Fast Track Design Using Process Mining: Does It Improve Saturation and Times in Emergency Departments?
Featured Application From a clinical perspective, the proposed fast-track pathway is specifically designed to address the needs of complex patients, who typically account for a significant proportion of resource consumption in emergency departments. By targeting this high-impact population, the path...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO) |
| Repositorio: | r-FISABIO. Repositorio Institucional de Producción Científica |
| OAI Identifier: | oai:fisabio.fundanetsuite.com:p19199 |
| Acesso em linha: | https://fisabio.portalinvestigacion.com/publicaciones/19199 |
| Access Level: | acceso abierto |
| Palavra-chave: | length of stay crowding emergency department healthcare hospital artificial intelligence |
| Resumo: | Featured Application From a clinical perspective, the proposed fast-track pathway is specifically designed to address the needs of complex patients, who typically account for a significant proportion of resource consumption in emergency departments. By targeting this high-impact population, the pathway contributes to optimising care delivery, reducing delays, and ensuring that resources are utilised more efficiently. This approach not only enhances patient outcomes but also mitigates operational challenges, making it a valuable tool for improving the overall efficiency of emergency services.Abstract 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. |
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