code-bot
AI & Data Science NITAFYAFLOW
Problem Statement
Healthcare facilities experience changes in patient demand over time, with some periods receiving substantially more patients than others. When future demand is not anticipated, healthcare managers may face difficulties preparing appropriate service capacity and effectively planning the use of available resources. This can contribute to periods of high service pressure, longer waiting times, inefficient utilization of available capacity, and reactive rather than proactive planning. AfyaFlow addresses the limited use of historical health-service data to forecast future patient demand and identify periods of potential capacity pressure before they occur.
Proposed Solution
AfyaFlow is a Machine Learning-based decision-support system that forecasts future patient demand using historical hospital admission data. It analyses historical admission patterns, estimates expected demand for future periods, and identifies periods where demand may be unusually high or low. The forecasts, demand trends, and capacity-pressure indicators will be presented through a decision-support dashboard, enabling healthcare managers to support proactive capacity planning and resource allocation. The system will use Machine Learning forecasting as its primary approach, with anomaly detection potentially used to identify unusual changes in demand.
Innovation
AfyaFlow combines Machine Learning-based patient-demand forecasting, demand anomaly detection, and capacity-pressure identification into a structured decision-support system. Rather than relying only on historical reports or reactive planning, the system transforms historical admission data into forward-looking demand forecasts and visual alerts through a dashboard.
Expected Impact
AfyaFlow is expected to help healthcare decision-makers anticipate changes in patient demand, identify potential periods of high service pressure, and support proactive, data-driven capacity planning. This can contribute to better preparedness, more efficient utilization of available capacity and resources, reduced pressure on services, and improved efficiency in healthcare service delivery.