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CHAPTER 4
about problem
Monitor
progres
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Problem Identification and Management
Nicola Cooper and John Frain
OVERVIEW
• Clinical reasoning involves problem recognition, definition, and
representation
The use of specific language helps clinicians match the patient’s
•
own words and data with illness scripts in long-term memory
• Sometimes problem representation is not possible, in which case
a problem list can be used
• It is still possible to act safely and confidently when dealing with
uncertainty
• Teachers have an essential function in role-modelling how to deal
with uncertainty
• Management reasoning differs significantly from diagnostic
reasoning in several ways
Introduction
Clinical reasoning is a complex cognitive process involving
clinical skills, memory, problem-solving, and decision-making.
In this chapter, we focus on problem-solving, what psychologists
have learned about it, and the factors that contribute to its success
or failure.
moving to a new town is ill-defined. Ill-defined problems can
have more than one ‘correct’ solution and require different skills
to solve them. Most problems in medicine are ill-defined.
Recognising there is a problem in the first place is the first step
in problem-solving. Problems can be presented (as in case histories on paper) or discovered (as when a problem is teased out
through a careful history). The problem then has to be defined
and represented before it can be solved. Problem representation
refers to how the problem is mentally organised before attempting
to solve it. In psychology, this consists of a description of the
problem, a description of the goal, a set of allowable operators,
and a set of constraints [1]. These are held in memory while we
try to solve the problem. Problem representations can be created
using abstractions (a summary of the problem’s essential characteristics using words), images, diagrams, or equations [2]. The
important thing to note is that the representation of a problem
affects the solution.
Evaluate
solution
Recognise
problem
Problem
identification
The Problem-solving Cycle
Psychologists have described problem-solving in terms of a cycle,
illustrated in Figure 4.1. Not all problem-solving proceeds
sequentially through all stages in this particular order. However,
once the relevant steps are completed, they usually give rise to a
new problem and then the steps need to be repeated. We have
used the term ‘problem identification’ to refer to the first two
steps which involve problem recognition, definition, and
representation.
There are two classes of problems: those that are well-defined
and those that are ill-defined. A simple maths problem is welldefined. How to decide which house to buy, or rent, after
ABC of Clinical Reasoning, Second Edition. Edited by Nicola Cooper and John Frain.
© 2023 John Wiley & Sons Ltd. Published 2023 by John Wiley & Sons Ltd.
s
Allocate
mental/physical
resources
Organise one‘s
knowledge
Define and represent
problem
Develop solution
strategy
Figure 4.1 The problem-solving cycle. Adapted from Pretz et al., 2003.
24 ABC of Clinical Reasoning
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Problem Representation in Medicine
In medicine, the problem representation is a key step in clinical
reasoning. It usually consists of an abstraction in one or two sentences. Sometimes it is referred to as the ‘impression’ (i.e., what
we think is going on). In the problem representation, clinical
findings are transformed into abstractions using ‘semantic qualifiers’. These are abstract binary descriptors such as acute/chronic,
unilateral/bilateral etc. (see Box 4.1). An example of a problem
representation would be, ‘A 60-year-old man with acute, recurrent attacks of severe left knee pain.’ As this example illustrates,
the problem representation is not the same as the presenting complaint, and it is not a summary of the history and examination
findings either. It is an encapsulation of the key features of the case
using very precise medical language. Patients do not come in
talking this way – clinicians have to transform their findings into
more abstract terms in order to define the type of problem or represent the problem overall (in this example, an episodic monoarthritis as opposed to a chronic poly-arthritis – this distinction is
important when thinking about potential diagnoses) [2].
Why does the precise language described above matter?
Language and memory have historically been studied apart as
unique cognitive abilities and with distinct research traditions
and methods. Over the past several decades, however, a growing
body of evidence suggests that language and memory are heavily
intertwined and may even rely on shared cognitive and neural
mechanisms [3]. The development and refinement of a problem
representation is a critical step that allows clinicians to match the
patient’s words and data with illness scripts (i.e., organised mental
summaries of different diseases) in their long-term memory and
thus start going about solving the problem [4]. Successful diagnosticians, whether students or specialists, elaborate using
semantic qualifiers more than unsuccessful ones when representing problems. They are also able to encapsulate a set of symptoms
and signs into clinical syndromes whenever possible [5]. For
example, a confusing array of neurological symptoms becomes, ‘A
3-day history of progressive, bilateral cerebellar symptoms.’
Symptoms such as polyuria and polydipsia are not seen as separate symptoms, but as a clinical syndrome. This immediately
helps to narrow down potential diagnoses and therefore what
tests may be required. Box 4.2 illustrates an example of how
problem representation, or lack thereof, affects the solution, and
Box 4.1 Examples of semantic qualifiers
• Acute/chronic
• Unilateral/bilateral
• Mono/poly
• Progressive/intermittent
• Sharp/dull
• Proximal/distal
• Sudden/gradual
• Single/recurrent
• Productive/non-productive
• Severe/mild
Semantic qualifiers are paired, opposing descriptors that can be
used to compare and contrast diagnostic considerations.
Box 4.3 illustrates an example of how language matters in problem
representation.
Generating an accurate problem representation is something
that is neglected in ‘history–examination–differential diagnosis’
teaching methods, but problem representation really matters.
Studies show the main difference in the discourse of ‘strong’ as
opposed to ‘weak’ diagnosticians is their semantic competence,
that is their use of language to organise their thinking [5]. This
becomes especially important when the case is complex. For
example, an elaborated, encapsulated structure, as described
above, is associated with 75–80% accuracy in resolving complex
problems as opposed to near zero resolution for ‘dispersed’ discourses. Importantly, learners can be taught to solve a problem by
defining and representing it first before blindly generating a series
of diagnostic impressions [6].
An example of a problem representation in need of improvement is ‘A 50-year-old man with chest pain and breathlessness.’
This is not precise enough and automatically takes one’s mind to
thinking about cardiac causes. An example of a good problem representation (in this case) is ‘A 50-year-old man, 4 weeks post-op
knee replacement, with acute left-sided pleuritic chest pain and
Box 4.2 Problem representation, or lack thereof, affects the
solution
A final-year medical student working in general practice and had
just seen an 18-year-old man with a two-day history of nausea,
fever, and abdominal pain. He had no past medical history, no
urinary symptoms, and had not opened his bowels for two days. He
had vomited once. On examination, the patient was tender in the
right iliac fossa with no other abnormal findings.
The student gave a good description of the patient’s symptoms
and signs to her supervisor. When asked what she thought the
diagnosis could be, the student thought for a moment and then
said, ‘Constipation.’ The supervisor was surprised. Together, they
worked to represent the problem and came up with: ‘An 18-yearold man with a 2-day history of nausea and vomiting, fever, and
right lower quadrant tenderness.’ Immediately the student thought
of appendicitis, which was the correct diagnosis.
Box 4.3 Language and problem representation
A final-year medical student had just ‘clerked’ an elderly woman
who had been admitted to hospital because of confusion. The
student had spoken to the patient’s husband to get a good
description of what had been happening at home. After obtaining a
history, examining the patient, and looking at the initial test results,
he summed up her problems as:
1. Acute confusion
2. Raised creatinine
However, the student was unable to formulate a plan for each of
these problems and was unsure about what to do next. He was
encouraged to re-define the problems using more precise medical
language. He was able to re-define them as:
1. Delirium
2. Acute kidney injury
Following this, he was immediately able to retrieve information from
memory to formulate a management plan for the patient.
Problem Identification and Management 25
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Relevant past
history
Age+gender
Figure 4.2 Example structure for teaching problem representation. *An
abstraction is a summary of the problem’s essential characteristics using
semantic qualifiers and precise medical language. If relevant, the
characteristics can be encapsulated as a clinical syndrome (for example,
instead of saying that a 50-year-old man with chronic kidney disease stage
5presents with ‘progressive breathlessness, orthopnoea and leg oedema’,
you could say ‘fluid overload’).
(includesmedical,
social,family,
medicationhistory)
Abstraction* of
symptoms,signs,
+/- initialtest
results
breathlessness.’ This immediately leads us to think about other
things, for example, pulmonary embolism. An example structure
for teaching problem representation is shown in Figure 4.2.
Problem-solving by Experts Vs Novices
Studies have found that experts in a particular domain go about
solving problems differently to novices. These differences are
summarised in Box 4.4. You may have seen this in action when an
expert spends significantly more time defining and representing a
problem (e.g., by asking themselves, ‘Why exactly did the patient
come to hospital today?’) and deliberately seeking out further
information (e.g., by talking to relatives to get a collateral history)
before starting to work on a solution.
Experts also solve problems by reasoning forwards, which is
less effortful, whereas novices reason backwards, which can be
laborious and unreliable. For example, novices will select a
potential diagnosis and then check out the description to see
whether it contains facts that support or contradict that diagnosis. Errors can arise by accepting a diagnosis because there is
some evidence to support it and no evidence against it – even
though some other diagnosis, not yet considered, would fit better.
Experts, on the other hand, reason forwards by noting significant
facts which they then explore and are thus able to converge on a
diagnosis in a more straightforward manner [7]. However,
experts also resort to reasoning backwards when they encounter
difficult problems – in other words, clinicians employ the strategy
that best suits their knowledge. An example of forward reasoning
is shown in Figure 4.3.
Problem-solving in the Classroom Vs
Clinical Environments
In a classroom environment, problems are usually presented to
learners ‘on a plate’. Thus, the opportunity to practice problem
recognition, definition, and representation is limited. Because
these are key skills in clinical reasoning, teachers should
endeavour to show, not tell as much as possible during case-based
learning sessions. This can be done in a classroom environment
by using videos of patients describing their symptoms, using
images or sounds of physical examination findings if possible,
and providing test results such as 12-lead electrocardiograms and
blood results without interpretation.
In the clinical environment, teachers should not take the case
presentations of learners at face value. This is because of significant
deficiencies in the clinical skills of learners, due to their inexperi-
ANGINA
exertional
intermittent
non-exertionalchronic
Chest pain
acute
Figure 4.3 An example of forward reasoning.
Box 4.4 Experts go about solving problems differently to
novices
Experts Novices
Spend significantly more time on
problem representation and then
proceed to solve the problem
quickly
Redefine and reinterpret problems
Define and represent problems
according to underlying principles
Generate more efficient problem
representations, stripped of
irrelevant details
Break the problem-solving task
into parts and are able to monitor
their sequential progress easily
Adapted from Zimmerman BJ and Campillo M. Motivating self-regulated
problem solvers. In: Davidson JE and Sternberg RJ (Eds). The Psychology
of Problem Solving. Cambridge University Press, 2003. pp. 236–37.
continuous
Represent problems quickly and
then spend more time working on
a solution (often leading to
mistakes and having to start
again)
Respond to the task without
modifying the structure of the
problem
Define and represent problems
according to surface features
Include irrelevant details in
problem representations
Try to deal with the problemsolving task as a whole and are
less able to monitor their progress
as a result
ence, that result in their inability to discover, define, and represent
problems accurately [8]. As a result, their case presentations are
likely to contain errors and their learning will be greatly enhanced
by reviewing all the available information (including going back
to see the patient) together and then practicing problem recognition and representation with feedback.
Managing Uncertainty
It is not always possible to be certain about what the problem is
for every patient. Authentic clinical reasoning requires clinicians
to gather and interpret imperfect data in real time. Learning how
to take safe and effective action in complex and ambiguous settings is essential for patient safety [9]. Regulators such as the UK’s
General Medical Council include learning to deal with complexity
and uncertainty in their outcomes for graduates [10]. Learners are
likely to commence training believing that most clinical decisions
are binary, given adequate knowledge. For clinicians of all levels,
26 ABC of Clinical Reasoning
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the ability to diagnose and manage conditions accurately is an
important aspect of professional identity and well-being. This is
true of learners too. Many students struggle with the lived experience of uncertainty in clinical practice. The belief that one must
always know the answer can inhibit open discussion and learning,
particularly in groups. Such attitudes can translate into the student’s future practice, inhibiting their confidence and decisionmaking for fear of being seen to be wrong or harming a patient.
Adjusting to practice in the context of uncertainty has profound
impacts for clinical decision-making and patient care [11]. This is
particularly true in specialities where illness is undifferentiated
(e.g., primary care) or rapidly evolving (e.g., acute care).
When dealing with complexity and uncertainty, learners can
sometimes get themselves in a ‘cognitive knot’ by focusing on
what they do not know. Teachers can encourage them to re-frame
things in terms what they do know so they can start ‘untangling’
the problem. This iterative approach can also demonstrate to students that problem-solving, diagnosis and management evolves
during patient care and is not necessarily established at the outset.
Treatment can be given based on either framing a clinical problem
or a diagnostic hypothesis. Reflection on the patient’s response, in
some cases, can further develop the management plan. An
example of getting learners to re-frame things in terms of what
they know is shown in Box 4.5.
In some cases, it may be difficult to come up with a single
problem representation, but it is still possible to identify several
discrete problems. This is common in geriatric medicine and in
patients with multiple co-morbidities. In these cases, a common
approach is to create a problem list. A problem list consists of several mini abstractions which may or may not be diagnoses. An
example of a problem list is shown in Box 4.6. The advantage of a
problem list is that each problem is important in its own right and
requires a management plan. In the ‘history–examination–
differential diagnosis’ method, important problems, and therefore plans to address them, may inadvertently be missed.
Ilgen and colleagues have helpfully deconstructed the term
‘comfort with uncertainty’ to help educators develop the clinical
reasoning ability of learners in situations that appear to be complex and ambiguous [9]. First of all, they unpack what we mean by
‘uncertainty’. Do we mean low self-efficacy (confidence we can
deal with the situation)? Low knowledge? Lack of information?
Discomfort? Clinical ambiguity? They conclude that the word
Box 4.5 Getting learners to re-frame things in terms what
they know
Mrs. Smith is 85 years old and has just been admitted to hospital.
Learner: ‘I have no idea what’s going on, the patient is really
confused and can’t give me any history, all the blood tests are
normal, the vital signs are normal, and when I examine her, I can’t
find anything wrong. I phoned the care home and they told me that
Mrs. Smith is normally really with it, but this morning she was really
confused and that’s why they called the ambulance.’
Teacher: ‘Let’s try and summarise what we do know’ … [Together]: Mrs.
Smith, elderly care home resident, has acute confusion, i.e. delirium.
Box 4.6 Example of a problem list
1. Faecal loading due to opioids
2. Urinary retention
3. Urinary tract infection
4. Acute kidney injury (AKI)
5. Hyperkalaemia
6. Opioid toxicity
7. Caregiver strain
A problem list is a list of acute or presenting problems, not the past
medical history.
‘uncertainty’ is not helpful in adequately capturing what is going
on. Instead, they propose more precise terms:
•
Ambiguity=the properties of the situation
•
Uncertainty=the experience of the individual
Uncertainty, or what the individual is experiencing, can be due to
1) recognising a situation is ambiguous, 2) perceiving limitations
in your own knowledge (which could be due to lack of confidence,
or a well-calibrated judgement), or 3) recognising you have
incomplete information. Certainty is your confidence in inter-
preting the clinical situation. Comfort is your confidence in being
able to act safely and effectively in a situation. This could be as
simple as walking into a situation and recognising you need help
straight away. In summary, being comfortable with uncertainty is
about recognising the situation is ambiguous, or you lack
knowledge/skills, or you have incomplete information, but you
are confident you can still act safely and effectively to manage the
situation.
Finally, when is a good time to start teaching about managing
uncertainty? It may not be helpful to introduce this too early in a
curriculum for reasons to do with cognitive load and stages of
learning. As a simple example, how would you teach a four-yearold to cross the road? How do you cross the road? Learners
acquire skills through instruction and practice, but novices differ
from competent/proficient learners in terms of their recollection,
recognition, decision-making, and awareness [12]. Novices need
recipes and rules, may lack confidence, and their learning and
practice requires high degrees of concentration. This is why consistency of teaching is so important at this stage. Competent/proficient learners have more conceptual understanding and are able
to use underlying principles to find solutions for the context in
which they find themselves. So, learning to deal with uncertainty
is probably something that should be emphasised later in medical
school while practicing with real clinical cases.
Role-modelling by teachers serves an important function.
Though it may diminish, uncertainty in practice never wholly
disappears. The effective clinician does not practice solely by conviction, but recognises practice is uncertain and this is reflected in
their management decisions. Not knowing the diagnosis does not
prevent making management decisions. Management decisions
may themselves reveal or clarify aspects of a diagnosis. Teachers
who share their own experience of this process, and their reflections on their own development, are being supportive and
respectful of their learners’ own developmental needs. Box 4.7
Problem Identification and Management 27
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Box 4.7 Strategies for improving tolerance of uncertainty in
decision-making
•
Careful data-gathering (history)
• Structured physical examination
• Working within a framework of differential diagnosis
• Considering alternative hypotheses
• Excluding ‘must-not-miss’ diagnoses
• Using best available evidence
• Re-evaluating the management plan
• Seeking advice from colleagues
• Sharing limitations of knowledge with the patient
• Shared decision-making with the patient
• Role-modelling reflection
From the ABC of Clinical Resilience, Wiley-Blackwell, 2021.
shows strategies for educators in improving tolerance of uncertainty in decision-making.
Management Reasoning
A lot of the focus of the clinical reasoning literature is on diagnostic reasoning. That is because diagnosis is something that can
be objectively measured in studies. But management reasoning is
clearly an important part of clinical reasoning. Management
reasoning is the process of making decisions about patient
management, including choices about treatment, follow-up visits,
further testing, and allocation of limited resources [13]. There are
several key differences between diagnostic and management
reasoning. Diagnosis can be either correct or incorrect, whereas
there can be more than one ‘correct’ way to manage a patient’s
problems. Diagnosis can often be done without the patient,
whereas management decisions require communication and
shared decision-making. Management reasoning is also influenced by the preferences, values, resources, and constraints of the
patient, clinician, and healthcare institution. Finally, management
plans are inherently fluid and require ongoing monitoring and
frequent adjustments [13].
Shared decision-making is a topic we discuss further in Chapter5.
Management decisions are often co-produced with patients and
carers, but shared decision-making also refers to teams, evidencebased guidelines, technology, scores, and decision aids.
As we stated in Chapter 1, there are situations that call for
decidedly technical and knowledgeable responses (e.g., providing
timely and correct treatment for a myocardial infarction), and
then there are situations that call for wisdom and care (e.g., recognising a person is dying and shifting the goals of treatment towards
caring instead of curing). All of these situations require good
communication skills. The challenge for educators is to provide
multiple opportunities in lots of different contexts for learners to
practice the skills they need for sound management reasoning.
Summary
Clinical reasoning is a complex cognitive process involving
clinical skills, memory, problem-solving, and decision-making.
In medicine, problem identification (recognising, defining, and
representing problems) is important in clinical reasoning. The
kind of language used in representing problems matters, and this
is something that can be taught. Learners need the opportunity to
practice this with coaching and feedback, but this requires that
clinical teachers do not take the case presentations of learners at
face value, because significant deficiencies in the clinical skills of
learners mean that they may be unable to discover, define, and
represent problems accurately.
Managing uncertainty can be overcome, in part, by getting
learners to re-frame things in terms what they do know, generating problem lists, and teachers and learners having a clear and
shared understanding of what we mean by ‘uncertainty’.
Management reasoning is different in many ways to diagnostic
reasoning; it is influenced by preferences, values, resources, and
constraints. Learning management reasoning requires knowledge
and practice with lots of different cases in lots of different contexts with coaching and feedback.
References
1. Pretz JE, Naples AJ and Sternberg RJ. Recognizing, defining, and representing problems. In: Davidson JE and Sternberg RJ (Eds). The psychology
of problem solving. Cambridge University Press, 2003. pp 3–30.
2. Bordage G. (1999). Why did I miss the diagnosis? Some cognitive explanations and educational implications. Academic Medicine; 74(10):
S138–S143.
Duff M and Piai V (Eds). Language and memory: understanding their
3.
interactions, interdependencies, and shared mechanisms. Frontiers Media,
2020. doi:10.3389/978-2-88966-121-3.
4. Erickson B, Dhaliwal G, Henderson MC et al. (2011). Effusive reasoning.
Journal of General Internal Medicine; 26(10): 1204–1208.
5. Bordage G. (1994). Elaborated knowledge: a key to successful diagnostic
thinking. Academic Medicine; 69(11): 883–885.
6. Bordage G and Lemieux M. (1991). Semantic structures and diagnostic
thinking of experts and novices. Academic Medicine; 66(9 Suppl): S70–S72.
7. Bareiter C and Scardamalia M. Experts are different from us: they have
more knowledge. In: Surpassing ourselves: an enquiry into the nature and
implications of expertise. Open Court Publishing, 1993. pp 25–43.
Holmboe ES. (2004). Faculty and the observation of trainees’ clinical
8.
skills: problems and opportunities. Academic Medicine; 79(1): 16–22.
9.
Ilgen JS, Eva KW, de Bruin A et al. (2019). Comfort with uncertainty:
reframing our conceptions of how clinicians navigate complex clinical situations. Advances in Health Sciences Education; 24: 797–809.
General Medical Council. Outcomes for graduates. GMC, 2018. www.
10.
gmc-uk.org (accessed April 2022).
11. Iannello P, Mottini A, Tirelli S et al. (2017). Ambiguity and uncertainty
tolerance, need for cognition, and their association with stress. A study
among Italian practicing physicians. Medical Education Online; 22(1):
1270009.
12. Dreyfus SE. (2004). The 5-stage model of adult skill acquisition. Bulletin
of Science, Technology & Society; 24(3): 177–181.
13. Cook DA, Sherbino J and Durning SJ. (2018). Management reasoning:
beyond the diagnosis. JAMA; 319(22): 2267–2268.
Further Resource
1. Cooper N and Frain J (Eds). ABC of Clinical Communication. Wiley-
Blackwell, 2018.
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CHAPTER 5
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Shared Decision-making
Anna Hammond and Simon Gay
OVERVIEW
• Clinical reasoning continues after a diagnosis is made as part
of subsequent decisions about investigation, management, and
treatment
Guidelines, scores, and decision aids can help clinicians make
•
better decisions
• Guidelines, scores, and decision aids can be applied incorrectly,
leading to errors
• Evidence-based medicine is the integration of best, current
research evidence with one’s own clinical expertise and individual
patient values
• Using effective communication skills, clinicians should endeavour
to present information to patients in a way that is easy to understand, whatever their health literacy, in order to facilitate shared
decision-making
• It is important that clinicians are aware of how context influences
clinician decision-making and student learning
Introduction
Shared decision-making refers to decisions that are co-produced
with patients and carers, within clinical teams, or using evidencebased guidelines, technology, scores, and decision aids. In this
chapter, we will consider how clinical reasoning continues during
decisions about investigations and management, and how those
decisions can be shared. We will also consider the importance of
situated cognition to both the clinical reasoning process and its
development.
Many clinical guidelines, scores, and decision aids function as
heuristics (‘rules of thumb’). They have the advantage of being
externally constructed, incorporate the best available evidence,
and reflect the consensus of a medical community regarding their
validity and reliability. The intention of using them is to increase
the likelihood of patients receiving evidence-based care, with
intended benefits to patients in terms of outcomes, and to healthcare systems in terms of efficiency. The benefits for healthcare
professionals are increased confidence that good care is being
provided, and in the time saved as a result of the critical appraisal
and synthesis of research evidence being done by external bodies.
Clinical Guidelines
The development of a clinical guideline begins with a systematic
review of the literature on the topic under consideration. The process is at risk of bias and conflicts of interest, and a well-conducted
systematic review will include a description of how this risk has
been addressed. It must also assess the strength of the evidence
from each piece of research. Subsequent steps involve consultation with a wide variety of stakeholders, including patient representatives, before the guideline is made available to clinicians [1].
There are many guidelines available, and it can be difficult for
clinicians to judge which are the best ones to use. The features of
a good clinical guideline are shown in Box 5.1.
Box 5.1 Features of a good clinical guideline
• It is based on a well-conducted and transparent systematic review
that includes statements about potential conflicts of interest and
the strength of the evidence
• The guideline’s use should be demonstrated to improve outcomes
for patients in real situations by means of a prospective validation
study
• A range of relevant professionals have been involved in its
development and have reached a consensus about the content
and recommendations
• Patient representatives have been involved in its development and
their views on its acceptability have been incorporated
• A positive impact on outcomes for patients is likely as a result of
its use
• The guideline is applicable to an appropriate range of clinical
situations and individual patients
• The guideline is clearly written and states precisely what its
recommendations are and in what circumstances they apply
• There is enough flexibility in the guidance that patients’ views and
values can be taken into account
• The guideline is updated as new evidence emerges
• It represents a cost-effective use of resources
ABC of Clinical Reasoning, Second Edition. Edited by Nicola Cooper and John Frain.
© 2023 John Wiley & Sons Ltd. Published 2023 by John Wiley & Sons Ltd.
30 ABC of Clinical Reasoning
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Key decisions that clinicians must make when using clinical
guidelines are how well the guideline fits the individual patient
and their situation, and how accurately the population-level data
on which the guideline is based translates to the individual patient
in front of them.
Scores and Decision Aids
The development of scores and decision aids involves a lengthy
process including:
•
The identification of predictors from clinical observation
•
Validation of the ‘rules’ involving cohort studies or controlled
trials
•
Analysis of the usefulness of each rule in terms of its acceptability, feasibility, and cost-benefit
•
Encouraging its adoption into standard clinical practice
As is true for clinical guidelines, the last phase can be challenging.
There is often resistance from clinicians to using scores and
decision aids. This may arise from uncertainty about how to use
them correctly, doubts about their validity and reliability, or a disquiet about wanting to be patient-centred when using a guideline
developed from population statistics.
There are many examples of clinical decision aids and most clinicians will be familiar with their use. A key consideration of the
role they play in decision-making is knowing in what circumstances to apply them. This, in turn, depends on an accurate
clinical assessment through the use of good consultation skills.
When used judiciously, clinical decision aids can enhance a
clinical decision and patient management by adding to its reliability and its acceptability to patients.
The following are situations in which clinical decision aids may
be of use:
•
To inform decisions about investigations and therapeutic
interventions
•
To screen for specific conditions that need a complex or costly
assessment
•
When the clinical decision is a particularly complex one
All make use of clinical assessment findings and some include
numerical scoring systems linked to these findings.
An example of a commonly used score/decision aid in clinical
practice is the Wells’ Score for the investigation of deep vein
thrombosis (DVT) in patients whom, after a history and physical
examination, a clinician has decided may have a DVT [2]. Key
features of the history and physical examination are combined
into a numerical score which is widely available online in an
interactive form. The score is used to estimate the clinical probability of a DVT. The combination of a low Wells’ Score and a
negative D-dimer eliminates the need for further investigations
(i.e., Doppler ultrasound). This is a good example of the importance of using a score/decision aid correctly – the Wells’ Score for
suspected DVT cannot be used by itself to rule out a DVT if the
score is low. Care must also be taken not to confuse this score/
decision aid with the Wells’ Score for suspected pulmonary
embolism.
Other clinical decision aids use the presence or absence of
defined symptoms as the basis for predicting the likelihood of a
specific diagnosis, for example, the Rome 4 Criteria for irritable
bowel syndrome [3]. Others use the presence or absence of
defined features to predict outcome, and therefore which patients
require admission to hospital (e.g., the CRB-65 score for community-acquired pneumonia) [4].
Pitfalls in the Use of Guidelines, Scores,
and Decision Aids
Using a Clinical Decision Aid Incorrectly
Sometimes, clinicians use clinical decision aids incorrectly. An
example would be a clinician consulting with a patient with calf
pain, using the Wells’ Score for DVT and concluding that a low
Wells’ Score means a DVT is unlikely – and therefore that the
patient does not need further assessment with a D-dimer and
possibly a Doppler ultrasound scan. Using the Wells’ Score in the
first place is for situations when a DVT is suspected following the
history and physical examination.
Applying a Screening Tool to Diagnosis
Sometimes, clinicians mistakenly use clinical decision aids to
make a diagnosis when they were designed as screening tools.
This is inappropriate and can lead to misleading results. The
correct approach is that a positive result on a screening tool
should lead to a more thorough clinical assessment before a diagnosis is made. An example of this is the CAGE Questionnaire
which was intended to be a screening tool for alcohol dependence
[5]. It makes use of four questions to be asked during historytaking. A score of two or more is associated with problem drinking
and is a cue to explore drinking habits further; it does not diagnose alcoholism. It is important that clinicians use such tools for
the purpose for which they were designed and in the context of a
fuller clinical assessment.
Entering an Algorithm Inappropriately
Some guidelines and decision aids take the form of algorithms and
are often electronically based. While they can help with some aspects
of making decisions, they may rely on classical presentations and
progression of disease and cannot take into account individual variation and anomalies, and thus involve the potential for errors. An
example is the management of acute coronary syndromes, when
there can be several underlying causes. Imagine a patient who has
been admitted to hospital following 30 minutes of cardiac-sounding
chest pain, who has ST depression on the 12-lead electrocardiogram
and a significantly raised high-sensitivity troponin. However, the
clinician fails to recognise a three-month history of indigestion, iron
deficiency anaemia, and a history of black, tarry stools for the last
few days which has precipitated the acute coronary syndrome, and
starts treatment with dual anti-platelet therapy and heparin –
leading to severe bleeding.
The possible pitfalls for clinicians in using clinical guidelines
and decision aids are summarised in Box 5.2.
Evidence-based Medicine
The difficulty with guidelines is they are based on evidence from
studies of large groups of people. However, clinicians consult with
individual patients, each in their own specific and unique set of
Shared Decision-making 31
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Box 5.2 Possible pitfalls in using clinical guidelines and
decision aids
Using a clinical decision tool incorrectly as a diagnostic tool
•
• Using a screening tool incorrectly as a diagnostic tool
• Applying population-level findings to individuals
• Assuming that all diseases present and progress in a uniform manner
• Failing to take patient factors and preferences into account
• Some decision aids require classical clinical presentations and
progression of diseases
circumstances, with their own unique beliefs, values, and preferences. A key clinical decision is how applicable this guideline is to
the individual patient. Sackett, known as the father of evidencebased medicine, defines evidence-based medicine [6] in Box 5.3,
and Figure 5.1 illustrates the application of evidence-based medicine in a specific clinician–patient encounter.
It is important to take account of individual patient factors when
applying a guideline. Otherwise the outcome may be suboptimal
for that patient. As clinicians, we must be mindful that patients may
be harmed if they are subjected to procedures and treatments that
are inappropriate for them in their circumstances, or unacceptable
to them for a variety of reasons, even if they meet ‘the criteria’.
Applying Clinical Guidelines in Practice –
Helping Patients Share Decision-making
Good communication is a vital aspect of clinical reasoning – from
history and physical examination through to using guidelines,
scores, and decision aids in clinical practice. This communication
needs to be directed at reaching a shared understanding with
patients about their illness in order to share decision-making with
them (see Figure 5.2).
The challenge for clinicians is in translating scientific, populationbased data into a practical management plan for the patient in front
of them. This involves many complex decisions, both relating to the
critical appraisal of the information itself and its practical application,
to the assessment of the needs of an individual patient, and how to
maximise the chances of the patient accepting and adhering to the
proposed management plan. The decisions involved in this latter
aspect are about how to communicate risks and benefits of treatments in a way that recognises an individual patient’s health literacy.
There is evidence that many people do not understand percentages, proportions, or ratios, and that a more effective strategy
is to use absolute risk. For example, when thinking about women
deciding whether or not to take hormone replacement therapy
because of the risk of breast cancer, consider the three statements
in Box 5.4. They all sound quite different, and it may be difficult
to know what each actually means.
Now consider the statements in Box 5.5. This is the same risk
expressed as the absolute risk and is much easier for many patients
to understand. Patients may feel more confident about making a
decision having been given the information in an absolute risk
format.
Another way of using absolute risk is by talking about absolute
riskreduction, and a variation of this is the concept of ‘prolongation
of life’. This can be used when helping patients to decide about
Box 5.3 Sackett et al.’s definition of evidence-based medicine
‘Evidence-based medicine is the integration of best (current)
research evidence with clinical expertise and patient values.’
Adapted from Sackett DL et al., 1996.
Figure 5.1 Application of evidence-based medicine (EBM) at the level of
the individual patient and clinician.
Figure 5.2 Shared decision-making with patients. Image created by Dr Mark
Hamilton, Associate Professor, Leicester School of Medicine, using free to
use images from the website of the National Cancer Institute (https://www.
cancer.gov).
32 ABC of Clinical Reasoning
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Box 5.4 Talking about risk and hormone replacement therapy
(HRT)
For women who take HRT, the risk of breast cancer increases by
•
just over a quarter
• For women who take HRT, the risk of breast cancer increases by 27%
• For women who take HRT, the risk of breast cancer is 1.27 times
greater than for those who do not take HRT
From the Medicines & Healthcare Products Regulatory Agency
UK, 2019.
preventative measures such as stopping smoking. Consider the case in
Box 5.6, for example. As a result of the patient having information as
a simple statement of prolongation of life, she was able to understand
the benefits of stopping smoking in a way that was directly related to
her concerns and priorities. She could perceive a clear and easily comprehensible gain if she could manage to stop. This increased her conviction that she should stop. The doctor used reasoning skills to make
decisions about how to find, appraise, interpret, and apply evidencebased information to achieve a good outcome for the patient.
In recent years, extending the concept of giving patients enough
information to make informed decisions about their health has
led to a growing interest in formal Patient Decision Aids (PDAs).
These are intended to present evidence-based information to
patients in a way they can easily understand in order to help them
make decisions with the support of their clinician. An example of
such a decision aid is shown in Box 5.7.
Consider the statements in Box 5.5. The ‘understanding the
risks of breast cancer’ infographic compares lifestyle risk factors
versus HRT treatment which might further help clinicians’ discussions with some patients regarding the risks of HRT. The
information is simple and visual, but the onus is on the clinician
to communicate the information in a manner that matches the
patient’s health literacy and thus help the clinician and patient
make a shared management decision together.
There is currently rapid development in the field of patient
decision aids that goes hand in hand with the democratisation of data
as a result of better access via electronic records. Often, patients arrive
at their consultations with ideas generated as a result of their own
online searches. An important contributor to many consultations is
the clinician’s willingness to help with the interpretation and explanation of such information and its application to the patient’s individual
circumstances. The patient’s choice of information provides a cue to
their own values and perspective. This is a particularly important
contribution made by the clinician. As can be seen when comparing
the detail of Boxes 5.4 and 5.5, the data offered by different studies
does not always match precisely, and the clinician then has a crucial
role in making sense of the detail through discussion with the patient.
Box 5.5 Talking about absolute risk and hormone replacement
therapy (HRT)
In a group of 1000 women, there will be 3 new cases of breast
•
cancer every year
• In a group of 1000 women who take combined HRT, there will be
nearly 4 new cases of breast cancer every year
Adapted from British Menopause Society 2017.
Box 5.6 Using prolongation of life to encourage smoking
cessation
Donna is 40 years old. She has smoked 25 cigarettes a day since
•
she was 16. Donna’s general practitioner (GP) wants to convey to
her the benefits of stopping smoking.
• The GP knows that the chance of a woman who smokes surviving
until the age of 79 years is 32% lower than for one who does not,
and that the rate of death from any cause among current smokers
is three times higher for people aged 25–79 years than for those
who do not smoke. The GP also knows that the average age of
death for women who do not smoke is 81 years, and 71 years for
those who do smoke. The absolute risk reduction (for dying from a
cause associated with smoking) is 90% for those who stop
smoking before they are 40 years old. Donna has not been
convinced by any of these arguments and thinks that she has
smoked for so long that nothing will make any difference now.
•
The GP decides to try a different approach. She wants to convey
to Donna that stopping smoking will have a positive effect on her
life expectancy. After some searching, she finds out that if Donna
were to stop smoking in the next year, she is likely to live for
about 9 years longer than if she does not stop. This would mean
that her life expectancy would become almost the same as if she
had never smoked.
• When the GP used this different approach, Donna found this
information compelling, worked hard at stopping smoking, and
was successful. She also convinced her partner to stop by using
the same argument.
Situated Cognition
Situated cognition, a concept discussed further in Chapter 8,
encompasses a range of theories that are united by the assumption
that cognition is inherently tied to the social and cultural contexts
in which it occurs [7]. In clinical practice, situated cognition
embraces the notion of complex interactions between the individual
participants and the environment, all of which can influence the
outcome (patient care) in the medical encounter [8]. This is very
important to clinical reasoning in two ways:
•
The clinician needs to appreciate how the context in which
clinical reasoning is occurring influences both the reasoning
itself and its outcomes.
•
As much as students can learn through abstract means such as lectures, books, and tutorials, at some stage they have to engage in
learning clinical reasoning situated in the clinical environment
where they can eventually deploy their clinical reasoning on a routine basis. In this way the learner can move from the abstract and
theoretical to the practical, experienced in an authentic context [9].
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