Prioritization Algorithms in Emergencies: Automated Triage to Enhance Clinical Decision-Making

In the dynamic environment of emergencies, the ability to effectively prioritize patients is crucial for optimizing clinical outcomes and operational efficiency. Prioritization algorithms and automated triage are emerging as essential tools to enhance clinical decision-making. These systems not only allow for a faster and more accurate assessment of patient severity but also free up human resources to focus on direct patient care.

Diving Deeper into Automated Triage

The use of large language models and other artificial intelligence (AI) technologies is transforming emergency medicine. These models can significantly improve clinical decision-making by providing real-time support for triage, enabling early recognition of patient urgency. Furthermore, the automation of patient record synthesis can reduce administrative burdens and enhance patient-centered care.

A recent study on the use of clinical decision-support tools for the triage of acute burns highlights how these technologies can assist in diagnosis, referral, and triage from the point of care to specialized centers. This not only improves equity in access to appropriate care but also optimizes the use of available resources.

On the other hand, the development of automated remote decision-making algorithms for mass casualty incidents (MCI) has proven to be a viable strategy for categorizing the emergency level of a patient using clinical parameters measured with portable devices. These algorithms can be employed to transfer patients and redistribute available resources according to their priorities.

Conclusions

The implementation of prioritization algorithms and automated triage in emergencies represents a significant advancement in clinical decision-making. These technologies not only improve operational efficiency and the quality of patient care but also allow for better resource management in critical situations. As we continue to integrate these tools into clinical practice, it is essential to keep researching and validating their use to ensure their safety and effectiveness.

Referencias

• [1] The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review
• [2] Clinical decision-support for acute burn referral and triage at specialized centres - Contribution from routine and digital health tools
• [3] Automated remote decision-making algorithm as a primary triage system using machine learning techniques

Created 24/1/2025