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snap40’s solution, Senda, aims to prevent avoidable patient health deterioration, in hospital and in the community. Senda is an advanced machine learning software platform, which consumes data from a variety of different sources, including continuous vital signs from snap40's own wearable device and electronic health records. Senda analyses the consumed data in real-time to calculate a probability of future health deterioration, allowing oversight across the emergency department of patient risk and enabling better planning and data-driven admission decisions. When the probability of deterioration reaches a threshold, Senda issues immediate calls to action, bringing healthcare to patients pro-actively.

The SortED tablet prototype is a comprehensive toolkit to help nurses perform faster, safer and more efficient initial assessments of Emergency Department (ED) patients. It aims to mimic the Doctor's Rapid Assessment and Treatment approach by getting investigations started earlier and early treatments and procedures underway. Ready for clinical testing, 'Sorted Dashboard' is a module for manages which helps predict 'meltdown' and other events, allowing resources to be prioritised. 'Dashboard' is at an earlier stage. In Phase 2 these tools will be integrated with ED and Hospital systems.

Ubisense technology accurately locates the position of tagged items within an indoor environment, monitoring interactions between these objects and utilising the information to drive software applications. The technology is currently used in challenging manufacturing environments to monitor, control and optimise complex assembly processes. There are a number of surprising parallels between the ad-hoc, non- linear, processes used in off-line rework and those found in A&E Departments. Therefore this project explores whether the same proven techniques, employed by over half of the major automotive companies, could be used to generate similar benefits within A&E for patient flow and admissions management.