information theory. Here too, the starting point is how a road to the preferred outcome can be constructed without prior analysis of the past. Unpredictability is not a problem, but a factor that can be worked with. In a few short pieces with the same title but a different number, we will explore these analogies.


Complex is not (the same as) difficult (2)

Pieter Jansen and Fredrike Bannink


Linear and nonlinear systems
In the medical domain research is – not surprisingly –  mainly analytical in nature. In our modern western society, the dominant paradigm is analysis. And this is a very successful approach. But there also seem to be limits to analysis. How do we deal with issues that cannot be properly analyzed? How do we deal with unpredictability?


A 65 kg gymnast jumps down 1 meter. Calculate the forces that his knees must transfer when landing. The same gymnast makes a jump of 2 meters. Recalculate the forces. Then, with 10 meters. He is likely to be injured.

A patient with heart failure shows an improvement of his cardiac output at a dose of 0.5 mg digoxin per day. Will the improvement be 2x as large with 1 mg digoxin? At 10 mg per day there is a serious risk of death. What is 2x grief? Or 3x anger?

The examples above show some limitations of applying linearity to the medical domain. What is linearity? In science and technology, two basic systems are distinguished: linear and nonlinear systems.

  • A linear system has two essential properties: homogeneity and additivity. Similar elements are exactly the same and interchangeable. You can disassemble elements and reassemble them; you can multiply elements and divide them again. The result is always predictable. We only know pure linear systems as theoretical models. Linear models are pleasant for research. They are intuitive, work according to the cause-and-effect principle, are convenient and simple. The world as a clockwork.
  • Other systems are nonlinear. The real world is nonlinear. In the real world, building blocks are not the same. There is always variation. Small irregularities explain the phenomenon that interactions can lead to unpredictable results. The butterfly effect is a well-known metaphor for this unpredictability.


There are conditions that are uniform and stable where a linear approach is feasible. In a more irregular environment one should be careful with interpreting results from research; and even more cautious with extrapolating the outcomes to a situation that is slightly different. What do you know about patients, for example, when they have filled out questionnaires about feelings and thoughts? And can you use that information for situations that are slightly different?

The good news is that there is a simple approach for unpredictable topics: the solution-focused model.

The Complexity Academy has made a series of short videos about linear and nonlinear systems.
They can be found at:

Videos about chaos theory can be found at:



Complex is not (the same as) difficult (3)

Pieter Jansen and Fredrike Bannink



Change is happening all the time, stability is an illusion. Complexity is unavoidable, so we better take it seriously. In the medical profession, however, it is not yet common practice to work with complexity. It takes courage to leave the beaten path, but turns out to generate good results and is (even more) fun to practice.


The butterfly effect: A wingbeat of a butterfly in Brazil can lead to a tornado (or nice weather) in Texas months later.

An everyday example: I left home a little late and missed the train. And then…….

The metaphor of the butterfly shows how a small change in the starting position can cause major differences in a later stage. Especially when there are many factors with many mutual interactions.
In modern science, two important paradigms can be distinguished: the analysis paradigm and the synthesis paradigm.

  • The analysis paradigm uses reductionism as a working method. We try to understand a system by dividing it into constituent elements. These elements are examined in isolation and the system is seen as a sum of the elements. However, this requires linearity. But pure linearity only exists as a theoretical model, a simplification of reality. Small variations and the truly innumerable interactions between all elements make the real world a nonlinear affair. When using this paradigm, it is assumed that if one analyzes long enough with ever stronger microscopes and more powerful computers, eventually everything will be known. This is the dominant paradigm in contemporary science.
  • In addition to analysis, science distinguishes the synthesis paradigm. Here, one does not see the subject as the sum of its components, but one looks at how the parts interrelate to form a whole. It focuses on the dynamics between the parts. It concerns complex systems that are characterized by these interrelationships. In Wittgenstein’s words: “Do not ask for the meaning; ask for the use.” Not the why is important, but the how, the function.


A characteristic of a longer existing paradigm is that it is no longer consciously experienced. Education makes a paradigm natural, making it more difficult to think outside this framework. Reductionism is important because it has yielded great successes in science and technology. The tremendous changes of the past centuries in our daily lives and also in medicine are largely due to reductionistic research. It certainly has brought us a lot.

But what is the alternative when reductionism does not appear to be suitable for highly complex topics? In those cases we advocate the use of the synthesis paradigm.

Complex is not (the same as) difficult (4)

Pieter Jansen and Fredrike Bannink



The whole is greater than the sum of its parts.

In complex systems, unpredictability is not the same as randomness. Often more or less clear patterns or forms of organization can be recognized. In these cases it is tempting to look for causality. But is this the best way to move forward?


Mr. A sees a psychologist. He fills out a questionnaire, showing that his complaints meet the DSM-5 criteria for depression. The pattern is recognizable, but is the course of psychotherapy hence predictable? What are possible treatments? Does this mean the cause-and-effect model of psychotherapy needs to be installed here?

A small difference in an initial state can have big consequences at a later stage. Unfortunately, diagnoses do not provide predictability. There is no straight line between cause and effect.
Let’s look at it from a different angle. How important is the information from questionnaires from the perspective of the complexity model? After all, as in thermodynamics, previous states do not inform us about future developments, not even when patterns can be recognized. In a complex setting people construct their preferred outcome by using real-time opportunities.


Emergence shows the effect of countless interactions within complex systems. The appearance of new properties in a system, due to large amounts of interactions between smaller elements in that system, is called emergence, when the smaller elements do not show these properties.

An example of an emergent property is color. Individual atoms have no color. An arrangement of large numbers of atoms causes a color to develop when light is absorbed and reflected. An ant colony is another example. Based on the behavior of one ant one cannot derive how the behavior of an entire ant colony is organized. Temperature, fluidity, pressure, culture, language and health are all examples of emergent properties.

The concept of emergence is different from the concept of causality because it is not suitable for scientific (analytical) experiments, where usually one cause-and-effect relationship is central. With emergence, there is an infinite or very large number of causes that (can) occur together at the same time.


In the end, probably all phenomena are emergent. Verlinde (2017) even tries to prove that gravity is not a fixed and universal value, but an emergent property, a consequence of interactions. This is of course of no importance for everyday life. It is important to know that there is weak and strong emergence. The analysis paradigm may be used for weak emergent topics. A functional approach (the synthesis paradigm) works better in strong emergent topics.

Still much territory for synthesis remains unexplored. Fortunately, there is a functional working method, the solution-focused model, which has already proven its value in practice.


Verlinde, E. (2017). Emergent gravity and the dark universe. SciPost Physics, 2, 016 (2017)

Complex is not (the same as) difficult (5)

Pieter Jansen and Fredrike Bannink


Causality, correlations, and averages.

We find causality convenient. Many athletes perform certain rituals before a game which they also performed before a previous victory. Superstition? Some people stop using a certain product after they have had an unpleasant experience (abdominal pain or skin irritation) with that product. Coincidence?


Okay, now it gets trickier. Scientific studies show that running therapy has a beneficial effect on recovery from depression. Causal relation or correlation? Remember there are also people who suffer from depression who do not benefit from running therapy. High blood pressure, smoking, hypercholesterolemia and obesity are often referred to as causes of cardiovascular disease. They are, of course, risk factors.
Smeets gives, in a wonderful TED talk about correlations, an example of research from the seventies in the US showing that children who performed well in school also felt very confident. This study received a lot of attention and for decades parents worked on the self-confidence of their children, because high self-esteem would lead to good school performances. From research many years later, it turned out to be the exact reverse: children who did well in school became more confident.


Complexity is fascinating, however, we find causality convenient. We are prone to construct causal relations when there is merely coincidence or correlation. Kahneman (2011)  did a lot of research on ‘biases’ (systematic errors or intuitive prejudice) in our thinking. In 2002 he received the Nobel Prize for Economics for his work.


The medical model (e.g., examination, diagnosis, treatment) is an example of the cause-and-effect model. It is based on causality. The model has proven to be useful in our medical profession. Treatment options often refer to scientific research. This research should have (sufficiently) large numbers in the research population, given the variation in the components to be studied. If we want to draw conclusions from these studies we have to make use of averages. Compare this to the bed of Procrustes.


In Greek mythology Procrustes was an innkeeper. He ensured his guests fit perfectly into their beds by either stretching or chopping off their limbs.


In order to perform reductionist research on an irregular issue, we use an artifice. The irregular elements are converted into averages. We replace individual goals by norms. And the process must follow guidelines, directives or blueprints (the average approach). This does not do justice to variation. A functional approach, such as the solution-focused model (see Complex 4 and 6), does.


The average Dutch male is 182.5 cm tall; the average Dutch female is 168 cm.
Does that make a Dutch person of 182.5 cm a male?


Kahneman, D. (2011). Thinking, fast and slow, New York: Farrar, Straus and Giroux.

Smeets (2012), TED-talk via


Complex is not (the same as) difficult (6)

Pieter Jansen and Fredrike Bannink


Positive health.

The World Health Organization (WHO) still uses the definition for health as formulated in 1948, “Health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” This definition describes a state, a situation at a certain moment. It is a static view on health. Moreover, for many of us this ‘ideal’ state is practically unattainable.


In 2011, Huber and colleagues came up with a more dynamic concept: “Health as the ability to adapt and self-manage, in light of the physical, emotional and social challenges of life.” They called their concept Positive Health.

Health is no longer a state, but an ability, a power or competence. The importance of this concept is that it makes it possible for people to live a meaningful life – even in the presence of disease. In our book Positieve Gezondheidszorg [Positive Health Care], we write extensively on this subject.


Positive health does justice to the many factors that influence finding our way through life. In our opinion, these are not only physical, emotional and social factors, but also genetic, economic and environmental factors. The nature of these influences and the way they interact is constantly changing.

Therefore our health is always changing. Could that be the reason why we often ask about each other’s health? “How are you doing?” This changeability complicates working with results from research, because while doing research or an examination, the subject has already changed. When we take a picture, it is already outdated a moment later.


Positive health describes health as a complex system. There is an infinite number of causes and consequences that can no longer be unraveled. Health is an emergent property (see Complex (5)). Causality can only be recognized in small areas and/or relatively short duration.
From this perspective, the analysis paradigm falls short in working with health; in addition we need a functional approach. “Don’t ask for the meaning; ask for the use” (conform Wittgenstein*). A functional approach is not bothered by irregularity and unpredictability, but instead makes use of it.


When health is seen as an ability or competence, a competency-focused approach is indicated. A good example of such an approach is the solution-focused model (Bannink, 2013, 2015). It is a pragmatic model about finding what works for this patient, at this moment, in this context.



Bannink. F.P. (2013). 1001 Solution-focused questions. Handbook for solution-focused interviewing. New York: Norton.

Bannink, F.P. (2015). Book series: 101 Solution-focused questions for help with 1. Anxiety, 2. Depression, 3. Trauma. New York: Norton.

Bannink, F.P. & Jansen, P. (2017). Positieve gezondheidszorg. Oplossingsgericht werken in de huisartsenpraktijk [Positive health care. The solution-focused model in primary health care]. Amsterdam: Pearson.

Huber, M., Knottnerus, J.A., Green, L., Horst, H. van der, Jadad, A.R. et al. (2011). How should we define health? British Medical Journal,  343, d4163.


*This slogan is widely attributed to the later Wittgenstein – “Don’t ask for the meaning; ask for the use.” Although it was not a formulation actually employed by Wittgenstein himself,  there is some consensus that this was Wittgenstein’s point, in Philosophical Investigations.



Complex is not (the same as) difficult (7)

Pieter Jansen and Fredrike Bannink


Information theory.

Information theory makes complexity quantifiable and mathematical. This theory was developed by Shannon (1948) for information technology, but may be applied to all complex systems. It is also suitable when working with biological processes and to understand how we think. Information theory is all about organizing information and managing uncertainty. In its essence, it is surprisingly simple.


The bit is the smallest unit of information.

But what does the bit measure? A bit can only have two values—yes or no; on or off. Information arises when an event takes place, for which it was uncertain beforehand whether it would happen. In essence everything is information. We can describe biology as information theory. We can describe our body as an information processor. Information can not only be found in the instructions of genes, and memory can not only be found in the brain. Both are in all (parts of) our cells.
Dawkins (1986), an evolutionary biologist, states, “If you want to understand life, you have to think about information technology”. Today there is no difference between physics and information theory anymore. Information is more fundamental than matter; matter results from information. “It from bit”, states Wheeler (1994), a theoretical physicist.


Like in thermodynamics, information theory uses the concept of entropy – the amount of uncertainty or disorder in a given system. Spontaneous structures arise in dynamic systems and when energy is added, self-organization arises. Biological organisms have a high degree of self-organization. They interact with their environment and face an ongoing challenge to keep the internal entropy limited. After all, the second law of thermodynamics states that entropy in an isolated system will tend to increase over time.
Hirsh, Mar and Peterson (2012, p.304) developed the entropy model for uncertainty for biological processes.


‘We propose the entropy model of uncertainty (EMU), an integrative theoretical framework that applies the idea of entropy to the human information system to understand uncertainty-related anxiety. Four major tenets of EMU are proposed:
(a) Uncertainty poses a critical adaptive challenge for an organism, so individuals are motivated to keep it at a manageable level;
(b) uncertainty emerges as a function of the conflict between competing perceptual and behavioral affordances;
(c) adopting clear goals and belief structures helps to constrain the experience of uncertainty by reducing the spread of competing affordances;
(d) uncertainty is experienced subjectively as anxiety and is associated with activity in the anterior cingulate cortex and with heightened noradrenaline release.‘


In other words, all organisms have certain possibilities for action. There is a constant flow of information that must be weighed against these possibilities. Many options signify a high level of uncertainty – a high entropy. Our brain ‘measures’ the activity of these options and functions as an entropy meter. A goal or belief structure limits the number of options, so is helpful in reducing entropy. Finding more or better goals, beliefs and values leads to a greater reduction of entropy and gives direction to our behavior.


In the cause-and-effect model, statistics are used to make certain issues uniform and suitable for causality. Information theory uses mathematics of the probability theory to calculate uncertainty. These calculations determine the direction to achieve a manageable level of entropy, over and over again. This has proven useful when faced with a complex and dynamic setting.
Note that deep learning and data mining use information theory to find patterns. And subsequently these patterns are often used – in a reductionist way – as a subject in a cause-and-effect model. So this is also a way to deal with uncertainty for analytical purposes.



Dawkins, R. (1986). The blind watchmaker. New York: Norton, p.112.

Wheeler, J.A. (1994). At home in the universe. New York: American Institute of Physics, p.296.

Hirsh, J.B., Mar, R.A., & Peterson, J.B. (2012). Psychological entropy: A framework for understanding uncertainty-related anxiety, Psychology Review, 119, 2, p.304-320.

Complex is not (the same as) difficult (8)

Pieter Jansen and Fredrike Bannink


Creative processes.

A recent development in theories about psychological processes is the honing theory (Gabora, 2016). She describes creative processes from the perspective of complex systems. Systems theory, and especially complexity theory, sees us humans and our brains as self-organizing systems.
Gabora refers to a publication by Hirsh, Mar and Peterson (2012, see Complex 7), who developed a model using entropy as a description and measure of psychological uncertainty.


The term entropy refers to disorder or uncertainty. In the information theory, entropy is a measure of information density. Self-organizing systems need to restrict entropy, and in a psychological sense this means that we need to limit uncertainty. We experience uncertainty either as anxiety or – more generally – as arousal.


We cannot store information in our brain in the same way we store books in a library. We simply do not have enough neurons; instead we store information in neural networks.
We constantly add new information and these neural networks must constantly be rearranged. Because of this complexity, the storage and rearrangement cannot be explored in a reductionist way. Here we need information theory, because rearranging happens by aiming for the lowest possible entropy.


Gabora describes creative processes as a constant process of exchange, comparison and reorganization of information, all with the aim of minimizing entropy. We experience high entropy as arousal, either in a negative sense as fear or cognitive dissonance, or in a positive sense as curiosity or inspiration. Low entropy corresponds to peace, stability and harmony.


The solution-focused model (Bannink, 2013; 2015) provides a good way to stimulate creative processes. Practitioners ensure an encouraging context for change and invite patients to see different perspectives by asking solution-focused questions. Patients may compare, combine and reorganize the information from these new points of view. This way they construct new ideas and possibilities. When the spontaneous creative process gets stuck, the solution-focused approach may help to get it going again. It is easy to notice parallels between the honing theory and the solution-focused approach. However, further research into these parallels is necessary.


The problem-focused – medical – approach is an analytical process. Practitioners look for the error (disease), and treatment focuses on repairing the error. It requires convergent thinking in search for the one correct answer. The solution-focused approach supports a creative process (together with our patients). It is about designing, constructing and achieving a new and better life with (new) possibilities. This requires divergent thinking— a constant exploration of possible options, which may then be (partly) realized.



Bannink. F.P. (2013). 1001 Solution-focused questions. Handbook for solution-focused interviewing. New York: Norton.

Bannink, F.P. (2015). Book series: 101 Solution-focused questions for help with 1. Anxiety, 2. Depression, 3. Trauma. New York: Norton.
Gabora, L. (2016). Honing theory: A complex systems framework for creativity. Cornell University Library.

Hirsh, J.B., Mar, R.A., & Peterson, J.B. (2012). Psychological entropy: A framework for understanding uncertainty-related anxiety. Psychology Review, 119, 304-320.


There is also criticism of the concept ‘Positive Health’


In the special issue 04 about ‘Positive Health’ in the Dutch Tijdschrift Positieve Psychologie [Journal of Positive Psychology], November 2017, a critical article by Van Staa, Cardol and Van Dam is included. Under the title, ‘Not new, unclear, misleading and not without risk’ the authors give an overview of their objections to the concept of ‘positive health’ of Huber et al. (2016) based on three themes: criticism regarding (I) the conceptual and methodological level,(II) the practical implementation and application, and (III) a warning against the possible consequences. They call for a debate.

It is always good to debate. This often clarifies viewpoints and can lead to progress around new ideas. We are pleased to contribute to this debate. Besides, in addition to enthusiasm, we also hear criticism as described in the article by Van Staa and colleagues. The concept of positive health, or the presentation of it, is apparently unclear.
We agree with Van Staa and colleagues about the confusion. But we come to a different conclusion about the significance of the concept of positive health. It does not go too far for us: we see it as an important step in the right direction in thinking about health. For us it is actually not far enough. Because when the vision of health as a complex dynamic system is followed and carried through to all consequences, it becomes easier to talk about.

(I) Conceptually unclear
In the concept of Huber and colleagues health is described as the “ability to adapt and selfmanage.” This makes it a complex and dynamic concept. This is a big benefit. But they also divide positive health in a reductionist way into six dimensions: bodily functions, mental functions and the mental perception, spiritual/existential dimension, quality of life, social and societal participation and daily functioning.
This is confusing. Complexity and reductionism do not go well together. The confusion makes it difficult to argue about matters that Van Staa et al. mention in their article such as, ‘classification’, ‘where is the boundary between illness and health?’, ‘where are the objectifiable conditions or the disorder […]?‘ and ‘cause and effect are mixed up’. These are topics that fit in with a reductionist approach, but not with complexity. The separation of the aspects of a complex system and the desire to proceed reductionistically can resolve this confusion. Health as a whole is complex. This does not exclude, however, that research is possible in sub-areas, with sometimes clear results. Attention to both viewpoints is clarifying.

(II) Practical implementation
Ambiguity about the practical implementation leads to criticism. For example, van Staa and colleagues argue, ‘[..] is part of a neoliberal ideology of cost control and engineerable society,responsibility and self-reliance, reduction of claims to collective resources, and community involvement’, ‘victim blaming’, ‘obligation to work on your health, taking responsibility for your health behavior and to be positive’, ‘the compulsion to no longer think in terms of limitations, but in possibilities’ and ‘resistance […] to such use of veiling language and the constant emphasis on opportunities and possibilities without the recognition of misery, loss or inherent vulnerability’. All common misunderstandings.
In our opinion, this is the consequence of the lack of a clear practical approach. We are lucky to have a great deal of experience with the solution-focused approach, a functional model that arose in psychotherapy in the 1980s. It has grown into a fully-developed model that is ideally suited for complex issues where a reductionistic approach is not necessary, undesirable or simply does not work.
It is about finding what works for this patient, in this context, at this moment. It requires a collaboration where it is the task of the professional to invite patients to think differently, to describe their desired future, to notice positive differences and to make progress. This has nothing to do with neoliberal ideology, nor with victim blaming, but it has everything to do with autonomy, competence and relatedness (Ryan & Deci, 1987). After all, patients are experts of their own life and context. In our opinion, complex topics require an approach that is specifically designed for this. The solution-focused model is a useful example of this.

(III) Warning
Another criticism is that the concept of Huber et al. ‘does not distinguish between good or healthy adaptive forms and adaptation mechanisms that we generally do not find proper (such as accepting domestic violence or smoking to handle stress)’. We propose to look at it as follows: A professional and patient who use the concept of positive health have the same knowledge and research results as those who are using the conventional concept. How do Van Staa and colleagues see a difference here, if a distinction between right and wrong, or healthy and unhealthy would be made? How will professionals and patients act differently when this distinction is made?

Is the concept of Huber and colleagues too broad? Does the conversation tool My Positive Health (MPH) lead to the search for happiness or ‘the good life’ instead of to healthiness? We do not know. What we do know is that the solution-focused model does not use the MPH model, but uses open-ended questions about how people like their lives to be (different) and what works in their lives. Moreover, solution-focused questions motivate people to take action. From the extensive solution-focused literature we do not come across the topic ‘too broad’.
However, it is known from the literature within psychotherapy that the solution-focused approach leads to shorter treatments than traditional forms of psychotherapy, the autonomy of patients is guaranteed and there is less burnout among healthcare providers (Franklin et al., 2012, Stams et al., 2006, Medina & Beyebach, 2014). We are not worried by the risk, stated by Van Staa and colleagues, that governments, sports clubs or churches will also engage in the health domain.

The solution-focused model (Bannink, 2010; 2015) can be very useful as a working method for (positive) health. In addition, we should consider how we can combine this model with the regular medical model. We call this combination “Positive Healthcare”.

With our concept of Positive Healthcare we use the following assumptions:
(1) Health is a complex (non-linear) dynamic system.
(2) In sub-areas within the health system, there are relatively clear and stable processes where cause and effect can be recognized, a reductionist approach is possible and results from research make a difference.
(3) There are two fundamentally different ways to approach issues. In terms of philosophy of science, we distinguish the analysis paradigm and the synthesis paradigm.
(4) Within the health domain, both paradigms are needed.
(5) Separate and yet combinable working methods are needed for both paradigms.

The solution-focused functional model can be easily combined with the reductionist medical model. A solution-focused approach uses everything that works in the life of the patient.Results from research are seen as good examples and not as guidelines or protocols.To represent what the integration of the medical and solution-focused model might look like, we use ‘the entropy model of uncertainty’ (EMU) by Hirsh, Mar and Peterson (2012). EMU is based on the information theory and thus designed to make choices or to choose a direction in a complex dynamic environment. A dynamic environment is constantly changing and directions must change accordingly. How are directions determined? The entropy model assumes that an organism, like any self-organizing system, has an interest in constraining entropy (also interpreted as ‘uncertainty’ or ‘disorder’). Hirsh and colleagues argue that goals and belief structures reduce the spread of affordances and help to constrain entropy. We state that research results are also part of these ‘belief structures’ and also limit the number of options. Research evidence that is strong and fits the context of the person can greatly reduce the number of choices— the lowest entropy determines the direction. And because the circumstances are constantly changing, this is a continuous loop.

What is sufficient evidence? An interesting development is the ongoing discussion about sharpening the p-value threshold to 0.5 percent, instead of the traditional 5 percent (Benjamin, 2017). This makes the need for an alternative to the reductionistic approach much greater.

For doctors who already work with a combination of the medical and the solution-focused model, the balance varies per consultation. In the case of psychological conditions, a consultation can be (almost) completely solution-focused. Sometimes it is restricted to a number of solution-focused questions such as, “What have you already done against the pain that helped, even just a little bit?”
In between these two models, perhaps the most important question is, “How do you interpret results from research (with averages) to the individual patient?” We realize that more research and practical experience is needed. In our opinion, however, there are good reasons to continue with the development of opportunities for positive health (and positive health care).


Bannink, F.P. (2010). 1001 Solution-focused questions. Handbook for solution-focused interviewing. New York: Norton.

Bannink, F.P. (2015). Book series: 101 Solution-focused questions for help with 1. Anxiety, 2. Depression, 3. Trauma. New York: Norton.

Benjamin, D.J. (2017). Redefine statistical significance. Nature Human Behavior, 01 September 2017

Deci, E. L., & Ryan, R. M. (1987). The support of autonomy and the control of behavior. Journal of Personality and Social Psychology, 53, 6, 1024-1037.

Franklin, C., Trepper, T.S., Gingerich, W.J. & McCollum, E.E. (2012). Solution-focused brief therapy. A handbook of evidence based practice. New York: Oxford University Press.

Hirsh, J.B., Mar, R.A., & Peterson, J.B. (2012). Psychological entropy: A framework for understanding uncertainty-related anxiety. Psychology Review, 119, 2, 304-320.

Medina, A. & Beyebach, M. (2014). The impact of solution-focused training on professionals’  beliefs, practices and burn-out of child protection workers in Tenerife Island. Child Care in Practice, 20, 1, 7-36.

Stams, G.J., Dekovic, M., Buist, K. & Vries, L. de (20016). Effectiviteit van oplossingsgerichte korte therapie: Een meta-analyse. Gedragstherapie, 39, 2, 81-94.