The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care.

Emergency department overcrowding disproportionately affects complex patients, such as older adults and those with comorbidities, who consume significant resources and experience prolonged delays. This study integrates process mining and predictive simulation to identify key factors influencing length of stay and to propose a data-driven solution: a tailored fast-track pathway for high-risk patients. Using data from 94,489 emergency episodes, a predictive formula was developed based on clinically relevant variables, including age (>65 years); triage levels (II and III); frequent emergency department visits; need for mobility aids; and specific reasons for consultation such as dyspnea, abdominal pain, and poor general condition. Simulation results demonstrated that implementing this fast-track pathway reduces length of stay by up to 21% and emergency department saturation by 35%, even with minimal resource allocation (five beds). The manual predictive formula showed comparable prediction performance to machine learning models while maintaining transparency and traceability, ensuring greater acceptability among healthcare professionals. This approach represents a paradigm shift in emergency department management, offering a scalable tool to optimise resource allocation, improve patient outcomes, and reduce operational inefficiencies. Future multicenter validations could establish this model as an essential component of emergency department management strategies.

Todos los días podemos leer noticias que nos hablan de la saturación de los Servicios de Urgencias Hospitalarios (SUH), del aumento de la demanda asistencial, del cierre de los centros de atención primaria, de la falta de personal sanitario, del cansancio de los profesionales, pero todo esto se justifica por la crisis sanitaria vivida consecuencia de la pandemia de coronavirus [Fragmento de texto].