{"id":{"repo_id":"sask","oai_identifier":"oai:harvest.usask.ca:10388/17612"},"canonical_url":"https://search.dev.ndltd.org/etd/sask/oai:harvest.usask.ca:10388/17612","repository":{"repo_id":"sask","name":"University of Saskatchewan","base_url":"https://harvest.usask.ca/server/oai/request"},"display":{"title":"IMPROVING PATIENT EXPERIENCE WITH EMOTION-SENSITIVE LARGE MODELS","abstract":"In the contemporary digital healthcare landscape, technological innovations have significantly improved access and efficiency; yet, an essential question persists: can these technologies also address the emotional needs of patients? This study investigates the role of Large Language Models (LLMs), a class of foundation models trained on vast datasets, in improving both administrative efficiency and patient-centered care within healthcare environments. This study demonstrates the feasibility of employing LLMs to develop a custom, clinicoriented booking assistant capable of automating appointment scheduling, reducing administrative workload, and improving access to healthcare services. This system was implemented using Google Apps Script, a cloud-based JavaScript platform that enables automation of workflows and integration with Google services. Beyond administrative applications, the study examines the ability of LLMs to detect patient emotions by leveraging established facial expression datasets to simulate real world telehealth interactions. The findings from technical implementation and experiments underscore the broader promise of LLMs in healthcare; specifically, by combining administrative efficiency with emotionally adaptive interventions, LLMs can contribute to the creation of more patient centered digital healthcare systems that not only streamline operations but also address patients’ psychological and emotional needs.","abstract_html":"In the contemporary digital healthcare landscape, technological innovations have significantly improved access and efficiency; yet, an essential question persists: can these technologies also address the emotional needs of patients? This study investigates the role of Large Language Models (LLMs), a class of foundation models trained on vast datasets, in improving both administrative efficiency and patient-centered care within healthcare environments. This study demonstrates the feasibility of employing LLMs to develop a custom, clinicoriented booking assistant capable of automating appointment scheduling, reducing administrative workload, and improving access to healthcare services. This system was implemented using Google Apps Script, a cloud-based JavaScript platform that enables automation of workflows and integration with Google services. Beyond administrative applications, the study examines the ability of LLMs to detect patient emotions by leveraging established facial expression datasets to simulate real world telehealth interactions. The findings from technical implementation and experiments underscore the broader promise of LLMs in healthcare; specifically, by combining administrative efficiency with emotionally adaptive interventions, LLMs can contribute to the creation of more patient centered digital healthcare systems that not only streamline operations but also address patients’ psychological and emotional needs.","abstract_has_math":false,"creators":["DEL ROSARIO, INDIRA"],"institution":"University of Saskatchewan","degree_name":"Master of Science (M.Sc.)","degree_level":"Masters","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Zhang, Chris","Lin, Randy","Ip, Andrew","Ko, Seok-Bum"],"year":2025,"date_issued":"2025-12-09","date_published":"2025-12-09","updated_at":"2026-07-24T04:27:18Z","subjects":["Telehealth","Patient Experience","Large Language Models","Emotion Detection","AI in Healthcare"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10388/17612","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Zhang, Chris","Lin, Randy","Ip, Andrew","Ko, Seok-Bum"]},{"key":"dc:creator","label":"Author","values":["DEL ROSARIO, INDIRA"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-09T19:45:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-09T19:45:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-09"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.Sc.)"]},{"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":["Telehealth","Patient Experience","Large Language Models","Emotion Detection","AI in Healthcare"]}]},{"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/10388/17612"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In the contemporary digital healthcare landscape, technological innovations have significantly improved access and efficiency; yet, an essential question persists: can these technologies also address the emotional needs of patients? This study investigates the role of Large Language Models (LLMs), a class of foundation models trained on vast datasets, in improving both administrative efficiency and patient-centered care within healthcare environments. This study demonstrates the feasibility of employing LLMs to develop a custom, clinicoriented booking assistant capable of automating appointment scheduling, reducing administrative workload, and improving access to healthcare services. This system was implemented using Google Apps Script, a cloud-based JavaScript platform that enables automation of workflows and integration with Google services. Beyond administrative applications, the study examines the ability of LLMs to detect patient emotions by leveraging established facial expression datasets to simulate real world telehealth interactions. The findings from technical implementation and experiments underscore the broader promise of LLMs in healthcare; specifically, by combining administrative efficiency with emotionally adaptive interventions, LLMs can contribute to the creation of more patient centered digital healthcare systems that not only streamline operations but also address patients’ psychological and emotional needs."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["IMPROVING PATIENT EXPERIENCE WITH EMOTION-SENSITIVE LARGE MODELS"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Zhang, Chris","Lin, Randy","Ip, Andrew","Ko, Seok-Bum"],"dc:creator":["DEL ROSARIO, INDIRA"],"dc:date.accessioned":["2025-12-09T19:45:39Z"],"dc:date.available":["2025-12-09T19:45:39Z"],"dc:date.issued":["2025-12-09"],"dc:description.abstract":["In the contemporary digital healthcare landscape, technological innovations have significantly improved access and efficiency; yet, an essential question persists: can these technologies also address the emotional needs of patients? This study investigates the role of Large Language Models (LLMs), a class of foundation models trained on vast datasets, in improving both administrative efficiency and patient-centered care within healthcare environments. This study demonstrates the feasibility of employing LLMs to develop a custom, clinicoriented booking assistant capable of automating appointment scheduling, reducing administrative workload, and improving access to healthcare services. This system was implemented using Google Apps Script, a cloud-based JavaScript platform that enables automation of workflows and integration with Google services. Beyond administrative applications, the study examines the ability of LLMs to detect patient emotions by leveraging established facial expression datasets to simulate real world telehealth interactions. The findings from technical implementation and experiments underscore the broader promise of LLMs in healthcare; specifically, by combining administrative efficiency with emotionally adaptive interventions, LLMs can contribute to the creation of more patient centered digital healthcare systems that not only streamline operations but also address patients’ psychological and emotional needs."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10388/17612"],"dc:language.iso":["en"],"dc:subject":["Telehealth","Patient Experience","Large Language Models","Emotion Detection","AI in Healthcare"],"dc:title":["IMPROVING PATIENT EXPERIENCE WITH EMOTION-SENSITIVE LARGE MODELS"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science (M.Sc.)"],"thesis:institution_name":["University of Saskatchewan"]},"updated_at":"2026-07-24T04:27:18Z"}