Length of Hospital Stay and Quality of Care
Main Author: | |
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Publication Date: | 2016 |
Other Authors: | , , , |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10174/17411 https://doi.org/10.1007/978-3-319-27478-2_19 |
Summary: | The relationship between Length Of hospital Stay (LOS) and Quality-of-Care (QofC) is demanding and difficult to assess. Indeed, a multifaceted intertwining network of countless services and LOS factors is available, which may range from organizational culture to hospital physicians availability, without discarding the possibility of lifting the foot on intermediate care services, to the customs and cultures of the people. On health policy terms, LOS remains a measurable index of efficiency, and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times. Therefore, and in order to assess this statement, a Logic Programming based methodology to Knowledge Representation and Reasoning, allowing the modeling of the universe of discourse in terms of defective data, information and knowledge is used, being complemented with an Artificial Neural Networks based approach to computing, allowing one to predict for how long a patient should remain in a hospital or at home, during his/her illness experience. |
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Length of Hospital Stay and Quality of CareLength of Hospital StayLogic ProgrammingKnowledge Representation and ReasoningArtificial Neural NetworksThe relationship between Length Of hospital Stay (LOS) and Quality-of-Care (QofC) is demanding and difficult to assess. Indeed, a multifaceted intertwining network of countless services and LOS factors is available, which may range from organizational culture to hospital physicians availability, without discarding the possibility of lifting the foot on intermediate care services, to the customs and cultures of the people. On health policy terms, LOS remains a measurable index of efficiency, and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times. Therefore, and in order to assess this statement, a Logic Programming based methodology to Knowledge Representation and Reasoning, allowing the modeling of the universe of discourse in terms of defective data, information and knowledge is used, being complemented with an Artificial Neural Networks based approach to computing, allowing one to predict for how long a patient should remain in a hospital or at home, during his/her illness experience.Springer International Publishing2016-02-17T18:05:36Z2016-02-172016-01-01T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10174/17411http://hdl.handle.net/10174/17411https://doi.org/10.1007/978-3-319-27478-2_19engNeves, J., Abelha, A., Vicente, H., Neves, J. & Machado, J., Length of Hospital Stay and Quality of Care. In S. Kunifugi, G. Papadopoulos, A. Skulimowski & J. Kacprzyk, Eds., Advances in Intelligent Systems and Computing, Vol. 416, pp. 273–287, Springer International Publishing, Cham, Switzerland, 2016.Cham978-3-319-27477-52194-5357http://link.springer.com/chapter/10.1007/978-3-319-27478-2_191519DQUIjneves@di.uminho.ptvascoabelha91@gmail.comhvicente@uevora.ptjoaocpneves@gmail.comjmac@di.uminho.ptNeves, JoséAbelha, VascoVicente, HenriqueNeves, JoãoMachado, Joséinfo: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:RCAAP2024-01-03T19:04:41Zoai:dspace.uevora.pt:10174/17411Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T12:08:55.760158Repositórios 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 |
Length of Hospital Stay and Quality of Care |
title |
Length of Hospital Stay and Quality of Care |
spellingShingle |
Length of Hospital Stay and Quality of Care Neves, José Length of Hospital Stay Logic Programming Knowledge Representation and Reasoning Artificial Neural Networks |
title_short |
Length of Hospital Stay and Quality of Care |
title_full |
Length of Hospital Stay and Quality of Care |
title_fullStr |
Length of Hospital Stay and Quality of Care |
title_full_unstemmed |
Length of Hospital Stay and Quality of Care |
title_sort |
Length of Hospital Stay and Quality of Care |
author |
Neves, José |
author_facet |
Neves, José Abelha, Vasco Vicente, Henrique Neves, João Machado, José |
author_role |
author |
author2 |
Abelha, Vasco Vicente, Henrique Neves, João Machado, José |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Neves, José Abelha, Vasco Vicente, Henrique Neves, João Machado, José |
dc.subject.por.fl_str_mv |
Length of Hospital Stay Logic Programming Knowledge Representation and Reasoning Artificial Neural Networks |
topic |
Length of Hospital Stay Logic Programming Knowledge Representation and Reasoning Artificial Neural Networks |
description |
The relationship between Length Of hospital Stay (LOS) and Quality-of-Care (QofC) is demanding and difficult to assess. Indeed, a multifaceted intertwining network of countless services and LOS factors is available, which may range from organizational culture to hospital physicians availability, without discarding the possibility of lifting the foot on intermediate care services, to the customs and cultures of the people. On health policy terms, LOS remains a measurable index of efficiency, and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times. Therefore, and in order to assess this statement, a Logic Programming based methodology to Knowledge Representation and Reasoning, allowing the modeling of the universe of discourse in terms of defective data, information and knowledge is used, being complemented with an Artificial Neural Networks based approach to computing, allowing one to predict for how long a patient should remain in a hospital or at home, during his/her illness experience. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-02-17T18:05:36Z 2016-02-17 2016-01-01T00:00:00Z |
dc.type.driver.fl_str_mv |
book part |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/17411 http://hdl.handle.net/10174/17411 https://doi.org/10.1007/978-3-319-27478-2_19 |
url |
http://hdl.handle.net/10174/17411 https://doi.org/10.1007/978-3-319-27478-2_19 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Neves, J., Abelha, A., Vicente, H., Neves, J. & Machado, J., Length of Hospital Stay and Quality of Care. In S. Kunifugi, G. Papadopoulos, A. Skulimowski & J. Kacprzyk, Eds., Advances in Intelligent Systems and Computing, Vol. 416, pp. 273–287, Springer International Publishing, Cham, Switzerland, 2016. Cham 978-3-319-27477-5 2194-5357 http://link.springer.com/chapter/10.1007/978-3-319-27478-2_19 15 19 DQUI jneves@di.uminho.pt vascoabelha91@gmail.com hvicente@uevora.pt joaocpneves@gmail.com jmac@di.uminho.pt |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Springer International Publishing |
publisher.none.fl_str_mv |
Springer International Publishing |
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