Balancing Accuracy and Resource Constraints: Optimizing AI Decision-Tree Models for ENT Patient Triage with Dynamic Threshold Adjustments

Patient triage in ENT clinics means figuring out how urgent a patient’s issue is and what kind of appointment they need. This is especially true for symptoms like chronic dizziness. Around 70 million Americans have this problem, which creates a lot of work for clinics. Before, doctors spent 20% to 40% of appointment time just checking referrals and deciding what care patients needed. This took a lot of time and caused delays.

To fix this, Mayo Clinic started using an AI system for triage. It uses adaptive electronic questionnaires and decision-tree methods to sort symptoms and suggest the right kind of appointment. When tested with past patient data, the system was 87% accurate in predicting what kind of ENT consultation was needed, such as for balance disorders or vertigo follow-ups.

The AI works by asking patients structured questions, mostly multiple-choice. These answers guide the system through the decision tree to the best result. This clear structure helps the AI make better decisions. Electronic questionnaires replaced paper forms, which helped improve data quality and consistency.

Because of this system, triage time went down by 75%. This means doctors can see more patients without hiring extra staff. The AI helps by doing repeated tasks but does not make final decisions alone. Doctors still check and can change AI suggestions. This mix keeps care quality high and lets professionals stay involved.

Managing False Positives and False Negatives: Dynamic Threshold Adjustments

A big problem with AI triage is balancing false positives and false negatives. False positives happen when patients are sent to specialists unnecessarily. False negatives happen when patients who need care are missed. Both can cause problems, like long waitlists or wrong care.

Mayo Clinic made the AI able to change its decision rules based on how many specialist appointments are available right then. If there are few ENT or neurology appointments, the AI becomes stricter and lowers false positives. If many appointments are free, it refers more patients to reduce false negatives.

If the AI is not sure about a referral, it does not say no. Instead, it delays the decision and sets the patient up for a specialist visit when there is time. This way, patients do not lose care while the system manages workload.

This system helps match patient referrals to the number of appointments available. It helps keep patient flow smooth and reduces stress on clinics.

A Six-Step Framework for Scaling AI in Clinical Practice

Mayo Clinic shared six steps for adding AI into clinical work:

• System Redesign: Change workflows to add AI and new ways to collect data, like switching from paper forms to electronic questionnaires.
• Data Quality: Collect clear, organized data. Avoid many open-ended questions because they are hard for AI to handle.
• Model Selection: Pick AI models like decision trees that fit the clinical task.
• Human-Computer Interaction: Keep doctors involved so they can check and change AI suggestions.
• Change Management: Get feedback from all users early and make changes based on input.
• Result Measurement: Keep watching how well the AI works and its impact on care and operations.

This approach helps avoid problems faced by tools that are just set up and forgotten.

Practical Implementation: Insights from Mayo Clinic

Mayo Clinic’s AI triage showed clear results:

• Doctors spent 75% less time checking referrals, freeing them to care for patients.
• The clinic saw more patients without hiring more staff.
• Doctors could still review and change AI suggestions, which helped acceptance.
• Electronic questionnaires made data collection easier and smoother for AI.
• The system is seen as a help tool, so it does not need FDA approval, making deployment simpler.

Equity and Sustainability in AI Deployment

Dr. Christina Yuan highlighted that AI must work fairly for all patient groups. This includes people of different races, genders, ethnicities, and incomes. Challenges like economic differences and health literacy still exist.

Mayo Clinic checked how the AI worked across different groups to find and fix biases. They also made sure users like clinicians could give feedback to keep the system practical.

Using AI is an ongoing job. Regular updates and checks help the system stay accurate and trusted over time.

Optimizing ENT Clinic Workflow Through AI Automation and Decision Supports

AI decision-tree models for triage fit with other tools that automate front-office work. For clinic managers and IT staff, using AI helps solve different problems:

• Call Management and Patient Intake: AI phone systems can handle common questions, book appointments, and do initial symptom checks before doctors get involved. This reduces wait times and lets staff focus on harder problems.
• Data Capture and Preliminary Triage: Adaptive questionnaires on phones or web portals gather patient symptoms clearly for the AI to analyze.
• Appointment Scheduling Optimization: AI can work with scheduling systems to manage appointments, prioritize urgent cases, delay uncertain ones, and avoid overbooking.
• Documentation Automation: New AI tools can help with paperwork, like chart reviews and spotting surgery candidates, cutting admin work and giving doctors better data.

These improvements help clinics where front-office staff have many repetitive tasks and appointment spots are limited.

AI phone automation tools complement clinical triage models by handling first patient contacts. Together, they give a fuller solution to patient flow.

Considerations for US Medical Practice Administrators and IT Managers

For clinics in the US using AI in ENT triage and office automation, here are important points:

• Data Infrastructure: Move from paper to digital forms with clear answer choices for smooth AI use.
• Clinician Collaboration: Get doctors involved early and often so the system supports their work and is accepted.
• Resource Monitoring: Track appointment availability and adjust AI decisions to match clinic capacity in real time.
• Equity Audits: Regularly check the AI’s performance across different patient groups to avoid bias.
• Business Model Alignment: Understand how AI cuts workload and raises patient volume to justify costs.
• Regulatory and Privacy Compliance: Even if FDA approval is not needed, clinics must follow HIPAA rules and protect patient data.
• AI Literacy and Training: Train staff on what AI can and cannot do to build trust and proper use.

Using AI decision-tree models for ENT triage with flexible thresholds can improve clinic efficiency without lowering care quality or overloading staff. Mayo Clinic’s experience shows better patient flow and accuracy when AI is carefully added. Combining AI triage with front-office tools like AI phone automation offers a more responsive outpatient process. Paying close attention to data quality, clinician input, fairness, and ongoing checks is key to success.