Schematic showing how EASUL-based tools were configured and utilized in two different ways. For research, quality and service improvement using static data sets and Python scripting and analytics. Creation of a prototype CDS tool through integration of outputs/results into a clinical information system. *ADT=hospital admissions, discharges and transfers. In all cases, a Plan is initially defined using Python classes.
When tallied, EASUL risk assessment matched with the SPIN teams 49.4 % of the time. EASUL never rated any patient as low risk who had been rated as high risk by the clinical team. EASUL also identified 57 cases which, when reviewed by researchers, should have been rated as high risk but only recorded as low or moderate by clinical staff.
As a result, EASUL could be adjusted to suit the needs of a variety of clinical settings. It is also designed to automatically adjust its calculation in case of missing data, meaning it could provide robust and relevant information to clinical staff in a variety of different situations.