Scenario-Based Simulation Modeling for Performance and Efficiency Improvement in an Ultrasonography Department

TL;DR

This study uses simulation modeling to improve efficiency and reduce waiting times in an ultrasonography department by optimizing resource allocation and exploring IoT applications.

Contribution

The novel contribution is applying simulation-based optimization and IoT integration to enhance performance in a stochastic healthcare setting.

Findings

Optimized system configurations significantly reduced patient waiting times and total system time.

Incorporating IoT applications further improved operational efficiency and resource utilization.

Simulation-based approaches offer a systematic framework for evaluating healthcare operational scenarios.

Abstract

Background/Objectives: Hospitals prioritize effective resource allocation and patient satisfaction as key performance indicators. Improving the performance of the ultrasonography department remains a major challenge for hospital management due to the inherently unplanned and stochastic nature of its operations. Arrival patterns vary throughout the day, and examination durations differ depending on patients’ clinical pathways and examination types. This study focuses on the ultrasonography department of a private healthcare facility located in one of the most densely populated regions of Istanbul. The primary objective of this study was to improve departmental performance in terms of average waiting time, total time spent in the system, and resource utilization. Methods: To address the variability in patient arrivals and service times across different ultrasonography procedures, a simulation-based optimization approach was employed. Current system performance was evaluated, and multiple alternative operational scenarios were developed and simulated. In addition, the potential impact of Internet of Things applications on the performance of the ultrasonography department was investigated by incorporating alternative system configurations into the simulation model. Results: The simulation results enabled a comparative evaluation of alternative scenarios based on key performance indicators. The findings demonstrate that optimized system configurations can significantly reduce patient waiting times and total system time while improving resource utilization. The inclusion of Internet of Things applications further contributed to performance improvements in the selected scenarios. Conclusions: The proposed simulation-based approach provides a systematic decision-support framework for evaluating alternative operational scenarios in ultrasonography departments. By optimizing resource allocation and leveraging Internet of Things applications, hospital managers can improve operational efficiency and patient satisfaction. The results highlight the value of data-driven decision-making in managing complex and stochastic healthcare systems.

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