Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retrospective cohort study of 1,013 hospital encounters with unanticipated ICU transfers from a multi-site pragmatic randomized controlled trial, we assessed the influence of CONCERN EWS on in-hospital mortality and length of stay following unanticipated ICU transfers. Chi-square tests, t-tests, multivariate logistic regression, and generalized linear models were used. Our findings showed that patients who had unanticipated ICU transfers from acute care units with CONCERN EWS had a lower in-hospital mortality rate and a shorter average hospital stay than those transferred from units receiving usual care. These results suggest that CONCERN EWS enhances shared situational awareness for care teams, improves communication, and effectively facilitates timely interventions, thereby streamlining care processes and improving patient outcomes.