Implementing a system for the real-time risk assessment of patients considered for intensive care - Simarjot S. Dahella; James S. Briggs; Paul Coombes; Nazli Farajidavar; Paul Meredith; Timothy Bonnici; Julie L. Darbyshire; Peter J. Watkinson (BMC Medical Informatics and Decision Making, 2020)
Abstract Background Delay in identifying deterioration in hospitalised patients is associated with delayed admission to an intensive care unit (ICU) and poor outcomes. For the HAVEN project (HICF ref.: HICF-R9–524), we have developed a mathematical model that identifies deterioration in hospitalised patients in real time and facilitates the intervention of an ICU outreach team. This paper describes the system that has been designed to implement the model. We have used innovative technologies such as Portable Format for Analytics (PFA) and Open Services Gateway initiative (OSGi) to define the predictive statistical model and implement the system respectively for greater configurability, reliability, and availability. Results The HAVEN system has been deployed as part of a research project in the Oxford University Hospitals NHS Foundation Trust. The system has so far processed > 164,000 vital signs observations and > 68,000 laboratory results for > 12,500 patients and the algorithm generated score is being evaluated to review patients who are under consideration for transfer to ICU. No clinical decisions are being made based on output from the system. The HAVEN score has been computed using a PFA model for all these patients. The intent is that this score will be displayed on a graphical user interface for clinician review and response. Conclusions The system uses a configurable PFA model to compute the HAVEN score which makes the system easily upgradable in terms of enhancing systems’ predictive capability. Further system enhancements are planned to handle new data sources and additional management screens.