{"id":{"repo_id":"sask","oai_identifier":"oai:harvest.usask.ca:10388/16963"},"canonical_url":"https://search.dev.ndltd.org/etd/sask/oai:harvest.usask.ca:10388/16963","repository":{"repo_id":"sask","name":"University of Saskatchewan","base_url":"https://harvest.usask.ca/server/oai/request"},"display":{"title":"Integrating Participatory Modeling with Simulation Techniques for Evidence-Informed Decision-Making in Addressing Patient Flow and COVID-19 Challenges","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Tian, Yuan"],"institution":"University of Saskatchewan","degree_name":"Doctor of Philosophy (Ph.D.)","degree_level":"Doctoral","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Osgood, Nathaniel D"],"committee_chairs":[],"committee_members":["Stanley, Kevin","Penz, Erika","Gutwin, Carl","Waldner, Cheryl","Mcquillan, Ian","Marshall, Deborah"],"year":2025,"date_issued":"2025-05-28","date_published":"2025-05-28","updated_at":"2026-07-24T04:27:20Z","subjects":["participatory modeling","hybrid modeling","hybrid simulation","discrete-event simulation","agent-based modeling, infectious disease, COVID-19","patient flow"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10388/16963","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Osgood, Nathaniel D"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Stanley, Kevin","Penz, Erika","Gutwin, Carl","Waldner, Cheryl","Mcquillan, Ian","Marshall, Deborah"]},{"key":"dc:creator","label":"Author","values":["Tian, Yuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-05-28T20:13:27Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-28T20:13:27Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-05-28"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (Ph.D.)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Saskatchewan"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["participatory modeling","hybrid modeling","hybrid simulation","discrete-event simulation","agent-based modeling, infectious disease, COVID-19","patient flow"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10388/16963"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Integrating Participatory Modeling with Simulation Techniques for Evidence-Informed Decision-Making in Addressing Patient Flow and COVID-19 Challenges"]}]}],"canonical_facts":{"dc:contributor.advisor":["Osgood, Nathaniel D"],"dc:contributor.committeemember":["Stanley, Kevin","Penz, Erika","Gutwin, Carl","Waldner, Cheryl","Mcquillan, Ian","Marshall, Deborah"],"dc:creator":["Tian, Yuan"],"dc:date.accessioned":["2025-05-28T20:13:27Z"],"dc:date.available":["2025-05-28T20:13:27Z"],"dc:date.issued":["2025-05-28"],"dc:description.abstract":["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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10388/16963"],"dc:language.iso":["en"],"dc:subject":["participatory modeling","hybrid modeling","hybrid simulation","discrete-event simulation","agent-based modeling, infectious disease, COVID-19","patient flow"],"dc:title":["Integrating Participatory Modeling with Simulation Techniques for Evidence-Informed Decision-Making in Addressing Patient Flow and COVID-19 Challenges"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy (Ph.D.)"],"thesis:institution_name":["University of Saskatchewan"]},"updated_at":"2026-07-24T04:27:20Z"}