Li B, Zhang S, Hoover S, Arnold R, Capan M
IEEE J Biomed Health Inform 2018 Oct;
PMID: 30295635
Abstract
While physiological warning signs prior to deterioration events during hospitalization have been widely studied, evaluating clinical interventions, such as Rapid Response Team (RRT) activations, based on scoring systems remains an understudied area. Simulation of physiological deterioration patterns represented by scoring systems can facilitate testing different RRT policies without disturbing care processes. Christiana Care Early Warning System (CEWS) is a scoring system developed at the study hospital to detect the physiological warning signs and inform RRT activations. The objective of this study is to evaluate CEWS-triggered RRT policies based on patient demographics and policy structures. Using retrospective data derived from a subset of Electronic Health Records between December 2015 and December 2016 (6,000 patients), we developed a microsimulation model with integrated regression analysis to compare RRT policies on subpopulations defined by age, gender and comorbidities to find score thresholds that result in lowest percent of time spent above critical CEWS values. Policies that rely on average scores were more sensitive to threshold changes compared to policies that rely on current value and change in CEWS. Policy using score threshold 10 provided the lowest percentage of time in critical condition for majority of subpopulations. The proposed model is a novel framework to simulate individual deterioration patterns and systematically evaluate RRT policies based on their impact on health conditions. Our work highlights the importance of integration of data-driven models into personalized care, and represents a significant opportunity to inform biomedical and health informatics research on designing and evaluating EWS-based clinical interventions.