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Introducing the DM-P approach for analysing the performances of real-time clinical decision support systems, Knowledge-Based Systems (2020), doi:

A new methodology is proposed to compare database performance for streams of patient respiratory data from patients in an intensive care unit. New metrics are proposed through which databases may be compared both for this and similar streaming applications in the domain of the Internet of Things. Studies are reported using simulated patient data for four freely available databases. The statistical technique of non-parametric bootstrapping is used to minimise the total running time of the tests. We report mean values and bias corrected and accelerated confidence intervals for each metric and use these to compare the databases. We find that, among the four databases tested, ScaleDB is an optimum database technology when handling between 200 and 800 patients in this application, while PostgreSQL performs best outside of this range. Comparing the non-parametric bootstrapping method to a complete set of tests shows that the two approaches give results differing by a few percent.

This paper introduces the real-time Healthcare 4.0 system, the VILIAlert system and a new approach that we propose for the robust assessment of it's performance. The VILIAlert system alerts clinicians when a patient's tidal volume value rises above the clinically accepted level of 8 ml/kg as beyond this point (> 8 ml/kg), a patient is considered high risk of permanent damage to their lungs. In order to ensure success with the VILIAlert system, the ideal scenario is to ensure that as soon as patients in the Intensive Care Unit experience tidal volume values beyond the 8 ml/kg level, a clinical intervention can be carried out so to minimise the risk of patients ever having permanent damage. The approach has been implemented in the Intensive Care Unit at the Royal Victoria Hospital Belfast, Northern Ireland demonstrating the potential for such an approach to be used across all hospitals in the region.