Enhancing Clinical Decision-Making in Rapid Response Systems: Integrating AI-Based Predictions and Early Pattern Clustering

Abstract Background The AI solution used in the hospital's Rapid Response System is designed to predict critical events such as cardiopulmonary resuscitation (CPR), unexpected deaths, and unanticipated transfers to the intensive care unit (ICU) in real time. However, given the limitations perceived by the clinical staff regarding its effectiveness, we considered whether it could provide additional information to support decision-making. Objective This study supplemented decision-making support by utilizing predictive patterns produced by a critical event AI solution. Methods This study assessed the predictive performance of three decision-making strategies using data from Ilsan Hospital (n = 292,981; admissions between April 1, 2019, and February 28, 2024): a cut-off value–based strategy utilizing AI solution outputs, a clustering-based strategy categorizing patients into 10 classes according to early prediction patterns during the first five days of hospitalization, and a hybrid strategy combining both approaches. Results Although the AI solution–based decision-making approach yielded the highest AUROC (74.54%), its AUPRC (6.21%), F₁ score (17.86), and recall (10.72%) were relatively low. Including Classes 2, 5, and 6 along with AI solution further improved recall to 32.4% and the F₁ score or 34.54% although the AUROC decreased to 66.70%. Conclusions Supplementing the existing AI solution–based decision-making process with pattern-based predictions over a defined observation period may enhance the practical utility and real-world applicability of clinical decision-making in hospital settings.