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'Noisy patients'—can signal detection theory help?

Nature Clinical Practice Neurology volume 4, pages 306–316 (2008)Cite this article

Abstract

Signal detection theory tests an observer's ability to discriminate between signal and noise. Deciding whether or not a patient's symptoms warrant further investigation or treatment is an example of this task in the clinical setting. Noise can exist within the observer—for example, in the brain of a tired or inexperienced doctor—or can arise from an external source such as the patient. Patients can produce external noise by giving numerous unrelated presenting complaints, providing overly detailed accounts of their symptoms, or simply talking too quickly. The more noise that is present, the harder the signal (such as a new disease or a notable change in an old condition) is to detect. Patients in the neurology clinic seem to be 'noisier' than average, perhaps owing to the long duration of their condition in many cases and the relatively high proportion of patients with medically unexplained symptoms. The ability to interpret such 'noisy' histories often underpins the neurological diagnosis. This Review aims to promote the relevance of signal detection theory to the overworked neurologist on the ward or in the clinic and explores strategies to reduce the noise generated both within the brain of the doctor and by patients.

Key Points

  • Signal detection theory offers a powerful model to explain decision-making processes in the laboratory, and it can also be applied to the setting of clinical consultation

  • Signal becomes harder to distinguish from noise when we are tired or trying to hold too much information in our working memory

  • The way in which patients present information might make important symptoms harder to distinguish from less clinically relevant ones

  • Patients in neurology might be 'noisier' than average, owing to the often long-standing duration of their condition and the greater proportion that have medically unexplained symptoms

  • Strategies to reduce noise include writing down key facts as they emerge, getting patients to self-filter their symptoms, and asking the patients to focus on one presenting complaint at a time

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Figure 1: Calculation of d-prime.
Figure 2: The criterion.
Figure 3: The effect of shifts in the criterion.
Figure 4: The receiver operating characteristic curve for different values of d-prime.
Figure 5: The merging of criteria in a multi-stimulus environment.

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Author information

Authors and Affiliations

  1. R Oliver is a Clinical Research Fellow and J Rothwell is Professor of Human Neurophysiology in the Institute of Neurology, and O Bjoertomt is a Research Fellow in the Institute of Cognitive Neuroscience, all at University College London,

    Rupert Oliver, Otto Bjoertomt & John Rothwell

  2. R Greenwood is Consultant Neurologist to the National Hospital for Neurology and Neurosurgery, Queen Square and the Homerton University Hospital, London, UK.,

    Richard Greenwood

Authors
  1. Rupert Oliver
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  2. Otto Bjoertomt
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  3. Richard Greenwood
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  4. John Rothwell
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Corresponding author

Correspondence to Rupert Oliver.

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Supplementary information

Supplementary Box 1

The ability of neurologists, radiologists and emergency physicians to detect infarction or hemorrhage on cranial CT. (DOC 55 kb)

Supplementary Box 2

The ability of clinicians to detect malignant melanoma. (DOC 24 kb)

Supplementary Box 3

Signal detection: clinical observation versus radiological investigation. (DOC 26 kb)

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Oliver, R., Bjoertomt, O., Greenwood, R. et al. 'Noisy patients'—can signal detection theory help?. Nat Rev Neurol 4, 306–316 (2008). https://doi.org/10.1038/ncpneuro0794

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