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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.

Scheduling medical appointments plays a fundamental role in managing patient flow and ensuring high-quality care. However, no-shows can significantly disrupt this process and affect patient care. To address this challenge, healthcare facilities can adopt different strategies, including overbooking in medical consultations. While this reduces the risk of unused slots, it can generate associated costs and affect the perception of service quality. In this article, we propose an integer linear optimization model that maximizes the expected utility of a medical center, considering the risk of no-shows and overbooking. For this purpose, machine learning is used to estimate the propensity of each patient to attend their medical appointment, using real data from three medical specialties of a hospital. The results of the application demonstrate the model’s ability to assign appointments and perform overbooking efficiently and in an organized manner, implying an improvement in the utility of a medical center and a positive impact on the perception of the quality of care.

Disrupting a criminal organization requires a significant deployment of human resources, time, information, and financial investment. In the early stages of an investigation, details about a specific crime are typically scarce, often with no known suspect. The literature has shown that an effective approach for analyzing criminal organizations is social network analysis. This approach allows the use of traditional social network tools for analyzing criminal networks, as well as more sophisticated and recent tools. This article introduces a model called StPro, which enables the identification of members of a criminal organization starting from a single suspect. It utilizes linear optimization modeling based on Steiner trees. A suspect is used as the root node, and the resulting tree reveals a probable configuration of the criminal organization to which the suspect may belong. Its application to a real-world case demonstrates that there are no significant differences in effectiveness between the proposed model and the state-of-the-art in the literature, despite requiring less information. It also demonstrates how its application aided in the identification of a gang dedicated to violent crimes in Chile. These results highlight the strong capability of the proposed model to support criminal investigations.