Pain management decisions in emergency hospitals are predicted by brain activity during empathy and error monitoring

Abstract

Pain undertreatment, or oligoanalgesia, is frequent in the emergency department (ED), with major medical, ethical, and financial implications. Across different hospitals, healthcare providers have been reported to differ considerably in the ways in which they recognise and manage pain, with some prescribing analgesics far less frequently than others. However, factors that could explain this variability remain poorly understood. Here, we used neuroscience approaches for neural signal modelling to investigate whether individual decisions in the ED could be explained in terms of brain patterns related to empathy, risk-taking, and error monitoring. For 15 months, we monitored the pain management behaviour of 70 ED nurses at triage, and subsequently invited 33 to a neuroimaging study involving three well-established tasks probing relevant cognitive and affective dimensions. Univariate and multivariate regressions were used to predict pain management decisions from neural activity during these tasks. We found that the brain signal recorded when empathising with others predicted the frequency with which nurses documented pain in their patients. In addition, neural activity sensitive to errors and negative outcomes predicted the frequency with which nurses denied analgesia by registering potential side-effects. These results highlight the multiple processes underlying pain management, and suggest that the neural representations of others' states and one’s errors play a key role in individual treatment decisions. Neuroscience models of social cognition and decision-making are a powerful tool to explain clinical behaviour and might be used to guide future educational programs to improve pain management in ED.

Publication
British Journal of Anaesthesia, 123, e284-e292

Download Supplementary Information.

Corrado Corradi˗Dell'Acqua
Corrado Corradi˗Dell'Acqua
Neuroscientist - Cognitive Psychologist - Data Scientist

Related