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.

The quadratic multiple knapsack problem (QMKP) is a well-studied problem in operations research. This problem involves selecting a subset of items that maximizes the linear and quadratic profit without exceeding a set of capacities for each knapsack. While its solution using metaheuristics has been explored, exact approaches have recently been investigated. One way to improve the performance of these exact approaches is by reducing the solution space in different instances, considering the properties of the items in the context of QMKP. In this paper, machine learning (ML) models are employed to support an exact optimization solver by predicting the inclusion of items with a certain level of confidence and classifying them. This approach reduces the solution space for exact solvers, allowing them to tackle more manageable problems. The methodological process is detailed, in which ML models are generated and the best one is selected to be used as a preprocessing approach. Finally, we conduct comparison experiments, demonstrating that using a ML model is highly beneficial for reducing computing times and achieving rapid convergence.

The scheduling of medical appointments is an activity of great importance in a hospital since different human and material capitals must be used efficiently. One of the problems of this work is the non-attendance of a patient, which decreases the efficiency of the use of resources. Several studies have proposed considering "absenteeism" for scheduling medical appointments to address this. However, predicting it is a complex task. This research proposes the prediction of absenteeism to medical appointments for three medical areas of the Hospital Clínico Regional Dr. Guillermo Grant Benavente in the city of Concepción, Chile. For this purpose, five Machine Learning algorithms are trained and evaluated. The best-trained model managed to be a predictive tool of a patient's absenteeism level for his next appointment and to characterize those patients with higher levels of no-show.

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.

Este trabajo describe el área de la telemedicina como herramienta de gestión para el sistema público de salud en Chile. Se incluye las tres áreas relevantes como lo son la descripción de los procesos involucrados, las tecnologías de la información utilizadas y los diversos recursos humanos y materiales necesarios para aumentar la calidad de los servicios ofrecidos a los pacientes a través de los hospitales públicos en la Octava región en Chile. Se describe un proyecto de teleasistencia financiado por organismos del Estado y se muestran sus resultados preliminares. Además se identifican áreas futuras de desarrollo con el fin de incentivar a profesionales de la ingeniería para que se involucren en la investigación y desarrollo del sistema público de salud.

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.