AI in Emergency Medicine

Can AI Reduce Emergency Department Wait Times?

· 4 min read

Can AI Reduce Emergency Department Wait Times?

Author: Rasit Dinc

Introduction

Emergency Departments (EDs) are the frontline of acute healthcare, but they are under immense pressure. Overcrowding, long wait times, and resource limitations are persistent challenges for healthcare systems globally [1]. These issues not only compromise patient safety and outcomes but also contribute to staff burnout and decreased patient satisfaction [2]. In the search for solutions, Artificial Intelligence (AI) has emerged as a powerful tool with the potential to revolutionize ED operations and significantly reduce wait times.

How AI is Reshaping the Emergency Department

AI, particularly its subfields of machine learning (ML) and deep learning, offers a suite of tools to optimize ED workflows. Unlike traditional, often manual, processes, AI systems can analyze vast and complex datasets in real-time to support clinical decision-making, predict patient flow, and allocate resources more effectively.

AI-Powered Triage: A New Standard of Prioritization

One of the most impactful applications of AI in the ED is in the triage process. Traditional triage systems rely on subjective clinical assessments, which can lead to variability and inconsistency, especially during peak hours or mass casualty incidents [2]. AI-driven triage systems, however, use objective data to prioritize patients with greater accuracy and speed.

By analyzing a combination of vital signs, symptoms, electronic health records (EHRs), and even unstructured data from clinician notes through Natural Language Processing (NLP), AI algorithms can identify high-risk patients almost instantly. This is critical for time-sensitive conditions like stroke or sepsis, where rapid intervention is key. Studies have shown that AI-based triage can reduce time-to-treatment by up to 20%, a significant improvement that directly translates to better patient prognoses [3]. One study even reported a 30% decrease in wait times after implementing an AI-driven triage system [3].

Predictive Analytics for Resource Management

Beyond triage, AI excels at predictive analytics. By analyzing historical data and real-time trends, machine learning models can forecast patient arrival rates, predict peak demand times, and anticipate the resources needed, such as beds, staffing, and diagnostic equipment [4]. This allows ED managers to move from a reactive to a proactive stance, adjusting staffing levels and preparing for surges before they occur. This optimization of resource allocation is crucial for improving patient throughput and preventing the bottlenecks that lead to long waits.

The Evidence: What Recent Studies Reveal

Recent research provides compelling evidence for AI's effectiveness in the ED. A 2024 review highlighted that the most common applications of AI are in supporting clinical judgment and reducing the burden on overcrowded EDs, primarily during the triage stage [1]. Another comprehensive review from 2025 emphasized that AI-driven triage enhances patient prioritization, reduces wait times, and optimizes resource allocation, especially during mass casualty events [3].

These systems not only improve efficiency but also enhance consistency. By applying standardized algorithms, AI reduces the human variability and cognitive biases that can affect triage decisions, leading to more equitable and reliable patient care [3].

Challenges and the Path Forward

Despite its promise, the widespread adoption of AI in the ED is not without challenges. Key hurdles include:

The future of AI in emergency medicine will depend on addressing these challenges head-on. This involves refining algorithms with diverse and representative data, integrating AI with other technologies like wearable sensors for continuous patient monitoring, and conducting robust clinical trials to validate their safety and efficacy in real-world settings.

Conclusion

Artificial intelligence is no longer a futuristic concept in medicine; it is a practical tool that is already beginning to address the critical issue of emergency department wait times. By enabling faster and more accurate triage, optimizing resource allocation, and providing data-driven support to clinicians, AI has the potential to significantly improve patient flow, enhance the quality of care, and alleviate pressure on our vital emergency services. While challenges remain, continued innovation and thoughtful implementation will ensure that AI becomes an indispensable ally in the modern Emergency Department.

References

[1] Ahmadzadeh, B., Patey, C., Hurley, O., et al. (2024). Applications of Artificial Intelligence in Emergency Departments to Improve Wait Times: Protocol for an Integrative Living Review. JMIR Research Protocols, 13, e52612. https://pmc.ncbi.nlm.nih.gov/articles/PMC11053385/

[2] Hosseini-Shokouh, S. M., Mohammadi, K., & Shokouh, A. B. (2022). Optimization of service process in emergency department using discrete event simulation and machine learning algorithm. Journal of Biomedical Informatics, 131, 104105. https://pmc.ncbi.nlm.nih.gov/articles/PMC9206831/

[3] Da’Costa, A., Teke, J., Origbo, J. E., et al. (2025). AI-driven triage in emergency departments: A review of benefits, challenges, and future directions. International Journal of Medical Informatics, 197, 105838. https://www.sciencedirect.com/science/article/pii/S1386505625000553

[4] Akbasli, I. T., et al. (2025). Artificial intelligence-driven forecasting and shift optimization in the emergency department. JAMIA Open, 8(2), ooae138. https://academic.oup.com/jamiaopen/article/8/2/ooae138/8090057

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