Observational studyBMC medical informatics and decision making2025
From prediction to action: a retrospective observational study on the real-world implementation of Critical Interventions (CrIs), an AI-based clinical decision support system changing clinical behavior in the emergency department.
Observational study in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- An exploratory mixed-methods study using the ELICIT framework to identify stakeholder-informed implementation strategies for a population health platform.BMC health services research · 2026Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundOptimal emergency department (ED) care for critically ill patients relies on prompt recognition and initiation of necessary interventions. The Critical Interventions (CrIs) system was designed to predict the need for urgent procedures, including arterial line insertion, oxygen therapy, high-flow nasal cannula (HFNC), intubation, and inotropes or vasopressors, based solely on triage data. This study aimed to evaluate the effectiveness of the CrIs system in enhancing clinical decision-making and reducing time to intervention, as well as to assess user satisfaction post-implementation.
methodA retrospective observational study was conducted at a tertiary hospital in South Korea, comparing time to interventions and outcomes between pre- and post-implementation phases of CrIs. The system's predictive performance was analyzed using the area under the receiver operating characteristic curve (AUROC). Additionally, user feedback was collected through surveys.
resultsThe study included 37,632 ED visits, with notable predictive performance for CrIs across various interventions (AUROC: 0.879 for arterial lines, 0.906 for oxygen therapy, 0.95 for HFNC, 0.928 for intubation, and 0.88 for inotropes/vasopressors). Post-implementation, significant reductions were observed in the median time to intervention for A-lines, oxygen therapy, and HFNC. Survey responses highlighted overall satisfaction with CrIs, though suggestions for interface improvements were noted.
conclusionThe CrIs system demonstrated robust predictive accuracy and significantly reduced time to critical interventions, enhancing ED operational efficiency. Future enhancements should focus on improving user interface and communication features to maximize system utility. CLINICAL TRIAL NUMBER: Not applicable.
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