Patient Prioritization and Medical Triage: Strategies for Enhanced Efficiency in Urgent and Emergency Care
Patient prioritization and medical triage are essential components in the management of urgent and emergency care. In a context where resources are limited and demand is high, consultation efficiency becomes a primary objective. The COVID-19 pandemic has underscored the importance of these processes, forcing healthcare systems to adapt rapidly to manage patient flow effectively.
Diving Deeper into Medical Triage and Patient Prioritization
Medical triage is a dynamic process that aims to classify patients based on the severity of their condition to ensure that those in greatest need receive attention first. During the pandemic, technologies such as artificial intelligence have been implemented to enhance triage efficiency, particularly in areas like ophthalmology, where it has been used to predict severity outcomes and prioritize care through telemedicine.
In the field of neurosurgery, the pandemic has presented unique challenges, especially in developing countries. The implementation of teleconsultation and telenursing services has been crucial in maintaining continuity of care while prioritizing the most urgent cases.
Moreover, the use of machine learning models in the triage of patients with cardiovascular diseases has proven effective in differentiating and prioritizing high-risk patients, thereby improving the allocation of limited resources in emergency departments.
Conclusions
The implementation of prioritization and medical triage strategies is fundamental to improving consultation efficiency, especially in times of health crises. The integration of advanced technologies, such as artificial intelligence and machine learning, along with the adaptation of telemedicine services, has proven to be an effective solution to address current challenges. As we continue to face new demands in the healthcare system, it is crucial to keep innovating and adapting our practices to ensure that all patients receive the timely and efficient care they need.
Referencias
- [1] Artificial intelligence in ophthalmology during COVID-19 and in the post COVID-19 era.
- [2] Challenges posed by COVID-19 and neurosurgical nursing strategies in developing countries.
- [3] Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease.
Created 23/1/2025