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Found in translation: the impact of familiar symptom descriptions on diagnosis in novices

Authors


Meredith Young, Department of Psychology, Neuroscience and Behaviour, PC 141, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada. Tel: 00 1 905 525 9140 (ext 24824); Fax: 00 1 905 529 6225; E-mail: youngme2@mcmaster.ca

Abstract

Context  The language that patients use to communicate with doctors is quite different from the language of diagnosis. Patients may describe tiredness and swelling; doctors, fatigue and oedema. This paper addresses the process by which novices, who have learned standard medical terms for symptoms, use lay descriptions of symptoms to reach a diagnosis. Data in this paper indicate that the familiarity of the language used to describe symptoms influences diagnosis in novices and diagnosis does not, therefore, involve a simple translation into standard terms that are the basis of diagnostic decision.

Methods  A total of 24 undergraduate students were trained to diagnose 4 pseudo-psychiatric disorders presented in written vignettes. Participants were tested on cases that contained 2 equally probable diagnoses, in 1 of which the symptoms were expressed using previously seen descriptions. A deviation from 50 : 50 in reported diagnostic probabilities was expected if the familiar symptom descriptions biased diagnostic decisions. Twelve participants were tested immediately after training and 12 after a 24-hour delay.

Results  Participants assigned greater diagnostic probability to the diagnosis supported by the familiar feature descriptions (F[1.242] = 19.35, P < 0.001, effect size = 0.40) on both immediate (52% versus 41%) and delayed (51% versus 38%) testing.

Discussion  The findings indicate that diagnosis is not simply based on a process of translating patient descriptions of symptoms to standard medical labels for those symptoms, which are then used to make a diagnosis. Familiarity of symptom description has an effect on diagnosis and therefore has implications for medical education, and for electronic decision support systems.

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