The effects of clinical task interruptions on subsequent performance of a medication pre-administration task

There is a surge of research exploring the role of task interruptions in the manifestation of primary task errors both in controlled experimental settings, and safety critical workplaces such as healthcare. Despite such research providing valuable insights into the disruptive properties of task interruption, and, the importance of considering the likely disruptive consequences of clinical task interruptions in healthcare environments, there is an urgent need for an approach that best mimics complex working environments such as healthcare, whilst allowing better control over experimental variables with minimal constraints. We propose that this can be achieved with ecologically sensitive experimental tasks designed to have high levels of experimental control so that theoretical as well as practical parameters and factors can be tested. We developed a theoretically and ecologically informed procedural memory-based task - the CAMROSE Medication Pre-Administration Task. Results revealed significantly more sequence errors were made on low, moderate and high complex conditions compared to no interruption condition. There was no significant difference in non-sequence errors. Findings reveal the importance of developing ecologically valid tasks to explore non-observable characteristics of clinical task interruptions. Both theoretical and possible practical implications are discussed

Intrinsic gain modulation and adaptive neural coding

In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate vs current (f-I) curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.Comment: 24 pages, 4 figures, 1 supporting informatio