Understanding the Factors Influencing Nurses’ Acceptance of AI in Triage and the Effects of Perceived Job Substitution

Emergency departments (EDs) in the United States often get too crowded. This causes patients to wait longer and nurses to have more work. It also raises healthcare costs and can lead to more serious health problems. Studies show that busy EDs use more energy and produce more waste because patients stay longer and visit multiple times. This is a concern for keeping healthcare sustainable.
Triage is the way patients are sorted by how urgent their cases are. It helps manage how patients flow through the ED. But when many patients come in, manual triage can be hard to handle. Nurses get tired, may not always follow assessment rules evenly, and struggle to keep up during busy times. This can cause mistakes in deciding who needs care first.
AI-powered self-service technologies (SSTs) provide a partial solution. Patients can check in themselves using kiosks or their smartphones and give basic information before a nurse sees them. These AI tools help spot critical cases more quickly.
For healthcare leaders in the U.S., adding AI-driven SSTs can lower wait times, ease nurses’ workload, and improve care in emergency departments.

Nurses’ Attitudes Toward AI Adoption in Triage

Research with 159 triage nurses shows a few key points that affect how nurses accept AI in emergency triage:

• Task-Technology Fit: This means how well the AI works with nurses’ current tasks and workflows. Nurses like AI better when it fits smoothly and does not interrupt their work. For example, if AI helps with routine registration, nurses can spend more time with patients.
• Perceived Explainability: Nurses trust AI more if they understand how it makes decisions. Clear explanations help nurses feel comfortable using AI along with their own judgment.
• Facilitating Conditions: Support from the organization, like training and technical help, affects nurses’ willingness to use AI. Without support, even good AI tools may face resistance.

Healthcare managers in the U.S. should focus on these factors when they plan to use AI. Putting resources into designing AI that fits nurses’ tasks, improving user experience, and providing enough support can increase acceptance.

The Perceived Job Substitution Crisis and Its Impact

Even with AI’s benefits, many nurses worry AI might take their jobs or cut their duties. This fear is called the perceived job substitution crisis. It lowers nurses’ willingness to use AI, even if they have good feelings about the technology overall.
This worry is strong in the U.S., where there are not enough nurses and many leave their jobs. Nurses working long hours in busy EDs may see AI with doubt. They fear machines will replace important parts of what they do.
Studies show this fear not only lowers the intention to use AI but also weakens the link between positive views of AI and actual use.
So, just giving hospitals AI tools is not enough. Clear and ongoing messages that AI is meant to help nurses, not replace them, are needed. Hospitals should encourage a view of AI as a helper that supports nurses, makes work safer, and does not threaten jobs.

Direct Experience and Media Exposure Influence on AI Acceptance

In China, where EDs also face crowding and staff shortages, studies show that medical staff who actually use AI triage like it more. More experience with AI builds trust. This means that testing AI in small ways before full use in the U.S. could help nurses accept it better.
Also, how much nurses hear about AI in media matters. Professional news, reports, and education can teach staff what AI can and cannot do. This knowledge lowers fears and fixes wrong ideas.
For IT leaders and hospital managers, it is important to add good communication plans when starting AI projects. This helps increase acceptance.

AI and Workflow Automation in Emergency Triage: Enhancing Efficiency in U.S. Healthcare Settings

Using AI in triage is more than automating patient check-in. It involves changing work steps to save time and resources while still being accurate in care.
AI-powered SSTs let patients put in their own details when they arrive. This cuts down paperwork for nurses and lowers wait times. These tools can judge how urgent cases are, alert nurses to critical patients, and make documentation smoother.
Technologically, AI tools must fit with what nurses do. For example:

• Automated Phone Handling and Scheduling: Companies like Simbo AI create phone systems that answer patient calls, book appointments, and handle simple triage questions. This reduces the number of calls nurses or front desk workers must take. Staff then can focus more on clinical work.
• Decision Support Systems: AI can suggest patient priority levels using data patients give. Nurses check these and can change AI decisions when needed to keep patient safety.
• Resource Coordination: AI connected to hospital systems can predict patient load and help assign staff based on the triage data. This helps manage patient flow.

For medical practice leaders in the U.S., introducing these AI tools means carefully mapping workflows and testing usability. The aim is to help nurses, not make their jobs harder.

The Critical Role of Human Oversight and Trust

Even with AI help, humans must remain in control. Nurses have the main role in reading triage information and making final clinical decisions. AI is a tool to support, not replace them.
Trust in AI depends on how clear and open it is. If nurses know what data the AI uses and how it weighs symptoms, they are more likely to trust and use the systems.
Hospitals in the U.S. should run training that helps staff understand how AI works and its limits. Being open lowers resistance and helps AI use last longer.

Implications for Healthcare Policy Makers and IT Leadership in the United States

Research shows that making AI work well in triage depends on how the technology is designed, the culture of the workplace, staff training, and clear messages about AI’s purpose.
For U.S. healthcare leaders, important steps include:

• Design AI to fit nursing workflows. If AI is hard to use or does not fit the real work, nurses will reject it.
• Make AI decision-making clear. Provide easy-to-understand explanations of how AI works.
• Reduce fears of job loss. Explain that AI helps nurses by cutting routine tasks and improving accuracy, not taking over jobs.
• Offer good organizational support with training, technical assistance, and aligned policies.
• Introduce AI slowly through pilot programs and gather feedback to improve it.

IT managers can use AI solutions like Simbo AI’s automation tools to reduce pressure on clinical and admin staff. Automating phone answering and basic triage questions can improve patient experience and free nurses to focus on direct care.

Broader Context: AI Adoption Trends in Healthcare

Studies from China offer a useful comparison. Despite differences in culture and healthcare systems, 77.1% of Chinese medical staff accept AI triage. Almost half prefer only AI triage. Having experience with AI and seeing it in the media improves attitudes.
Concerns about accuracy, ethics, and job loss exist everywhere. But the pandemic showed AI can help reduce infections and manage healthcare demand.
In the United States, where there are nurse shortages and crowded EDs, AI triage and workflow automation offer useful ways to improve care and keep healthcare working well over time.

Summary

Using AI in triage in the U.S. will work best if nurses accept it. Leaders need to address nurse worries about job security, explain how AI works clearly, and make sure AI fits into current clinical routines.
Simbo AI’s front-office automation can help by handling routine tasks and supporting nurses and administrators. Healthcare leaders who consider these points when adding AI will help their hospitals manage emergency care better now and later.