Eyeballing: the use of visual appearance to diagnose ‘sick’ Academic Article uri icon

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abstract

  • CONTEXT: Prior studies suggest that clinicians can categorise patients in an emergency room as 'sick' or 'not sick' using rapid visual assessment. The rapid nature of these decisions suggests clinicians are relying on pattern recognition or System 1 processing; however, this has not been studied experimentally. In this study, we explore the accuracy of these decisions using patient disposition (discharge, admission to ward or admission to critical care) as an objective outcome, and collect evidence to argue for the use of System 1 processing in the 'sick' or 'not sick' decision process. METHODS: Fourteen practising emergency physicians reviewed 25 videos of patients presenting to the emergency room. They were asked to predict patient disposition (discharge, admission to ward or admission to critical care) and estimate whether they were 'sick' or 'not sick' using a continuous slider on a 'sick' scale from 'not sick' (0) to 'sick' (100). We collected decision time and asked physicians to identify how they came to the decision using a continuous slider on a 'system processing' scale from 'knew immediately' (0) to 'deliberated intently' (1). RESULTS: Inter-rater reliability judging 'sick' was computed as an intraclass correlation coefficient (ICC) of 0.54. Agreement among physicians in predicting disposition was 68% with ICC of 0.44, and accuracy at predicting disposition was 55%. Physicians made their decision in an average of 10 - 11 seconds and rated 70% of their decisions as < 0.5 on the scale from 'knew immediately' (0) to 'deliberated intently' (1). CONCLUSIONS: Experienced emergency physicians are able to visually assess patients rapidly and predict disposition in a very short time, albeit with fair reliability and lower accuracy than reported previously. Subjectively, they reported that the majority of decisions were on the side of 'knew immediately', consistent with the application of System 1 processing.

publication date

  • November 2017