Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach
Automation in healthcare is a major challenge to improve quality of service while compressing costs. In particular, correct administration of medicines to patients is crucial to ensure quality of care during hospitalization and minimize medication errors. Mistakes are more likely to happen when medi...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/24249
- Acceso en línea:
- https://doi.org/10.1109/COASE.2018.8560374
https://repository.urosario.edu.co/handle/10336/24249
- Palabra clave:
- Automation
Hospitals
Optimization
Quality of service
Stochastic systems
Centralized distribution
Medication errors
New mathematical model
Quality of care
Robust optimization
Medicine
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
id |
EDOCUR2_ea6ca75d7dc8d2431cc1d7c7aa35d898 |
---|---|
oai_identifier_str |
oai:repository.urosario.edu.co:10336/24249 |
network_acronym_str |
EDOCUR2 |
network_name_str |
Repositorio EdocUR - U. Rosario |
repository_id_str |
|
spelling |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approachAutomationHospitalsOptimizationQuality of serviceStochastic systemsCentralized distributionMedication errorsNew mathematical modelQuality of careRobust optimizationMedicineAutomation in healthcare is a major challenge to improve quality of service while compressing costs. In particular, correct administration of medicines to patients is crucial to ensure quality of care during hospitalization and minimize medication errors. Mistakes are more likely to happen when medicine administration is done manually (dispensing, ordering or administrating). To reduce the risks related to medication errors, automation of the pharmacy processes appears as an appropriately tool to solve this situation. In this paper, we have proposed a new mathematical model to optimize the processes related to unit-doses management and prescriptions preparation in a network of hospitals. To model the uncertainty associated with the demand of medicines, the concept of p-robustness is included; the concept of resilience is also considered to model the risk of centralized distribution processes. © 2018 IEEE.IEEE Computer Society20182020-05-26T00:10:41Zinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fapplication/pdfhttps://doi.org/10.1109/COASE.2018.85603740000201100002013https://repository.urosario.edu.co/handle/10336/24249instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85059988525&doi=10.1109%2fCOASE.2018.8560374&partnerID=40&md5=c859a21986a2be1e769821ca4778de95http://purl.org/coar/access_right/c_abf2Franco Franco, Carlos AlbertoAugusto V.Garaix T.Alfonso-Lizarazo E.Bourdelin M.Bontemps H.oai:repository.urosario.edu.co:10336/242492022-05-02T07:37:16Z |
dc.title.none.fl_str_mv |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
title |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
spellingShingle |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach Automation Hospitals Optimization Quality of service Stochastic systems Centralized distribution Medication errors New mathematical model Quality of care Robust optimization Medicine |
title_short |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
title_full |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
title_fullStr |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
title_full_unstemmed |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
title_sort |
Strategic territorial deployment of hospital pharmacy robots using a stochastic p-robust optimization approach |
dc.subject.none.fl_str_mv |
Automation Hospitals Optimization Quality of service Stochastic systems Centralized distribution Medication errors New mathematical model Quality of care Robust optimization Medicine |
topic |
Automation Hospitals Optimization Quality of service Stochastic systems Centralized distribution Medication errors New mathematical model Quality of care Robust optimization Medicine |
description |
Automation in healthcare is a major challenge to improve quality of service while compressing costs. In particular, correct administration of medicines to patients is crucial to ensure quality of care during hospitalization and minimize medication errors. Mistakes are more likely to happen when medicine administration is done manually (dispensing, ordering or administrating). To reduce the risks related to medication errors, automation of the pharmacy processes appears as an appropriately tool to solve this situation. In this paper, we have proposed a new mathematical model to optimize the processes related to unit-doses management and prescriptions preparation in a network of hospitals. To model the uncertainty associated with the demand of medicines, the concept of p-robustness is included; the concept of resilience is also considered to model the risk of centralized distribution processes. © 2018 IEEE. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2020-05-26T00:10:41Z |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
dc.identifier.none.fl_str_mv |
https://doi.org/10.1109/COASE.2018.8560374 00002011 00002013 https://repository.urosario.edu.co/handle/10336/24249 |
url |
https://doi.org/10.1109/COASE.2018.8560374 https://repository.urosario.edu.co/handle/10336/24249 |
identifier_str_mv |
00002011 00002013 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059988525&doi=10.1109%2fCOASE.2018.8560374&partnerID=40&md5=c859a21986a2be1e769821ca4778de95 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE Computer Society |
publisher.none.fl_str_mv |
IEEE Computer Society |
dc.source.none.fl_str_mv |
instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR |
instname_str |
Universidad del Rosario |
institution |
Universidad del Rosario |
reponame_str |
Repositorio Institucional EdocUR |
collection |
Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1803710536958869504 |