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| Just like Mr B and Phil, pattern recognition and problem based analysis may seem poles apart on the diagnostic continuum, but a recent paper in JVME argues that we must use both. |
“In an ideal world, every
patient would display unambiguous signs of disease confirming to classical
textbook descriptions, and the clinician’s pharmacy would be an assembly of
rational and efficacious therapeutic agents that would collectively address all
the diseases of the animal kingdom. Unfortunately, the ideal world is not the
real world, and a series of limitations relating to all aspects of diagnosis
and therapy make veterinary medicine (as with human medicine) a “science of
uncertainty’” – Stephen May, 2013.
As a veterinary student I
operated under the assumption that one qualified I would be a clinician working
in the ideal world, as described by May. I believed that once I graduated and
was in full command of all veterinary knowledge, I would be able to manage
cases and determine the single correct diagnosis and treatment plan. It turns
out that companion animals don’t read textbooks. They certainly don’t follow
them.
The type of clinical
reasoning taught in veterinary schools is one which assumes certainty – when
that can lead one up the (wrong) garden path in practice.
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| It may be the wrong garden path, but if I unleashed my guinea pigs here there would be a lot more path and a lot less garden. |
In a recent article in the
Journal of Veterinary Medical Education,
Royal Veterinary College Deputy Principal Stephen May highlights one of the
major flaws of veterinary curricula: the mixed messages it sends about clinical
reasoning. Students are taught problem-based learning and discouraged from
relying on pattern recognition, when pattern recognition clearly has merit.
The article makes an
important distinction between scientific reasoning (with its emphasis on
objectivity, presupposition that the observer stands outside the process,
solving scientifically framed questions with appropriate technology, and where
data requirements are saturated) and clinical reasoning (more subjective, where
the clinician is part of the process and outcome, problems are not neatly
framed, datasets are incomplete and ultimately the clinician must act in the
absence of complete knowledge or data). Unlikely scientific reasoning which
begins with a hypothesis, clinical reasoning starts with a problem and works
forward, on the basis of probability, using inductive logic, to determine the
most likely answer.
May discusses two major
forms of reasoning: type 1 being consistent with a pattern recognition approach
(this is intuitive, rapid and a not always conscious response to cues); with
type 2 being consistent with a problem based, analytical, first-principles
approach. It is much more demanding time wise – we tend to use this kind of
thinking when presented with an unusual condition, an uncommon presentation of
a common condition, or when something tells us this just isn’t right. Type 2
reasoning requires a large working memory.
He argues persuasively
that type 1 and type 2 reasoning are complimentary and should be used together.
We may arrive at an answer via type 1 reasoning but should be prepared to cross
check where any doubt is present.
“It is essential that
experts and novices alike be aware of the need and have the capacity to resort
to analytical reasoning when cases are difficult,” he writes.
But he adds that it is
dangerous to assume that type 2 reasoning is free of bias or free from error.
It requires some accurate framing of the problem, and because it employs rules
it is prone to error in cases where those rules don’t apply.
So sound clinical
reasoning involves weighing up probabilities, cross-checking our gut feeling
with the available dataset and the rules we know we can apply, and being aware
of gaps in our knowledge and limitations in our thinking.
Sir William Osler, cited
by May, expresses this beautifully:
[Medicine is the] practice of an art which consists largely in balancing possibilities. It is a science of uncertainty and an art of probability…Absolutely diagnoses are unsafe and made at the expense of conscience.
To study the phenomenon of disease without books it to sail an unchartered sea, while to study books without patients is not to go to sea at all.
It is wonderful to see
that clinical reasoning has become a subject of study unto itself. Thinking
through problems is central to what veterinarians do. One of my colleagues, a
retired equine practitioner, told me “vets are decision making machines. You
could make 1000 decisions in a day.” The mechanisms of decision making
therefore impact on clinical outcomes and patient wellbeing and are worthy of
our attention in their own right.
Reference
May SA (2013) Clinical
reasoning and case-based decision making: the fundamental challenge to
veterinary educators. Journal of
Veterinary Medical Education 40(3):200-209.
