⚡ Quick Summary
This study presents the development of an automated triage system for patients suffering from longstanding dizziness, utilizing artificial intelligence to enhance scheduling efficiency. The first-generation algorithm achieved a mean concordance of 79% with expert clinician schedules, demonstrating its potential to streamline patient management.
🔍 Key Details
- 📊 Dataset: 98 patients with longstanding dizziness
- 🧩 Methodology: Retrospective case review and expert panel consensus
- ⚙️ Technology: Machine learning algorithm for triage automation
- 🏆 Performance: Mean concordance of 79% with ideal clinician schedules
🔑 Key Takeaways
- 🤖 AI in healthcare can significantly improve the triage process for dizzy patients.
- 💡 Automated systems reduce the time and costs associated with manual triage.
- 📋 Previsit questionnaires are effective tools for gathering patient data.
- 🏥 Study conducted at a quaternary referral center.
- 🔍 Expert panel provided valuable insights for determining ideal patient schedules.
- 📈 Results indicate that AI can match clinician performance in triaging patients.
- 🌟 Future research could expand this model to other patient populations.
📚 Background
Longstanding dizziness is a complex condition that often requires multidisciplinary consultations. Traditional triage methods can be time-consuming and costly, leading to delays in patient care. The integration of artificial intelligence into the triage process offers a promising solution to enhance efficiency and improve patient outcomes.
🗒️ Study
The study involved a retrospective case review of 98 patients at a quaternary referral center. Researchers developed a previsit self-report questionnaire to gather information on patients’ dizziness complaints. An expert panel of clinicians then reviewed diagnostic outcomes to establish ideal appointment schedules based on final diagnoses, which served as a benchmark for evaluating the automated triage system.
📈 Results
The results showed that the automated triage algorithm achieved a mean concordance of 79% with the ideal schedules determined by the expert panel, compared to a 70% concordance for manual triage by clinicians. This indicates that the machine learning model is capable of effectively automating the triage process while maintaining a high level of accuracy.
🌍 Impact and Implications
The implications of this study are significant for healthcare systems dealing with high volumes of patients experiencing dizziness. By adopting automated triage systems, healthcare providers can enhance operational efficiency, reduce wait times, and ultimately improve patient satisfaction. This approach could serve as a model for other medical conditions requiring complex care coordination.
🔮 Conclusion
The development of an automated triage system for longstanding dizzy patients marks a significant step forward in the application of artificial intelligence in healthcare. The promising results suggest that such systems can effectively complement clinician efforts, leading to improved patient management. Continued research and refinement of these technologies could pave the way for broader applications in various medical fields.
💬 Your comments
What are your thoughts on the integration of AI in patient triage systems? We would love to hear your insights! 💬 Share your comments below or connect with us on social media:
Development of an Automated Triage System for Longstanding Dizzy Patients Using Artificial Intelligence.
Abstract
OBJECTIVE: To report the first steps of a project to automate and optimize scheduling of multidisciplinary consultations for patients with longstanding dizziness utilizing artificial intelligence.
STUDY DESIGN: Retrospective case review.
SETTING: Quaternary referral center.
METHODS: A previsit self-report questionnaire was developed to query patients about their complaints of longstanding dizziness. We convened an expert panel of clinicians to review diagnostic outcomes for 98 patients and used a consensus approach to retrospectively determine what would have been the ideal appointments based on the patient’s final diagnoses. These results were then compared retrospectively to the actual patient schedules. From these data, a machine learning algorithm was trained and validated to automate the triage process.
RESULTS: Compared with the ideal itineraries determined retrospectively with our expert panel, visits scheduled by the triage clinicians showed a mean concordance of 70%, and our machine learning algorithm triage showed a mean concordance of 79%.
CONCLUSION: Manual triage by clinicians for dizzy patients is a time-consuming and costly process. The formulated first-generation automated triage algorithm achieved similar results to clinicians when triaging dizzy patients using data obtained directly from an online previsit questionnaire.
Author: [‘Romero-Brufau S’, ‘Macielak RJ’, ‘Staab JP’, ‘Eggers SDZ’, ‘Driscoll CLW’, ‘Shepard NT’, ‘Totten DJ’, ‘Albertson SM’, ‘Pasupathy KS’, ‘McCaslin DL’]
Journal: OTO Open
Citation: Romero-Brufau S, et al. Development of an Automated Triage System for Longstanding Dizzy Patients Using Artificial Intelligence. Development of an Automated Triage System for Longstanding Dizzy Patients Using Artificial Intelligence. 2024; 8:e70006. doi: 10.1002/oto2.70006