Comparative analysis of multi-agent AI systems versus single-agent and human expert approaches in optimizing occupational health triage outcomes and resource allocation

Occupational health triage is a step-by-step process used to check how urgent and serious workplace health problems are. This helps decide who needs medical care first and which health experts should handle each case. Triage can be hard because there are many types of injuries and illnesses at work, workers come from different backgrounds, and it’s important to keep patients safe while using medical resources wisely.

Medical administrators and IT managers face problems like:

• Making correct triage decisions to avoid delays in care for urgent cases.
• Stopping overtriage, which wastes medical resources and costs more money.
• Using healthcare workers well by matching patient needs with the right expert.
• Managing many triage cases quickly without losing accuracy or patient safety.

Usually, experienced healthcare workers handle these tasks, but human decisions can vary and sometimes be wrong. Also, doing triage by hand takes time and money, so using technology can help.

Multi-Agent AI Systems: An Advanced Approach to Triage

Multi-agent AI systems like OccuTriage bring a new way to improve occupational health triage. Developed by Alok Kumar Sahu and team with healthcare experts, OccuTriage uses many specialized AI agents that think like healthcare professionals.

This system:

• Uses several AI agents that focus on different parts of the decision instead of just one agent. Each agent knows a lot about specific occupational health topics.
• Can access current and accurate health information to improve diagnosis.
• Has a process where AI agents work back and forth to improve triage decisions constantly, making them more correct.

Researchers tested this system on 2,589 occupational health cases and compared it with single-agent AI methods and human experts.

Performance: Multi-Agent AI Versus Single-Agent AI

OccuTriage did better than single-agent AI systems in matching triage decisions to the standard. It had a disagreement rate of 20.16%, while single-agent systems had a 43.05% rate.

This shows that using many specialized agents makes decisions more steady and reliable. Also, having updated knowledge helps improve the accuracy.

Comparison with Human Expert Performance

Compared to experienced health professionals, OccuTriage matched or did better. Human experts had a disagreement rate of 25.11%, higher than OccuTriage’s 20.16%.

The system also lowered missed urgent cases, called under-triage:

• Under-triage for deciding appointment types dropped to 9.84%.
• Under-triage for choosing the right assessors was 3.1%.

These low numbers mean OccuTriage improves safety by catching urgent cases more often. Medical administrators can trust these results to make clinics work better and keep patients satisfied.

Optimizing Resource Allocation with Multi-Agent AI

Health services often find it hard to assign limited healthcare workers and special assessments well. Multi-agent AI helps by sorting cases and assigning the correct experts. For example, some cases need occupational medicine doctors, others can be handled by nurses or assistants.

With OccuTriage, administrators can:

• Schedule appointments efficiently based on urgency and complexity.
• Send cases to the right clinicians, lowering wait times and unnecessary doctor visits.
• Manage busy clinics by automating simple triage tasks so clinicians can focus on hard cases.

This reduces costs and helps workers get the care they need on time.

AI and Workflow Integration in Occupational Health Practices

Using AI triage systems like OccuTriage can automate many tasks in healthcare settings. In hospitals and health clinics, this means faster patient check-in, quicker responses, and less paperwork.

Automation benefits include:

• AI answering phones and handling patient questions without waiting for a person.
• Managing appointment schedules in real time based on triage results to give priority to urgent cases.
• Helping with note-taking and report creation to reduce paperwork and let clinicians spend more time with patients.
• Protecting patient data by using strong security methods and following privacy laws like HIPAA.

IT managers get smoother patient interactions and lower costs. Administrators have clearer views of patient flow and case priorities.

Challenges and Considerations for U.S. Healthcare Settings

Even with benefits, there are challenges to using multi-agent AI systems:

• Protecting patient data is very important. Systems must follow laws and use encryption and controls.
• AI models could have biases that affect fairness. Regular checks are needed.
• AI must work well with existing computer systems like electronic health records and scheduling tools to avoid problems.
• Staff need training to trust and use AI properly. AI should help doctors, not replace them.
• There are rules from FDA and states that affect how AI can be used in healthcare.

Future Outlook: Multi-Agent AI in Occupational Health in the United States

As AI grows, it will likely play a bigger role in occupational health triage. Systems like OccuTriage show how AI can combine careful machine work with healthcare knowledge.

For healthcare decision-makers, these systems offer:

• Safer and more accurate patient triage.
• Better use of resources to save time and money.
• Happier patients from faster and suitable care.
• The ability to handle more cases without losing quality.

Hospitals and clinics using AI for front-office and triage work may gain advantages by lowering expenses and improving workplace health management.

Summary

Multi-agent AI systems give healthcare administrators and IT managers in the U.S. a strong way to improve occupational health triage. They do better than single-agent AI and human experts with more accurate decisions, fewer missed urgent cases, and better use of resources. AI-supported automation also helps healthcare operations run smoother and respond faster, making care better for workers.