2016
Neves, J.; Abelha, V.; Vicente, H.; Neves, J.; Machado, J.
Length of hospital stay and quality of care Proceedings Article
Em: G.A., Kacprzyk J. Skulimowski A. M. J. Papadopoulos (Ed.): pp. 273-287, Springer Verlag, 2016, ISSN: 21945357, (cited By 3; Conference of 9th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2014 ; Conference Date: 6 November 2014 Through 8 November 2014; Conference Code:164129).
Resumo | Links | BibTeX | Etiquetas: Computation theory; Computer circuits; Knowledge representation; Logic programming; Neural networks; Reconfigurable hardware, Health outcomes; Health policy; Intermediate cares; Knowledge representation and reasoning; Length of hospital stays; Organizational cultures; Quality of care; Universe of discourse, Hospitals
@inproceedings{Neves2016273,
title = {Length of hospital stay and quality of care},
author = {J. Neves and V. Abelha and H. Vicente and J. Neves and J. Machado},
editor = {Kacprzyk J. Skulimowski A.M.J. Papadopoulos G.A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975720683&doi=10.1007%2f978-3-319-27478-2_19&partnerID=40&md5=e94d0fa5c4bd31c39abe6b1aef60148b},
doi = {10.1007/978-3-319-27478-2_19},
issn = {21945357},
year = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {416},
pages = {273-287},
publisher = {Springer Verlag},
abstract = {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. © Springer International Publishing Switzerland 2016.},
note = {cited By 3; Conference of 9th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2014 ; Conference Date: 6 November 2014 Through 8 November 2014; Conference Code:164129},
keywords = {Computation theory; Computer circuits; Knowledge representation; Logic programming; Neural networks; Reconfigurable hardware, Health outcomes; Health policy; Intermediate cares; Knowledge representation and reasoning; Length of hospital stays; Organizational cultures; Quality of care; Universe of discourse, Hospitals},
pubstate = {published},
tppubtype = {inproceedings}
}
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. © Springer International Publishing Switzerland 2016.