Healthcare triage is a tough job. It means deciding which patients need care first based on their symptoms and risks. In busy clinics, mistakes or delays in triage can cause problems. Some patients may not get care on time, or resources might not be used well. This affects both patient safety and how well the clinic runs.
Large language models, like GPT, are AI systems trained on lots of text. They can understand and create human-like language. People suggest using these models to help healthcare workers. But normal LLMs have problems in healthcare:
To fix these problems, researchers work on improving LLMs by adding retrieval augmentation. This means the AI can look up specific medical data from trusted sources while making decisions.
Retrieval augmentation lets the AI add information from outside sources to what it already knows. This helps the AI get accurate and recent medical facts. In healthcare triage, this means the AI can better:
A study in 2025 by Yingshuai Wang, Yanli Wan, and Hongpu Hu looked at a way to improve LLMs with Retrieval Augmented Generation (RAG). They cleaned up medical texts, clarified terms, and removed extra data. The improved model did better than regular LLMs on tests about symptoms, diagnoses, and care priorities. The tests used the CCKS-TCMBench dataset, a common set of medical information.
Some key improvements they made:
These changes help lower errors in medical AI, which is very important for patient safety.
OccuTriage is an example of an AI system using retrieval-augmented LLMs. It works in occupational health, which deals with work-related health issues. Good triage here is important for employee health and safety at work.
Alok Kumar Sahu and team created OccuTriage with several special LLMs. They combined them with data from occupational health knowledge bases. This setup copies how healthcare workers think by adding rules, symptoms, and treatments used for workers’ health.
They tested OccuTriage on 2,589 job-related health cases and found:
This shows that specialized LLMs with targeted knowledge can improve triage accuracy and keep patients safe. OccuTriage uses two-way decision processes to learn and improve like humans do.
People who run medical clinics in the U.S. are under pressure to keep care good with fewer resources. There are many patients, rules to follow, and staff shortages. Good triage systems are needed to help with this. AI systems that give accurate first assessments can help by:
Clinics using automated front-office systems can add retrieval-augmented LLMs to reduce mistakes during patient calls and appointment booking. This allows more natural and helpful responses instead of just scripted answers.
Medical offices are starting to use AI automation for tasks like answering phones, booking, and answering patient questions. Simbo AI is an example of a company doing this with phone systems using AI.
By adding retrieval-augmented LLMs, systems like Simbo AI can:
Healthcare IT managers find these tools useful because they keep medical context in mind for safe patient contact. Clinic managers also benefit from easier workflows, fewer repeat tasks, fewer no-shows, and happier patients.
Using retrieval-augmented LLMs in U.S. clinics comes with things to think about:
These points affect how AI tools are chosen and managed. Doctors, staff, and IT teams need to work together.
One big benefit of retrieval-augmented LLMs like OccuTriage is better use of limited healthcare resources. Making fewer mistakes and missing fewer urgent cases means:
U.S. healthcare groups working with tight budgets and space can use these AI tools to keep care standards without making staff or facilities too busy.
Trust is a big reason why some U.S. clinics are slow to use AI. Research on retrieval augmentation and projects like OccuTriage shows that AI can be made safer by connecting it to real medical data. This lowers wrong or made-up info.
Hallucination, where AI makes up false content, is a big risk in medical use. Retrieval augmentation helps the AI check facts from trusted sources, making triage safer and helping doctors trust the system.
Also, systems like OccuTriage use back-and-forth decision steps. This lets AI improve answers over time, like human experts do. Such design helps users see how decisions are made and feel confident.
AI in U.S. healthcare triage will likely grow in these ways:
These steps will help U.S. clinics handle patient needs, keep care safe, and run smoothly.
Using retrieval augmentation with domain-specific medical knowledge helps large language models work better for healthcare triage. It lets AI check precise and current clinical facts, lowers false information, and copies how doctors think. This improves decision accuracy and the use of medical resources.
Systems like OccuTriage show how AI can help in job-related health triage and other medical areas. For clinic leaders and IT managers in the U.S., adding retrieval-augmented LLMs in automated front-office tasks can improve safety, patient experience, and how clinics work.
More research, teamwork, and careful use of this technology will be needed to get the best results in real clinics.
OccuTriage is an AI agent orchestration framework designed for occupational health triage prediction that systematically evaluates and prioritizes workplace health concerns to recommend appropriate care and interventions.
It uses specialized large language model (LLM) agents combined with retrieval augmentation enhanced by domain-specific knowledge and a bidirectional decision-making architecture to mimic healthcare experts’ thought processes.
It tackles critical triage challenges by improving decision accuracy, reducing discordance rates, and optimizing resource allocation while maintaining patient safety.
The framework was experimentally evaluated on 2,589 occupational health cases to measure performance against baseline single-agent models and human expert judgments.
OccuTriage achieved a 20.16% average discordance rate, significantly better than the 43.05% discordance rate seen with baseline single-agent approaches.
It matches or exceeds human expert performance, which had a discordance rate of 25.11%, demonstrating high efficacy in triage decisions.
The system reduces under-triage to 9.84% for appointment decisions and 3.1% for assessor type decisions, enhancing patient safety by minimizing missed urgent cases.
Retrieval augmentation enriches the LLM agents with accurate, context-relevant occupational health information, improving diagnostic precision and decision-making quality.
It enables dynamic interaction between AI agents and data inputs, facilitating iterative refinement of triage decisions for better accuracy and safety.
By accurately prioritizing cases and reducing under-triage, OccuTriage ensures that medical appointments and assessor types are assigned efficiently, thereby optimizing healthcare resources.
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