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Work is needed to determine the best scaling for PEF data to enable patients and clinicians to get the most benefit from them
A large part of medical practice involves pattern recognition. A clinician may note that a few key aspects of a patient’s history, their demographic data, their clinical examination, and chest radiograph fit a pattern they recognise as making a particular diagnosis highly probable. This pattern involves more than one domain of data acquisition, and both within and between these domains our ability to recognise patterns may be affected by how the information is presented to us. If data are presented to us verbally, the ordering of this information may be crucial. For example, verbal instructions on how to get from ward A to ward B are easier to understand and use if they are given in consecutive order starting from ward A and ending up at ward B rather than the instructions coming in random order. The order in which a patient’s history is presented to a colleague is an example of this. The graphical presentation of tables of numbers may improve the usefulness of the data,1 especially if there is a shape in the data that conveys the signal and the time required to search the data for any signal is thereby reduced.2 In this issue of Thorax Reddel and colleagues3 question whether we are doing enough to present our patients’ peak expiratory flow (PEF) data in a manner that is likely to facilitate both clinicians and patients distinguishing the signal or message in the data from all the noise.
Research continues to add to our understanding of how the brain detects and learns patterns. Facial recognition and the interpretation of facial expression are key aspects of human interaction involving complex pattern recognition. …
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