Emergency departments across the United States are facing ongoing overcrowding. This issue has worsened in recent years. Overcrowding causes longer patient wait times and strains clinical resources, which affects the quality of care. Pearce et al. (2023) found that overcrowding adds social, economic, and environmental burdens, including increased energy use from extended hospital stays and repeated visits. These factors raise healthcare costs and are linked to higher patient morbidity and mortality rates.
The triage process, which prioritizes patients based on their condition severity, is heavily affected by these conditions. Relying on nurses to manually manage triage increases the chances of mistakes, delays in identifying critically ill patients, and inconsistent assessments. This problem is especially severe during busy times when patient numbers grow sharply.
Traditional triage methods have limits such as staff fatigue, inconsistent protocols, and difficulties handling large patient volumes. These issues reduce triage accuracy and increase the risk of poor clinical outcomes when quick intervention is necessary. For these reasons, introducing AI and self-service technologies (SSTs) in emergency departments may help lessen these challenges and improve efficiency.
AI-driven triage systems use machine learning and natural language processing (NLP) to analyze patient data in real time, including vital signs, medical history, and symptoms. This allows for more accurate and consistent risk assessments. Such systems can automate patient prioritization and reduce waiting times, particularly during mass casualty incidents or peak periods.
AI-based SSTs, like check-in kiosks or mobile apps, let patients provide initial information themselves. This reduces paperwork for nursing staff and supports smoother workflows, saving critical triage time and improving patient flow. In research involving 159 triage nurses, Panzhang Wang and colleagues found that AI tools shortened registration times and helped nurses concentrate on clinical decisions over administrative tasks.
Other benefits of AI include:
Despite these benefits, challenges remain. Poor data quality and algorithmic bias can affect assessments. Clinician trust in AI tools is still limited, which can reduce adoption. Ethical concerns such as transparency and patient consent also complicate implementation.
Studies repeatedly show the need for strong human oversight in AI-driven triage. Fully automated systems without human review raise issues related to accuracy, safety, and trust. Nurses and other medical professionals are crucial for interpreting AI results, verifying assessments, and using clinical judgment to prevent errors or misclassifications.
The study by Panzhang Wang highlights that AI should support rather than replace human judgment. It presents a dual-server model combining queueing theory and AI to balance efficiency with oversight, ensuring smooth interaction between technology and nursing workflows.
Human oversight is key in several areas:
One common concern among triage nurses is the fear that AI will replace healthcare workers, sometimes called the “perceived substitution crisis.” This fear can affect their willingness to accept AI, even if they see its potential benefits. Such apprehension can slow AI adoption and limit its effectiveness in emergency departments.
Healthcare leaders need to communicate clearly that AI is meant to assist nurses, not replace them. Framing AI as a support tool can reduce resistance. Training programs and involving frontline staff in AI design and rollout help ease worries.
Successful AI implementation depends on how well the technology fits existing nursing workflows. Panzhang Wang’s study points out the following:
Emergency department management should ensure AI solutions adapt to workflows rather than disrupt them. User-friendly interfaces designed with clinical input are important. Providing help desks and regular training supports ongoing effective use.
Beyond triage, AI-driven front-office automation can improve emergency department operations. Companies like Simbo AI offer phone automation and AI answering services designed for healthcare. These tools make patient communication, appointment scheduling, and call handling more efficient, easing the load on administrative staff.
AI phone automation can:
Integrating AI phone services with triage systems creates a smoother patient experience from first contact to care. For U.S. providers, these technologies also help reduce bottlenecks common in busy emergency departments while ensuring compliance with privacy laws like HIPAA.
Automating routine front-office tasks reduces errors in data collection and registration, preparing the way for more precise AI-assisted triage. Linking AI tools with electronic health records supports seamless information sharing and better care coordination.
When implementing AI-driven triage, administrators and IT managers should consider these points:
AI-driven triage adoption in U.S. emergency departments presents an opportunity to address ongoing issues with overcrowding, patient flow, and care quality. AI can improve risk assessment and workflow, but success depends on combining technology with human oversight.
Developing better algorithms to reduce bias, integrating wearable devices for continuous data, and creating ethical guidelines for diverse patient groups are important steps forward. Educating clinicians and maintaining open conversations about AI’s benefits and limits will support lasting adoption.
As demand for emergency care grows, AI will likely become a regular part of service delivery. However, it is the collaboration of automated tools and experienced medical staff that will lead to better patient outcomes.
By focusing on clear communication, staff support, and fitting AI to existing workflows, U.S. emergency departments can use AI-driven triage systems while respecting clinical expertise and addressing the needs of patients. Providers offering AI-based front-office automation contribute to this change by helping healthcare facilities manage higher patient volumes with consistent quality and responsiveness.
The study explores the attitudes and intentions of emergency department (ED) staff, specifically triage nurses, regarding the adoption of artificial intelligence (AI) and self-service technologies (SSTs) for improved triage processes.
ED overcrowding affects sustainability by increasing energy consumption, healthcare costs, and morbidity, while also decreasing patient satisfaction and potentially increasing violence toward staff.
The triage process consists of three phases: pre-hospital triage, triage at the scene, and triage upon arrival at the ED.
SSTs, through kiosks or patient smartphones, allow patients to self-check-in, reducing the administrative burden on triage nurses and potentially saving time.
Factors include task-technology fit, perceived explainability of AI, and facilitating conditions. These elements significantly influence nurses’ willingness to adopt AI technologies.
The perceived substitution crisis negatively affects nurses’ behavioral intentions to adopt AI, potentially reducing their acceptance of these technologies in triage.
AI can streamline triage by making initial assessments via SSTs, reserving manual triage for cases that require human intervention, thereby improving efficiency.
Human oversight is critical to validate AI-driven decisions, ensuring that triage outcomes remain accurate and trustworthy, especially in critical care situations.
The study’s findings urge policymakers to design technology that aligns with nurse workflows, support transparency in AI decision-making, and provide resources for effective implementation.
It introduces a novel framework linking task-technology fit and AI adoption from the perspective of triage nurses, highlighting the need for sustainable healthcare outcomes.
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