Integrating Participatory Modeling with Simulation Techniques for Evidence-Informed Decision-Making in Addressing Patient Flow and COVID-19 Challenges

While simulation modeling methods are widely acknowledged as valuable tools in healthcare, their findings are rarely adopted by decision-makers in real-world policy-making. This problem arises from insufficient stakeholder engagement and the limitations of single-method simulation approaches, which often fail to capture the complexity of healthcare systems or align with the practical needs of policy-making. This dissertation addresses these challenges by developing and applying hybrid modeling and simulation approaches -- combining participatory modeling with various simulation techniques -- to bridge the knowledge-action gap, improve stakeholder engagement, and enhance the uptake of model findings in healthcare decision-making. Through three studies, I explored the use of participatory modeling combined with different simulation techniques to address two public health emergency challenges within the same healthcare system: emergency department crowding and COVID-19 responses. This dissertation highlights the feasibility and value of integrating participatory modeling with simulation techniques to engage stakeholders and facilitate evidence-informed decision-making. It contributes to the development and application of innovative hybrid modeling and simulation approaches to enhance stakeholder engagement, tackle complex healthcare challenges, and improve the translation of knowledge into actionable decisions in healthcare.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Saskatchewan

Year dc:date.issued

2025

Author dc:creator

• Tian, Yuan

Advisor dc:contributor.advisor

• Osgood, Nathaniel D

Committee members dc:contributor.committeemember

• Stanley, Kevin
• Penz, Erika
• Gutwin, Carl
• Waldner, Cheryl
• Mcquillan, Ian
• Marshall, Deborah

• participatory modeling
• hybrid modeling
• hybrid simulation
• discrete-event simulation
• agent-based modeling, infectious disease, COVID-19
• patient flow

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10388/16963

OAI identifier oai:identifier

oai:harvest.usask.ca:10388/16963