Developing a deep learning system to drive the work of the critical care outreach team

BackgroundCare of patients at risk of deterioration on acute medical and surgical wards requires timely identification, increased monitoring and robust escalation procedures. The critical care outreach role brings specialist-trained critical care nurses and physicians into acute wards to facilitate these processes. Performing this role is challenging, as the breadth of information synthesis required is both high and rapidly updating. We propose a novel automated watch-list to identify patients at high risk of deterioration, to help prioritise the work of the outreach team. ResultsThis system takes data from the electronic medical record in real-time and creates a discrete tokenized trajectory, which is fed into a recurrent neural network model. These models achieve an AUROC of 0.928 for inpatient death and 0.778 for unplanned ICU admission (within 24 hours), which compares favourably with existing early warning scores and is comparable with proof of concept deep learning systems requiring significantly more input data. ConclusionsBased on these results, we can conclude that it is technically feasible to build a set of predictive models that meet the needs of the critical care outreach role, based on a limited set of real-time clinical data.