Our emergency departments (EDs) are facing severe overcrowding issues and delays in timely care. To tackle this problem, we propose using artificial intelligence (AI) and machine learning to develop a data-driven patient flow model to help healthcare providers optimize resources, reduce wait times, and improve patient outcomes. This project makes a significant impact on several critical aspects of healthcare delivery, including:

• Reduced ED crowding and improved patient outcomes: The proposed model can potentially enhance the quality of care and overall patient outcomes by optimizing patient flow. For example, if our EDs can provide timely and appropriate care, patients will be less likely to face complications or require follow-up visits.
• More efficient use of resources: By identifying bottlenecks in the patient-flow process and optimizing resource allocation, the model can help healthcare providers make more efficient use of their resources, reducing costs and improving efficiency.
• Structured decision-making: The AI-based tool can help decision-makers make more informed and effective patient-care and resource-allocation decisions by using real-time data and predictive analytics.
• Better capacity planning: By predicting peak hours and allocating resources accordingly, the model can help healthcare providers plan for capacity needs more effectively, improving overall efficiency and reducing wait times.

We believe that the impact of this project is significant, and it has the potential to improve the quality of care, reduce costs and enhance the overall efficiency of healthcare delivery.