Traditional ED triage depends largely on clinical judgment, structured protocols like the Emergency Severity Index (ESI), and the experience of healthcare professionals to prioritize patients. This process can be subjective and inconsistent, especially during busy times or mass casualty situations.
AI-driven triage uses algorithms based on machine learning and natural language processing (NLP) to analyze real-time patient data such as vital signs, medical history, symptoms, and unstructured clinician notes to assist or automate patient prioritization. It identifies patterns in large clinical datasets that may be missed by human evaluation.
Logistic regression is still commonly used in triage models because it is interpretable and reliable. The area under the receiver operating characteristic curve (AUC) is a common metric to assess how well predictive models identify patients who need urgent care or critical interventions.
Key variables for AI models often include patient age, gender, vital signs (pulse, blood pressure, respiratory rate), and chief complaints. These inputs allow AI systems to predict outcomes such as hospital admissions, ICU needs, length of stay, and mortality risks. This supports care teams in making triage decisions based on both data and clinical judgment.
Hospital administrators and medical practice owners in the United States need clear, standardized measures to evaluate AI triage systems. These measures should cover clinical impact, operational efficiency, and economic outcomes. The following metrics are important to consider:
Despite benefits, AI triage tools are not yet broadly adopted across US institutions due to several challenges.
Data Quality and Integration
AI depends on large, accurate datasets. Fragmented Electronic Health Records (EHRs), inconsistent documentation, and missing information limit the effectiveness of AI predictions. Investments in data standards and ensuring interoperability between AI systems and hospital EHRs are needed.
Algorithmic Bias and Equity
AI models trained on unrepresentative data risk perpetuating bias. US emergency departments serve diverse populations, so addressing bias is critical. Regular retraining with local demographic data and transparent validation help reduce this risk.
Clinician Education and Workflow Integration
Healthcare workers need training on how AI systems work and their limitations. Without this, clinicians may distrust AI or resist changes. Including frontline staff in selecting, customizing, and testing tools helps ease adoption.
Ethical and Legal Frameworks
Laws such as HIPAA regulate data privacy. Liability for automated decisions also raises ethical questions. Institutions should establish policies and consult legal experts to ensure compliance and set clear guidelines.
Validation and Continuous Monitoring
Many studies lack thorough validation before AI is used clinically. Multi-phase validation including retrospective assessment, real-time pilots, and ongoing monitoring helps ensure reliability over time.
Combining AI triage with workflow automation can improve operational efficiency in US EDs. AI can do more than scoring priority; it can handle routine administrative tasks that otherwise take staff time.
Automated Phone Answering and Scheduling
AI-driven systems can manage patient calls by screening, offering basic triage guidance, directing urgent cases to nurse triage lines, and scheduling appointments based on urgency. This reduces administrative delays and lets clinicians focus on patient care.
Real-time Data Entry and Alerts
AI can transfer patient data and vital signs automatically into EHRs, reducing manual errors and speeding data availability. It can also trigger alerts for abnormal findings, notifying care teams promptly.
Resource Coordination
Automation can manage logistics such as bed availability, staffing, and equipment readiness by aligning with AI triage outputs. This ensures resources are ready for critical patients and helps reduce wait times.
Documentation and Compliance
Automated reporting aligned with regulations lowers the clerical load on clinicians. It also supports tracking outcomes and quality improvement.
Medical practice administrators, facility owners, and IT managers in the US face pressures that vary by location and setting. Differences between large cities and rural hospitals affect AI triage adoption.
Urban EDs handle high patient volumes, frequent mass casualty events, and complex social factors. AI systems in these settings need to manage rapid data processing and reduce crowding while supporting fast critical care triage.
Rural EDs often have less experienced staff who may benefit from AI decision support providing evidence-based prioritization and referral advice. Remote AI triage can also connect smaller facilities with larger centers to improve transfers and resource use.
Both environments require AI platforms that integrate with common US EHR vendors like Epic and Cerner to avoid workflow disruptions. Compliance with federal and state rules on data security and software validation is also necessary.
Budget constraints influence choices too. Smaller hospitals might prefer cloud-based AI with scalable options and lower initial costs. Larger systems may invest in internal AI development and integration teams.
Success of AI triage systems in US EDs depends on solid research to validate benefits and improve implementation across different healthcare settings.
Future research should:
Partnerships between AI technology companies and healthcare providers can speed up deployment by combining expertise in clinical workflows and AI tools.
AI use in emergency department triage offers a way to address ongoing challenges related to resource limits and inconsistent patient prioritization. US medical administrators and hospital leaders should focus on validating performance, ethical use, and workflow integration to ensure AI-driven triage improves care without sacrificing quality and safety. As evidence grows and technology advances, AI is positioned to become a key part of emergency care in the US across various healthcare settings.
The motivation is to enhance triage systems beyond traditional medical knowledge by leveraging hidden patterns in large volumes of clinical data. Intelligent techniques can provide health professionals with objective criteria, improving patient care quality in the ED.
The primary objectives were to assess the contributions of intelligent CDSS to ED care quality and to identify the challenges encountered in their implementation.
A standard scoping review method was applied, involving manual searches across six digital libraries using customized queries to find relevant literature on ED triage and intelligent systems.
Logistic regression was the most frequently used technique for model design, with the area under the receiver operating curve (AUC) commonly used as a performance measure.
Key variables included patients’ age, gender, vital signs, and chief complaints, which were frequently utilized for predicting outcomes in triage.
Validated CDSS improved health professionals’ decision-making, which in turn led to better clinical management and improved patient outcomes.
More than half of the reviewed studies lacked an implementation phase, indicating the need for further validation and performance measure definitions for effective use in triage.
CDSS can predict patient prioritization, the need for critical care, hospital or ICU admissions, ED Length of Stay (LOS), and patient mortality from data available at triage.
Implementing AI can enhance the accuracy and efficiency of triage systems, leading to better prioritization of critical cases, improved patient flow, and optimized healthcare resources.
Future research should focus on validating CDSS implementations in ED settings and defining key performance measures to demonstrate the improvement impact on patient care.
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