Predicting radiology service times for enhancing emergency department management

Davide Aloini, Elisabetta Benevento, Marco Berdini and Alessandro Stefanini

Socio-Economic Planning Sciences, 2025, vol. 99, issue C

Abstract: Emergency departments (EDs) are increasingly challenged by overcrowding, resource shortages, and rising demand for care, which compromise operational efficiency and service quality. In response, machine learning (ML) is emerging as a powerful tool for ED management, offering predictive models to enhance real-time decision-making and optimize workflows.

Keywords: Healthcare management; Service time prediction; Predictive analytics; Artificial intelligence (AI); Emergency department (ED); Business process analytics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:99:y:2025:i:c:s0038012125000576

DOI: 10.1016/j.seps.2025.102208

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