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The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pragmatic trial with cluster-randomization of 74 clinical units (37 intervention; 37 usual care) across 2 health systems. Eligible adult hospital encounters were included. We tested if outcomes differed between patients whose care teams were and patients whose care teams were not informed by the CONCERN EWS. Coprimary outcomes were in-hospital mortality (examined as instantaneous risk) and length of stay. Secondary outcomes were cardiopulmonary arrest, sepsis, unanticipated intensive care unit transfers and 30-day hospital readmission. Among 60,893 hospital encounters (33,024 intervention; 27,869 usual care), intervention group encounters had 35.6% decreased instantaneous risk of death (adjusted hazard ratio (HR), 0.64; 95% confidence interval (CI), 0.53-0.78; P < 0.0001), 11.2% decreased length of stay (adjusted incidence rate ratio, 0.91; 95% CI, 0.90-0.93; P < 0.0001), 7.5% decreased instantaneous risk of sepsis (adjusted HR, 0.93; 95% CI, 0.86-0.99; P = 0.0317) and 24.9% increased instantaneous risk of unanticipated intensive care unit transfer (adjusted HR, 1.25; 95% CI, 1.09-1.43; P = 0.0011) compared with usual-care group encounters. No adverse events were reported. A machine learning-based EWS, modeled on nursing surveillance patterns, decreased inpatient deterioration risk with statistical significance. ClinicalTrials.gov registration: NCT03911687 .
Rossetti et al. (Wed,) studied this question.