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Optimization of Patient Flow through EMT Facilities Applying Dynamic Behavioral Simulation Models

Published online by Cambridge University Press:  22 November 2022

Andrea Bartolucci Open the ORCID record for Andrea Bartolucci [Opens in a new window] ,
Gabriele B. Bernardini ,
Marco D’Orazio  and
Enrico Quagliarini
Andrea Bartolucci
Affiliation:
ISGA, Leiden University, Den Haag, Netherlands
Gabriele B. Bernardini
Affiliation:
DICEA, Università Politecnica delle Marche, Ancona, Italy
Marco D’Orazio
Affiliation:
DICEA, Università Politecnica delle Marche, Ancona, Italy
Enrico Quagliarini
Affiliation:
DICEA, Università Politecnica delle Marche, Ancona, Italy
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Abstract

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Background/Introduction:

The effectiveness of care provided by EMTs is directly linked to the ability to ensure a controlled, coordinated, and safe patient flow through the post, especially in case of MCIs or disease outbreak. Currently, there are neither frameworks nor tools to analyze such flow that is often visualized on paper using arrows or simply connected to the care pathway, thus missing the impact of people’s interactions with the facility.

Objectives:

This study aims at exploring the use of a behavioral-design-based approach in simulating patient flow through EMTs.1

Method/Description:

It provides a dynamic behavioral simulation model to assess the interactions between patients, staff members, and the related dynamic movements/interactions with the health care facility, each of them having specific features also in relation to the emergency condition faced.2 Data used in this study consist of literature-based information concerning patient characteristics (eg, age), the variation of expected medical conditions and severity in relation to the time and typology of the emergency,3 layouts of existing facilities (eg, UKMed T1), and experimental activities (eg, exercise).

Results/Outcomes:

The analysis of the results will allow to simulate different scenarios and improve the design of health care facilities layouts in order to prevent overcrowding situation, avoid disease spreading, estimate the optimal number of staff for each task, and investigate interactions between patients and staff.

Conclusion:

Optimizing patient flow encompasses quickly, efficiently, and effectively movement meeting the demand for care by moving patients through care pathways while improving coordination of care, patient safety, and health outcomes.

Type
Meeting Abstracts
Information
Prehospital and Disaster Medicine , Volume 37 , Supplement S2: Abstracts of World Health Organization Emergency Medical Teams Global Meeting 2022 , November 2022 , pp. s66
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of World Association for Disaster and Emergency Medicine

References

References (optional)

Bernardini, G, D’Orazio, M, Quagliarini, E. Towards a ‘behavioral design’ approach for seismic risk reduction strategies of buildings and their environment. Safety Science. 2016;86:273–294.CrossRefGoogle Scholar
Helbing, D, Bialetti, S. Agent-Based Modeling. In: Social Self-Organization - Understanding Complex Systems. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2012.CrossRefGoogle Scholar
Bartolucci, A, MacKway-Jones, K, Redmond, AD. Decision support framework for deployment of emergency medical teams after earthquakes. Disaster Medicine and Public Health Preparedness. 2020;1–8.Google Scholar