on all of the diagnoses made regularly and critically and has an excellent memory.
Pattern recognition works well for many common disorders and has the advantage of being quick and cost‐effective…provided the diagnosis is correct. The vet appears competent to the client because the vet has acted decisively and confidently…provided the diagnosis is correct.
An example of a case where pattern recognition will invariably be used by most vets and will be successful (most of the time) is Sundance. The differential diagnoses for the pattern of clinical signs – weight loss despite polyphagia associated with polydipsia, altered behaviour and tachycardia – are very limited with hyperthyroidism being the explanation in the vast majority of cases. Score 10 out of 10 for pattern recognition!
However, pattern recognition can be flawed and unsatisfactory when the clinician is inexperienced (and therefore has seen very few patterns) or only considers or recognises a small number of factors (and is not aware that this process is mainly driven by unconscious processes that might need to be reflected upon if they fail). Or even if the clinician is experienced, it can be flawed for uncommon diseases or common diseases presenting atypically, when the patient is exhibiting multiple clinical signs that are not immediately recognisable as a specific disease, or if the pattern of clinical signs is suggestive of certain disorders but not specific for them.
The pattern of clinical signs that Brutus is showing has a much larger range of causes, but it is likely that an experienced veterinarian will recognise several possibilities though often not all. An inexperienced clinician will consider fewer potential differentials.
Differentials diagnoses that experienced vets will consider for Brutus include liver disease, renal failure, hypoadrenocorticism, hypercalcaemia and diabetes mellitus. Routine blood work will be very helpful (provided it is interpreted correctly). Even if the veterinarian hasn’t given a lot of thought to potential differentials (which may hinder discussion with the owner, however), sufficient information may be gained from suitably comprehensive testing (sometimes called ‘going fishing’).
For Brutus the liver enzymes were substantially increased, hypercalcaemia was noted and the final diagnosis was hypercalcaemia associated with hepatic lymphoma (confirmed on ultrasound‐guided biopsy).
The pattern of clinical signs that Erroll is showing are just downright weird, involving different body systems over a period of time and with no ‘obvious’ single explanation for all of the signs even for very experienced clinicians. His bloodwork only showed an inflammatory leukogram. The final diagnosis was a pancreatic abscess and peritonitis from which E. coli was cultured and a urinary tract infection – from which E. coli was cultured.
In addition, for the experienced clinician, the success of pattern recognition relies on a correct diagnosis for the pattern observed previously being reached and not assuming that similar patterns must equal the same diagnosis. Pattern recognition can lead to dangerous tunnel vision where the clinician pursues his/her initial diagnostic hunch based on pattern spotting to the exclusion of other diagnostic possibilities. Several types or indeed combinations of diagnostic bias can result – some examples are shown in Table 2.1.
Table 2.1 Diagnostic biases in clinical medicine.
| Availability bias | A tendency to favour a diagnosis because of a case the clinician has seen recently. |
| Anchoring bias | Where a prior diagnosis is favoured but is misleading. The clinician persists with the initial diagnosis and is unwilling to change his/her mind. |
| Framing bias | Features that do not fit with the favoured diagnosis are ignored. |
| Confirmation bias | When information is selectively chosen to confirm, not refute, a hypothesis. The clinician only seeks or takes note of information that will confirm his/her diagnosis and does not seek or ignores information that will challenge it. |
| Premature closure | Failing to look for additional information after reaching a potential diagnosis and, as a result, narrowing the choice of diagnostic hypotheses too early. |
And finally, the disadvantage of relying entirely on pattern recognition to solve clinical problems means that should the clinician realise subsequently that his/her pattern recognition was incorrect, there is no logical intellectual framework to help reassess the patient. Thus, pattern‐based assessment of clinical cases can result at best in a speedy, correct, ‘good value’ diagnosis but at worst in wasted time and money and, sometimes, it endangers the patient’s life.
I’ll do bloods!
Routine diagnostic tests such as haematology, biochemistry and urinalysis can be enormously useful in progressing the understanding of a patient’s clinical condition. However, relying on blood tests (often called a minimum database) to give us more information about the patient before we form any assessment of possible diagnoses can be useful for disorders of some body systems but totally unhelpful for others.
Serious, even life‐threatening, disorders of the gut, brain, nerves, muscles, pancreas (in cats) and heart, for example, rarely cause significant changes in haematological and biochemical parameters that are measured on routine tests performed in practice. Over‐reliance on blood tests to steer us in the right clinical direction can also be problematical when the results do not clearly confirm a diagnosis. The veterinarian can waste much time and the client’s money searching without much direction for clues as to what is wrong with the patient. And of course, the financial implications of non‐discriminatory blood testing can be considerable, and many clients are unable or unwilling to pay for comprehensive testing. Using blood testing to ‘screen’ for diagnoses can be misleading, as the sensitivity and specificity of any test are very much influenced by the precision of the test and the prevalence of a disorder in the population.
For experienced veterinarians, pattern recognition combined with ‘fishing expeditions’ (i.e. ‘I have no idea what’s going on so I’ll just do bloods and hopefully something will come up!’) can result in a successful diagnostic or therapeutic outcome in many medical cases in first‐opinion practice. However, there are always cases that do not yield their secrets so readily using these approaches, and it is these cases that frustrate veterinarians, prolong animal suffering, impair communication, damage the trust relationship with clients and on the whole make veterinary practice less pleasant than it should be.
You also have to know about and remember lots of diagnoses for this approach to be effective. This is problematical if the veterinarian does not recognise or remember potential diagnoses (e.g. for Brutus) or if, as discussed previously, the pattern of clinical signs doesn’t suggest many feasible differentials (e.g. for Erroll). It is also less useful for inexperienced veterinarians or veterinarians returning to practice after a career break or changing their area of practice.
It is for all of these reasons that we hope this book will enhance your problem‐solving skills as well as build your knowledge base about key pathophysiological principles. We want to assist you to develop a framework for a structured approach to clinical problems that is easy to remember, robust and can be applied in principle to a wide range of clinical problems. The formal term for this is problem‐based inductive clinical reasoning.
Problem‐based inductive clinical reasoning
