AI advice vs. patients’ advice vs. no advice: choosing a doctor
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/92319 |
Resumo: | Due to the development of information technologies and Artificial Intelligence (henceforth AI) solutions, hospitals can match patients’ to doctors more efficiently and effectively. In this research, I investigate trust in advice generated by AI over advice from other people, when a patient selects a doctor for a medical appointment. Data related to trust, likelihood to follow the received recommendation, likelihood to select a doctor, and demographics were collected via an online questionnaire to investigate whether people accept or refuse advice to choose a doctor based on AI advice (vs. other patients’ advice or no advice). The experiment revealed that patients are more likely to select a doctor following advice from AI, as compared to when they receive advice from other people or no advice at all. Moreover, patients trusted more AI advice than advice from other people. These results have important practical implication: they suggest that hospitals should inform patients about benefits and dispel doubts of using AI in matching-system through educational programs and leaflets. Finally, they also suggest developing AI recommender-systems that can share AI consultations with a selected physician. |
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AI advice vs. patients’ advice vs. no advice: choosing a doctorAI advicedoctorpatienttrustalgorithmslikelihoodDomínio/Área Científica::Ciências Sociais::Economia e GestãoDue to the development of information technologies and Artificial Intelligence (henceforth AI) solutions, hospitals can match patients’ to doctors more efficiently and effectively. In this research, I investigate trust in advice generated by AI over advice from other people, when a patient selects a doctor for a medical appointment. Data related to trust, likelihood to follow the received recommendation, likelihood to select a doctor, and demographics were collected via an online questionnaire to investigate whether people accept or refuse advice to choose a doctor based on AI advice (vs. other patients’ advice or no advice). The experiment revealed that patients are more likely to select a doctor following advice from AI, as compared to when they receive advice from other people or no advice at all. Moreover, patients trusted more AI advice than advice from other people. These results have important practical implication: they suggest that hospitals should inform patients about benefits and dispel doubts of using AI in matching-system through educational programs and leaflets. Finally, they also suggest developing AI recommender-systems that can share AI consultations with a selected physician.Consiglio, IreneRUNKaliszewska, Anna2020-02-06T16:21:37Z2019-09-202019-09-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/92319TID:202417956enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:41:10Zoai:run.unl.pt:10362/92319Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:31.168617Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
title |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
spellingShingle |
AI advice vs. patients’ advice vs. no advice: choosing a doctor Kaliszewska, Anna AI advice doctor patient trust algorithms likelihood Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
title_full |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
title_fullStr |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
title_full_unstemmed |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
title_sort |
AI advice vs. patients’ advice vs. no advice: choosing a doctor |
author |
Kaliszewska, Anna |
author_facet |
Kaliszewska, Anna |
author_role |
author |
dc.contributor.none.fl_str_mv |
Consiglio, Irene RUN |
dc.contributor.author.fl_str_mv |
Kaliszewska, Anna |
dc.subject.por.fl_str_mv |
AI advice doctor patient trust algorithms likelihood Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
AI advice doctor patient trust algorithms likelihood Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
Due to the development of information technologies and Artificial Intelligence (henceforth AI) solutions, hospitals can match patients’ to doctors more efficiently and effectively. In this research, I investigate trust in advice generated by AI over advice from other people, when a patient selects a doctor for a medical appointment. Data related to trust, likelihood to follow the received recommendation, likelihood to select a doctor, and demographics were collected via an online questionnaire to investigate whether people accept or refuse advice to choose a doctor based on AI advice (vs. other patients’ advice or no advice). The experiment revealed that patients are more likely to select a doctor following advice from AI, as compared to when they receive advice from other people or no advice at all. Moreover, patients trusted more AI advice than advice from other people. These results have important practical implication: they suggest that hospitals should inform patients about benefits and dispel doubts of using AI in matching-system through educational programs and leaflets. Finally, they also suggest developing AI recommender-systems that can share AI consultations with a selected physician. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-09-20 2019-09-20T00:00:00Z 2020-02-06T16:21:37Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/92319 TID:202417956 |
url |
http://hdl.handle.net/10362/92319 |
identifier_str_mv |
TID:202417956 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799137991881392128 |