Comparative Analysis of AI Triage Accuracy Versus Experienced Clinician Judgments in Categorizing Urgent and Non-Urgent Patient Cases

AI triage tools use natural language processing and machine learning to study patient symptoms and clinical data. They automatically sort cases by how urgent they are. These tools help clinical staff by making routine checks easier, making triage decisions more consistent, and spotting serious cases faster.

Several recent studies have looked at AI’s role in triage. They tested AI models like ChatGPT (versions 3.5 and 4o) and compared these models to experienced emergency doctors and nurses. These studies used known triage systems such as the Canadian Triage and Acuity Scale (CTAS) and the Manchester Triage System. These systems split patient conditions into different urgency levels.

AI Triage Accuracy Compared to Clinicians

Research shows that AI does well in some parts of triage but also has limits. It can help with decision-making but can’t replace human clinical judgment yet.

Study on ChatGPT’s Ability to Categorize ENT Patient Conditions

A study at the University of Zagreb, led by Dorian Fildor and Mirjana Pejić Bach, tested how well ChatGPT sorts ear, nose, and throat (ENT) patient cases by urgency. They compared the AI’s sorting with that of an experienced hospital doctor. ChatGPT could separate urgent and non-urgent cases moderately well. But it sometimes gave unclear answers. This means AI can’t fully replace clinical experts now.

The study showed that AI can help reduce the workload in triage. This gives healthcare workers more time to focus on patients. But AI needs careful supervision because it might miss some complex clinical details.

ChatGPT-4o Performance in Emergency Triage: A U.S. Context

A U.S. study by Ahmad A. Aalam, MD, tested ChatGPT-4o against four expert emergency doctors. They used 60 emergency situations and the Canadian Triage and Acuity Scale (CTAS). CTAS has five urgency levels:

• Level 1: Resuscitation (immediate intervention needed)
• Level 2: Emergent (assessment within 15 minutes)
• Level 3: Urgent (assessment within 30 minutes)
• Level 4: Less urgent (reassessment within 60 minutes)
• Level 5: Nonurgent (evaluation within 120 minutes)

ChatGPT-4o mostly agreed with the human doctors. It had a Cohen’s kappa score of 0.695, showing good agreement but also room for improvement. At the highest urgency (Level 1), the AI caught all emergency cases correctly (100% sensitivity) and had high accuracy in avoiding false alarms (97.67% specificity). For Level 5 (nonurgent), it also caught all true cases with 100% sensitivity and 93.02% specificity.

But at Level 4 (less urgent), the AI was less accurate, with 50% sensitivity. This shows AI finds it harder to spot moderately urgent cases. This makes sense because these cases can be complex and need a doctor’s detailed understanding.

Dr. Aalam said AI can’t replace doctors yet but is useful in spotting critical patients fast. It can help teams focus on urgent cases and use resources better.

AI versus Human Clinicians: The European Emergency Medicine Study

Another study from Vilnius University Hospital compared ChatGPT 3.5 to six emergency doctors and 51 nurses using the Manchester Triage System. They tested 110 cases and found:

• Doctors’ accuracy: 70.6%
• Nurses’ accuracy: 65.5%
• AI accuracy: 50.4%

For spotting urgent cases, sensitivity scores were:

• Doctors: 83.0%
• Nurses: 73.8%
• AI: 58.3%

The AI did better than nurses in the most urgent category (level 1), with accuracy of 27.3% vs. 9.3%, and specificity of 27.8% vs. 8.3%. This means AI tends to mark more cases as urgent, which might prevent missing serious patients but could use resources less efficiently.

Researchers, including Dr. Renata Jukneviciene, said AI should help clinical staff, not replace them. They warned that too much over-triage by AI can cause problems and said human review is needed to check AI results.

Implications of AI Triage Accuracy for Healthcare Administrators in the United States

Healthcare leaders in the U.S. must think about both the good and bad when using AI triage tools. AI can help workflows and support decisions, but it needs strong supervision by clinicians.

Important points for U.S. medical centers are:

• AI is very good at spotting life-threatening emergencies, which lowers the chance of missing serious cases.
• AI is less reliable in medium urgency cases, so humans should check these to avoid missing needed care.
• AI tends to over-triage, marking some non-urgent patients as urgent, which may strain resources.
• New AI versions like ChatGPT-4o do better than older ones but still need clinical testing.
• AI accuracy improves with oversight from experienced clinicians to catch errors and misunderstandings.

U.S. healthcare managers should keep these points in mind when thinking about AI triage. They need to balance new technology with training and way of working changes.

Integration of AI with Workflow Automation in Healthcare Triage

Automating triage and improving front-office work are key efforts by companies like Simbo AI. Simbo AI creates AI tools for phone automation and answering services made for healthcare. Their systems handle patient calls by screening, gathering symptom details, and sending urgent calls to medical staff fast. This helps lower the load on office workers and speeds up response to patients.

For U.S. healthcare centers with many incoming calls and limited staff, AI automation offers these benefits:

• Faster call handling: AI phone systems can answer patient questions, book appointments, and do early symptom checks, cutting wait times and missed calls.
• Automatic urgency detection: AI spots urgent problems during calls and alerts staff right away.
• Better resource use: Automating triage tasks lets clinical teams focus on patient care instead of paperwork.
• Data capturing: Front-office automation collects patient info in organized ways, which supports clinical decisions and lowers errors in documents.
• Consistency: AI follows triage rules all the time and is less affected by tiredness or stress that can make humans make mistakes during busy times.

Healthcare administrators and IT staff should see how front-office AI tools can work with electronic health records (EHR) and other systems. Making sure these tools connect well and let clinicians supervise is important for safety and quality.

Challenges in AI Adoption for Triage in U.S. Healthcare Settings

Even though AI shows promise, U.S. hospitals face some challenges when bringing in this tech:

• Accuracy and trust: AI can make mistakes, especially in complex or unclear patient cases.
• Ethical issues: Patient safety and privacy need strong rules about AI use.
• Understanding AI decisions: Doctors must know how AI works to trust and use its advice.
• Training and adapting: Staff need to learn about AI’s strengths and limits so they use it well but don’t replace good clinical judgment.

Hospitals should create rules that mix AI help with ongoing doctor checks. Testing AI in some departments first and watching results closely can guide wider use.

Summary of Research and Practical Considerations

The studies show AI triage tools like ChatGPT can help healthcare teams quickly check how urgent patients are. In emergencies, AI spots critical cases fast. Still, AI has trouble with tricky cases and needs supervision to avoid wrong urgency ratings.

For U.S. healthcare managers running busy practices or emergency rooms, adding AI phone automation together with clinical triage tools can cut workloads, improve patient flow, and use resources better. Simbo AI’s phone systems fit this need well by supporting patient communication and early screening before a clinical check.

Still, AI should be used carefully, with clear ideas about what it can and can’t do now. Training staff and watching over AI use remain essential for safe and effective healthcare.

Overall Summary

This review of AI and clinician triage accuracy offers helpful advice for U.S. healthcare leaders thinking about AI. Using proven AI tools while keeping skilled human staff in charge of care helps healthcare centers improve how they work without risking patient safety or quality.