Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-90966-6_7 http://hdl.handle.net/11449/222985 |
Resumo: | Emergency Care Networks (ECNs) are integrated healthcare systems comprised of emergency departments (EDs). ECNs are called to be the primary response of healthcare authorities to deal with the expected uptick in the future demands for emergency care during the current Covid-19 pandemic. Lean Six Sigma (LSS) has been proposed to address this challenge since it allows managers to detect factors contributing to the extended waiting times (WT) throughout the patient journey. The suggested framework follows the DMAIC cycle that was initiated with the project charter definition; in the meantime, a SIPOC diagram was drawn to analyze the emergency care process and pinpoint critical process variables. Following this, a nested Gage R&R study was undertaken to study the measurement system performance; subsequently, a normal-based capability analysis was carried out to determine how well the ECN process satisfies the specifications. The next step was to identify the potential causes separating the ECN nodes from the desired target. Afterwards, improvement strategies were devised to lessen the average WT. After suitable data collection, a before-and-after analysis was performed to verify the effectiveness of the implemented strategies. Ultimately, a control plan containing an I-MR control chart was designed to maintain the improvements achieved with the LSS implementation. The results revealed that the average WT of the showcased node passed from 190.02 min to 103.1 min whereas the long-term sigma level increased from −0.06 to 0.11. The proposed framework was validated through a case study including the involvement of a medium-sized hospital from the public sector. |
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Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case StudyCovid-19Emergency Care Networks (ECNs)HealthcareLean Six Sigma (LSS)Waiting times (WT)Emergency Care Networks (ECNs) are integrated healthcare systems comprised of emergency departments (EDs). ECNs are called to be the primary response of healthcare authorities to deal with the expected uptick in the future demands for emergency care during the current Covid-19 pandemic. Lean Six Sigma (LSS) has been proposed to address this challenge since it allows managers to detect factors contributing to the extended waiting times (WT) throughout the patient journey. The suggested framework follows the DMAIC cycle that was initiated with the project charter definition; in the meantime, a SIPOC diagram was drawn to analyze the emergency care process and pinpoint critical process variables. Following this, a nested Gage R&R study was undertaken to study the measurement system performance; subsequently, a normal-based capability analysis was carried out to determine how well the ECN process satisfies the specifications. The next step was to identify the potential causes separating the ECN nodes from the desired target. Afterwards, improvement strategies were devised to lessen the average WT. After suitable data collection, a before-and-after analysis was performed to verify the effectiveness of the implemented strategies. Ultimately, a control plan containing an I-MR control chart was designed to maintain the improvements achieved with the LSS implementation. The results revealed that the average WT of the showcased node passed from 190.02 min to 103.1 min whereas the long-term sigma level increased from −0.06 to 0.11. The proposed framework was validated through a case study including the involvement of a medium-sized hospital from the public sector.Department of Productivity and Innovation Universidad de la Costa CUCDepartment of Industrial Engineering Institución Universitaria ITSADepartment of Production Universidade Estadual Júlio de Mesquita Filho, Av. Ariberto P. Cunha 333Department of Maritime and Port Administration Corporación Universitaria ReformadaDepartment of Production Universidade Estadual Júlio de Mesquita Filho, Av. Ariberto P. Cunha 333Universidad de la Costa CUCInstitución Universitaria ITSAUniversidade Estadual Paulista (UNESP)Corporación Universitaria ReformadaOrtiz-Barrios, MiguelCoba-Blanco, DayanaJiménez-Delgado, GenettSalomon, Valerio A. P. [UNESP]López-Meza, Pedro2022-04-28T19:47:53Z2022-04-28T19:47:53Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject83-93http://dx.doi.org/10.1007/978-3-030-90966-6_7Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13097 LNCS, p. 83-93.1611-33490302-9743http://hdl.handle.net/11449/22298510.1007/978-3-030-90966-6_72-s2.0-85120631544Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2022-04-28T19:47:53Zoai:repositorio.unesp.br:11449/222985Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:45:31.546021Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
title |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
spellingShingle |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study Ortiz-Barrios, Miguel Covid-19 Emergency Care Networks (ECNs) Healthcare Lean Six Sigma (LSS) Waiting times (WT) |
title_short |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
title_full |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
title_fullStr |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
title_full_unstemmed |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
title_sort |
Implementation of Lean Six Sigma to Lessen Waiting Times in Public Emergency Care Networks: A Case Study |
author |
Ortiz-Barrios, Miguel |
author_facet |
Ortiz-Barrios, Miguel Coba-Blanco, Dayana Jiménez-Delgado, Genett Salomon, Valerio A. P. [UNESP] López-Meza, Pedro |
author_role |
author |
author2 |
Coba-Blanco, Dayana Jiménez-Delgado, Genett Salomon, Valerio A. P. [UNESP] López-Meza, Pedro |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidad de la Costa CUC Institución Universitaria ITSA Universidade Estadual Paulista (UNESP) Corporación Universitaria Reformada |
dc.contributor.author.fl_str_mv |
Ortiz-Barrios, Miguel Coba-Blanco, Dayana Jiménez-Delgado, Genett Salomon, Valerio A. P. [UNESP] López-Meza, Pedro |
dc.subject.por.fl_str_mv |
Covid-19 Emergency Care Networks (ECNs) Healthcare Lean Six Sigma (LSS) Waiting times (WT) |
topic |
Covid-19 Emergency Care Networks (ECNs) Healthcare Lean Six Sigma (LSS) Waiting times (WT) |
description |
Emergency Care Networks (ECNs) are integrated healthcare systems comprised of emergency departments (EDs). ECNs are called to be the primary response of healthcare authorities to deal with the expected uptick in the future demands for emergency care during the current Covid-19 pandemic. Lean Six Sigma (LSS) has been proposed to address this challenge since it allows managers to detect factors contributing to the extended waiting times (WT) throughout the patient journey. The suggested framework follows the DMAIC cycle that was initiated with the project charter definition; in the meantime, a SIPOC diagram was drawn to analyze the emergency care process and pinpoint critical process variables. Following this, a nested Gage R&R study was undertaken to study the measurement system performance; subsequently, a normal-based capability analysis was carried out to determine how well the ECN process satisfies the specifications. The next step was to identify the potential causes separating the ECN nodes from the desired target. Afterwards, improvement strategies were devised to lessen the average WT. After suitable data collection, a before-and-after analysis was performed to verify the effectiveness of the implemented strategies. Ultimately, a control plan containing an I-MR control chart was designed to maintain the improvements achieved with the LSS implementation. The results revealed that the average WT of the showcased node passed from 190.02 min to 103.1 min whereas the long-term sigma level increased from −0.06 to 0.11. The proposed framework was validated through a case study including the involvement of a medium-sized hospital from the public sector. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-04-28T19:47:53Z 2022-04-28T19:47:53Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-90966-6_7 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13097 LNCS, p. 83-93. 1611-3349 0302-9743 http://hdl.handle.net/11449/222985 10.1007/978-3-030-90966-6_7 2-s2.0-85120631544 |
url |
http://dx.doi.org/10.1007/978-3-030-90966-6_7 http://hdl.handle.net/11449/222985 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13097 LNCS, p. 83-93. 1611-3349 0302-9743 10.1007/978-3-030-90966-6_7 2-s2.0-85120631544 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
83-93 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808129459979550720 |