Preventing Health Risks Through Intelligent Medical Appointment Management Using Disease and Attendance Propensity

Intelligent management of medical appointments can significantly enhance patient care and reduce health risks. By leveraging disease and attendance propensity, our system aims to minimize the likelihood of patients developing serious conditions due to missed appointments by strategically scheduling those at higher risk. For its development, the methodology begins with the formulation of an optimization model, using assumed values for disease and attendance propensity to establish an initial, efficient scheduling of appointments. Subsequently, machine learning algorithms are applied to patient historical data to obtain more precise and realistic estimates of these propensities, which are then integrated into the model to adjust appointment allocation according to each patient's individual risk. The results demonstrate that the Intelligent Medical Appointment Management Model significantly outperforms random scheduling, which simulates current real-world practices without the use of an intelligent patient assignment system. Patients scheduled by the intelligent model show higher mean propensities to attend appointments and to develop diseases, ensuring that medical resources are allocated efficiently to those in greatest need. Statistical validation confirms the model's effectiveness, showing significant differences in scheduling outcomes between the intelligent and random models. This approach highlights the potential to reduce health risks among a group of patients by utilizing both their medical histories and synthetic data for more accurate predictions and effective scheduling.