Development and implementation of an artificial intelligence-enhanced care model to improve patient safety in...

Early detection of critical events in hospitalized patients improves clinical outcomes and reduces mortality rates. Traditional early warning score systems, such as the National Early Warning Score 2 (NEWS2), effectively identify at-risk patients. Integrating artificial intelligence (AI) could enhance the predictive accuracy and operational efficiency of such systems. The study describes the development and implementation of an AI-enhanced early warning system based on a modified NEWS2 scale with laboratory parameters (mNEWS2-Lab) and evaluates its ability to improve patient safety in hospital wards.

For this retrospective cohort study of 3,790 adults admitted to hospital wards, data were collected before and after implementing the mNEWS2-Lab protocol with and without AI enhancement. The study used a multivariate prediction model with statistical analyses such as Fisher's chi-square test, relative risk (RR), RR reduction, and various AI models (logistic regression, decision trees, neural networks). The economic cost of the intervention was also analyzed.

The mNEWS2-Lab reduced critical events from 6.15% to 2.15% (RR, 0.35; P<0.001), representing a 65% risk reduction. AI integration further reduced events to 1.59% (RR, 0.26; P<0.001) indicating a 10% additional risk reduction and enhancing early warning accuracy by 15%. The intervention was cost-effective, resulting in substantial savings by reducing critical events in hospitalized patients.

The mNEWS2-Lab scale, particularly when integrated with AI models, is a powerful and cost-effective tool for the early detection and prevention of critical events in hospitalized patients.