Epistemic tasks in clinical
decision-making
The goal of clinical decision-making is to compose a diagnosis and
treatment plan that is suitable to the patients’ personal situation,
signs and symptoms and based on relevant and reliable evidence.
Computer-based clinical decision support systems (CDSSs) are expected to
improve clinical decision-making by making it faster, cheaper, less
prone to human errors or more precise.6,12 In
practice, clinician and computer can complement each other, each having
different capacities to perform crucial but different epistemic tasks
that together add up to a diagnosis or treatment plan. In order for CDSS
to support clinical decision-making, the capacities of human and
artificial intelligence need to be maximally utilized and aligned to
each other. First, we will analyse which epistemic tasks can be better
done by CDSS, and which by clinicians.
Clinical decision support
systems
CDSS makes use of artificial intelligence (AI) that is designed to mimic
or improve clinical decision-making. Two broad categories of AI uses in
CDSS are usually distinguished:6,8,10‘knowledge-based’ AI (also called
rules-based expert systems9) and data-driven AI.
Knowledge-based AI systems have been in use since the late 1970’s, and
aim to replicate human decision-making by programming the rules experts
employ when they make decisions in their field in computational
terms.10 As such, a knowledge-based system can best be
thought of as a database of ‘best-practice’ rules that can be employed
to find the most suitable procedure (e.g. examination or treatment) for
an individual patient.9 The ‘logic’ employed by the
system can be represented as formal rules, such as “when a patient with
disease X also has symptom Y, use medication Z.” As such, the
‘reasoning’ employed by the system to arrive at a specific advice, can
easily be backtracked and
evaluated.
The data-driven use of AI has developed significantly over the
last decade, and employs statistical machine learning algorithms to
abstract patterns from large amounts of data. In the so-called
supervised machine learning to develop a CDSS, the machine learning
system is fed with a large amount of data about a group of patients
labelled with the clinical diagnosis by medical professionals, the
so-called ‘training dataset’. In this learning-phase, the CDSS learns to
‘recognize’ the patterns (represented by a ‘model’) in the training-set
that fit best with the correct diagnoses.
When a new case is entered into
the system, it will use the patterns that it has inferred from the
‘training-set’ to make a prediction about an individual
case.10 The
‘logic’ employed by this type of CDSS is (rather than rule-following as
in knowledge-based CDSS), based on comparisons between cases, such as
“other patients with disease X and symptom Y have benefited from using
medication Z”.9 Because data-driven CDSSs are often
trained using data from thousands of cases or more, a multitude of the
amount of cases that a physician sees in a lifetime, these systems are
able to detect very subtle and complex patterns in the data (e.g. Savage
202012). However, unlike knowledge-based AI, the
decision made in a data-driven CDSS cannot easily be
explained,6 which leads to critical questions about
the robustness, explainability, reliability and accountability of these
types of
systems.10
Epistemic tasks by CDSSs: statistical reasoning and pattern
recognition
Knowledge-based systems can be thought of as a database of best practice
in terms of rules, such as evidence-based guidelines. The advantage of
an automated system is that it can use the patient’s individual
characteristics to find the most suitable guidelines and procedures.
Data-driven systems do not use
this type of rule-following, but have other capacities. Boon (2020) has
analysed the epistemic tasks that machine-learning algorithms are
capable of doing. According to her categorisation of epistemic tasks,
machine-learning algorithms can match input data (e.g., an image
or a set of data points such a clinical signs and symptoms) with similar
cases in their database; interpret input data as belonging to a
specific category, defined by humans or by a machine-learning algorithm;diagnose a set of input data as probably belonging to a certain
class and from that infer other properties of the target;structure large amounts of data to find patterns, correlations
and causal relations; calculate in a way that outperforms humans;
and simulate complex dynamic process.15 In
short, computers outperform humans when it comes to deductive and
inductive reasoning, and are also rapidly improving at recognizing
patterns and images. As such, the medical field in which CDSS has been
most successful is radiology (and also other types of visual data, e.g.,
electrocardiograms), detecting conditions such as tumours and other
lesions in large amounts of imaging data in short amounts of
time.3,12,16 Furthermore, as humans are notoriously
bad at statistical reasoning (for example, estimating odds based on
quantitative information, see e.g. Kahneman 201117),
CDSS can provide a valuable contribution to the process of clinical
decision-making by comparing the information clinicians do have about a
patient with the information about other (groups of) patients in the
database of the CDSS. And, based on similarities with other cases, use
this to make suggestions about the diagnosis and predictions of possible
outcomes of a certain treatment.
However, as Boon contends, in most professional fields, the goal of
performing epistemic tasks such as those listed above, is not (only) toidentify the most refined classification, or the most perfectly
fitting class. Rather, the epistemic purpose is knowing how to control
or interact with the targeted phenomenon (e.g., the symptoms or illness
of a patient), which requires relevant understanding to begin with.
Translated to clinical practice,
the goal of performing epistemic tasks is to device interventions that
contribute to making the correct diagnoses or actions that alleviate the
patient’s symptoms or benefit the health of patients. This requires
human intelligence, for example to collect, review and process data
before it can be entered into the CDSS, to judge which information is
relevant, and to evaluate the outcomes. In the next section, we will
therefore elaborate on the epistemic tasks of clinicians.
Epistemic tasks by clinicians: constructing a ‘picture of a
patient’
In an earlier paper, we have argued that good quality decision-making
involves highly complex and refined ways of clinical reasoning, of which
several examples can be given.11 First, while
considering the available information, clinicians continuously deduce
and verify options – this is because they understand, for instance,
that one effect can have multiple causes and one cause can have multiple
effects. Second, in addition to algorithmic and deductive, rule-based
reasoning, “creative” thinking and nuanced styles of reasoning are an
important part of good clinical decision-making. For example, clinicians
make use of case reports, descriptions of individuals or small groups
with ‘surprising’ or ‘problematic’ symptoms18 to come
up with a possible diagnosis. Or they use narrative techniques to
logically integrate all available information.19Third, an understanding of the mechanisms of a disease is necessary to
translate general statistical information to the situation of individual
patients.20,21 Finally, Khushf (1999) argues that the
diagnostic process involves both determinative judgement(bringing a particular instance under a general concept) andreflective judgement (beginning with a particular and seeking out
a concept). When a patient visits a medical professional, this expert
develops an initial insight into what is the matter with that patient (a
set of possible diagnoses based on integration of the patient’s specific
signs and symptoms), thus providing a reflective judgment. A diagnosis
is then established by a determinative judgment, i.e. by determining
under which diagnosis the observed (but usually incomplete) signs and
symptoms fit best.22 These epistemic tasks (i.e.,
making these judgments) cannot be outsourced to a machine learning
system because it concerns reasoning which is not algorithmic or
statistical. It is therefore important that clinicians have developedexpertise , which includes tacit knowledge andcognitive skills , enabling them to draw up a diagnosis or
treatment plan, despite incomplete information and
uncertainty.14 In addition, clinical decisions are
often based on the integration of pieces of evidence generated by
medical professionals with different expertise. Interpreting and
adjusting the pieces of evidence into a coherent diagnosis takes place
in interaction between different experts. This requires specific skills
to enable the (social and epistemic) interaction between experts, i.e.
opening up and explaining their deliberation to others and justifying to
others how they come to a certain interpretation, while being sensitive
to deliberations and interpretations from others.23
Epistemological
responsibility
In the previous sections we have analysed which epistemic tasks
concerning clinical decision-making CDSS are well-equipped to perform,
and which epistemic tasks require human intelligence. Additionally, we
need to explain why clinicians remain responsible for the decisions made
in clinical practice, for which we give epistemological reasons.
Earlier, we have pointed out that clinicians have the epistemic task to
develop a ‘picture’ of a patient that is logically coherent and
consistent with contextual and personal information as well as general,
scientific and statistical knowledge.11 Clinicians
together with the patient, and usually in collaboration with other
medical experts, use this ‘picture’ in their clinical reasoningabout the diagnosis and treatment of the patient. Usually this involves
a process in which the clinician, based on the formed picture so far,
forms hypotheses about the illness and asks new questions. This leads to
additional diagnostic tests and searches in medical literature, which in
turn produces new information that is added to the picture, leading to
new hypotheses and questions, etc. In other words, the clinician enters
into a search process (exploration and investigation) in which new
information is adapted and integrated with the existing
information. In this process, clinicians continually update the
‘picture’ they have of their patients, and use it to direct the next
step in the search process.
Collecting, interpreting, adapting and integrating the data into a
coherent picture involves a considerable amount of choice, deliberation
and justification by clinicians, for example about the relevance and
quality of the information. Clinicians are epistemologically responsible
for these choices and deliberations, although CDSS can help by providing
information in ways suggested above. As a consequence of this
epistemological context, clinicians are responsible for the way they
construct and use the ‘picture’ of the patient. This also means that
clinicians need to be able to explain and justify their decision-making.
We have therefore argued that clinicians should consider themselvesepistemologically responsible to produce good quality knowledge
about their patients.11 The idea of epistemological
responsibility is based on Lorraine Code’s (1984) insight that cognitive
agents (such as doctors) have an important degree of freedom when it
comes to reasoning (e.g., in deciding which information is relevant and
which not in their argument; and how to interpret specific information)
and that they are accountable for how they deal with this
freedom.24 Therefore, in contrast to passive
information processors (such as CDSS or other algorithms) that are at
best reliable and fast, clinicians, as cognitive agents, should be
evaluated in terms of responsibility. With the notion of epistemological
responsibility we aim to grasp the specific epistemic challenges faced
by clinicians to perform epistemic activities involved in clinical
decision-making concerning diagnosis and treatment. As CDSSs outperform
clinicians in some specific, well-defined tasks, their applications may
still comply with the epistemological responsibility of clinicians. This
requires, however, that the CDSS is fitted into the clinical
reasoning process , and that the clinician is still able to take
responsibility for this process. In Section 3 we will analyse what this
means for the development of CDSSs, the required properties of a CDSS,
the required skills of the clinicians and the role that a CDSS can play
in clinical reasoning.