Clinical pathways: A better understanding of customer’s journey

Detalhes bibliográficos
Autor(a) principal: Costa, Filipa Ribeiro Sá da
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/128902
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Clinical pathways: A better understanding of customer’s journeyMachine LearningHealthcareClinical EpisodesPathwaysKnee SurgeryInsuranceCustomer JourneyInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsWhen it comes to understanding how a disease evolves, having clinical pathways mapped can provide valuable insights to doctors and insurance companies. By analysing which pathway clients with certain characteristics follow, extrapolations and predictions for new clients’ behaviour can be done. SAS, Python and other software tools were used to implement Advanced Analytics and Machine Learning techniques during the whole project, allowing to use the best part of machines to retrieve valuable information from previously disaggregated data. From an insurance point of view, when it comes to evaluating the risk that a client brings and how he will use his insurance, having this data is highly relevant. From a clinical point of view, it would be great if an invasive surgery, with several associated risks, could be avoided by recommending other types of procedures such as physiotherapy or other wellness related procedures. The outcome of this study drew several pathways that when analysed provided valuable insights to the insurance company and is a starting point for further analytical predictions of customer’s behaviour. Projects, such as this one, are very important to increase profitability because they allow the companies to make data driven decisions adjusted to their reality.Pinheiro, Flávio Luís PortasRUNCosta, Filipa Ribeiro Sá da2021-12-09T12:12:46Z2021-11-302021-11-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/128902TID:202814173enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:57:28Zoai:run.unl.pt:10362/128902Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:28:43.825002Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Clinical pathways: A better understanding of customer’s journey
title Clinical pathways: A better understanding of customer’s journey
spellingShingle Clinical pathways: A better understanding of customer’s journey
Costa, Filipa Ribeiro Sá da
Machine Learning
Healthcare
Clinical Episodes
Pathways
Knee Surgery
Insurance
Customer Journey
title_short Clinical pathways: A better understanding of customer’s journey
title_full Clinical pathways: A better understanding of customer’s journey
title_fullStr Clinical pathways: A better understanding of customer’s journey
title_full_unstemmed Clinical pathways: A better understanding of customer’s journey
title_sort Clinical pathways: A better understanding of customer’s journey
author Costa, Filipa Ribeiro Sá da
author_facet Costa, Filipa Ribeiro Sá da
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Costa, Filipa Ribeiro Sá da
dc.subject.por.fl_str_mv Machine Learning
Healthcare
Clinical Episodes
Pathways
Knee Surgery
Insurance
Customer Journey
topic Machine Learning
Healthcare
Clinical Episodes
Pathways
Knee Surgery
Insurance
Customer Journey
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-12-09T12:12:46Z
2021-11-30
2021-11-30T00:00:00Z
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/10362/128902
TID:202814173
url http://hdl.handle.net/10362/128902
identifier_str_mv TID:202814173
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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