ORAHS2025
Abstract Submission

156. Modeling strategic-level decision-making in specialized healthcare using simulation, with a focus on reducing waiting lists in traumatology services.

Invited abstract in session FA-1: Simulation 2, stream Sessions.

Friday, 9:00-10:30
Room: NTNU, Realfagbygget R5

Authors (first author is the speaker)

1. Miguel Baigorri
Statistics, informatics and Mathematics, Public University of Navarra
2. Marta Cildoz
Institute of Smart Cities, Public University of Navarre
3. Fermin Mallor
Institute of Smart Cities, Public University of Navarre

Abstract

Patient waiting list lengths are increasing in most countries worldwide due to the generalized aging of the population and the rising demand for health services. For instance, in the region of Navarra (Spain), waiting lists have reached 240,000 entries in a population of 660,000, marking a 29% increase since 2018. In this work, we present a discrete-event simulation model of a traumatology department to assess a strategic decision regarding changes in patient pathways aimed at reducing waiting list lengths in the mid-term. This model has been developed using real data provided by the University Hospital of Navarre and constitutes the first step toward creating a digital twin of the traumatology service. Digital twins enable the reproduction and optimization of systems through real-time data collection. The model allows for analyzing the impact of structural changes on patient waiting lists, including the effect of the behavior and use of different access pathways to specialized care by primary care physicians. Results show that the proposed changes in patient pathways may reduce waiting list lengths, provided that the hospital and primary care services make effective use of the new appointment options. The next step will be integrating real-time data into the model, thereby developing a digital twin and enabling operational decision-making in the short term.

Keywords

Status: accepted

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