A predictive and prescriptive analytical framework for scheduling language medical interpreters
Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatien...
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| Veröffentlicht in: | Health care management science Jg. 24; H. 3; S. 531 - 550 |
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01.09.2021
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| Abstract | Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives. |
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| AbstractList | Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives. Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives.Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives. |
| Author | Ahmed, Abdulaziz Frohn, Elizabeth |
| Author_xml | – sequence: 1 givenname: Abdulaziz surname: Ahmed fullname: Ahmed, Abdulaziz email: aaahmed@umn.edu organization: Business Department, University of Minnesota – sequence: 2 givenname: Elizabeth surname: Frohn fullname: Frohn, Elizabeth organization: Cloudbreak Healthath |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33629192$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.eswa.2008.11.049 10.1186/1471-2105-15-276 10.15171/ijhpm.2013.53 10.1016/j.compag.2015.05.001 10.1016/j.cie.2010.05.005 10.1097/00006565-200208000-00010 10.1613/jair.953 10.1016/j.ejor.2005.12.015 10.1155/2017/6563498 10.1080/07408170701244687 10.1007/978-1-4419-9326-7 10.1007/978-3-642-27242-4_4 10.1080/02286203.2007.11442393 10.1016/j.cie.2016.02.023 10.1136/bmjopen-2017-018628 10.3390/a7010166 10.12968/bjhc.2019.0067 10.1016/j.ijpe.2018.11.024 10.1377/hlthaff.24.2.435 10.1016/j.ejor.2011.10.046 10.3390/a10040114 10.1108/K-10-2018-0520 10.1016/j.ejor.2012.11.029 10.1007/s00170-012-4119-y 10.1155/2019/4359719 10.5582/bst.2017.01035 10.1093/intqhc/mzl069 10.2105/AJPH.94.5.866 10.1542/peds.2005-0521 10.1007/s10729-008-9087-2 10.1007/978-981-13-1280-9 10.1353/book6604 10.1109/SSCI.2017.8280850 10.5430/jha.v6n2p21 10.1016/j.ejor.2005.10.008 10.1016/j.ejor.2009.01.042 10.1007/s10951-010-0188-7 10.1177/1043659617747523 |
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| Keywords | Operations research Multi-objective integer programming Healthcare service Interpreting service Predictive modeling Scheduling Prescriptive modeling |
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| References | Shahnazari-ShahrezaeiPTavakkoli-MoghaddamRKazemipoorHSolving a new fuzzy multi-objective model for a multi-skilled manpower scheduling problem by particle swarm optimization and elite tabu searchInt J Adv Manuf Technol2013649–121517154010.1007/s00170-012-4119-y “Immigrants in Minnesota | American Immigration Council.” [Online]. Available: https://www.americanimmigrationcouncil.org/research/immigrants-in-minnesota. Accessed 03 Feb 2020 KoelemanPMBhulaiSvan MeersbergenMOptimal patient and personnel scheduling policies for care-at-home service facilitiesEur J Oper Res2012219355756310.1016/j.ejor.2011.10.046 AlfaresHKA simulation approach for stochastic employee days-off schedulingInt J Model Simul200727191510.1080/02286203.2007.11442393 ChawlaNVBowyerKWHallLOKegelmeyerWPSMOTE: synthetic minority over-sampling techniqueJ Artif Intell Res20021632135710.1613/jair.953 JacobsEAShepardDSSuayaJAStoneE-LOvercoming language barriers in health care: costs and benefits of interpreter servicesAm J Public Health200494586686910.2105/AJPH.94.5.866 ChuSCGenerating, scheduling and rostering of shift crew-duties: applications at the Hong Kong international airportEur J Oper Res200717731764177810.1016/j.ejor.2005.10.008 Rajeswari M, Amudhavel J, Pothula S, Dhavachelvan P (2017) Directed bee colony optimization algorithm to solve the nurse rostering problem. Comput Intell Neurosci, https://www.hindawi.com/journals/cin/2017/6563498/abs/. Accessed 26 Nov. 2018 QuanGGreenwoodGWLiuDHuSSearching for multiobjective preventive maintenance schedules: combining preferences with evolutionary algorithmsEur J Oper Res200717731969198410.1016/j.ejor.2005.12.015 JakobWBlumeCPareto optimization or cascaded weighted sum: a comparison of conceptsAlgorithms20147116618510.3390/a7010166 WilsonCPatient safety and healthcare quality: the case for language accessInt J Health Policy Manag20131425125310.15171/ijhpm.2013.53 Kalita S, Biswas M (2018) Recent developments in machine learning and data analytics. Springer, New York BagheriMDevinAGIzanlooAAn application of stochastic programming method for nurse scheduling problem in real word hospitalComput Ind Eng20169619220010.1016/j.cie.2016.02.023 AvramidisANChanWGendreauML’ecuyerPPisacaneOOptimizing daily agent scheduling in a multiskill call centerEur J Oper Res2010200382283210.1016/j.ejor.2009.01.042 De GranoMLMedeirosDJEitelDAccommodating individual preferences in nurse scheduling via auctions and optimizationHealth Care Manag Sci200812322824210.1007/s10729-008-9087-2 TatsumiKYamashikiYCanales TorresMATaipeCLRCrop classification of upland fields using random forest of time-series Landsat 7 ETM+ dataComput Electron Agric201511517117910.1016/j.compag.2015.05.001 HelberSHenkenKProfit-oriented shift scheduling of inbound contact centers with skills-based routing, impatient customers, and retrialsSpectr2010321109134 Ahmed A, Hamasha MM (2018) Scheduling language interpreters at a medical center: An integer programming approach. IISE Annual Conference and Expo, Orlando, FL, pp 312–317 KuLFloresGPay now or pay later: providing interpreter services in health careHealth Aff200524243544410.1377/hlthaff.24.2.435 ArazOMOlsonDRamirez-NafarrateAPredictive analytics for hospital admissions from the emergency department using triage informationInt J Prod Econ201920819920710.1016/j.ijpe.2018.11.024 CohenALRivaraFMarcuseEKMcPhillipsHDavisRAre language barriers associated with serious medical events in hospitalized pediatric patients?Pediatrics2005116357557910.1542/peds.2005-0521 Van den BerghJBeliënJDe BrueckerPDemeulemeesterEDe BoeckLPersonnel scheduling: a literature reviewEur J Oper Res2013226336738510.1016/j.ejor.2012.11.029 Kadri F, Harrou F, Sun Y (2017) A multivariate time series approach to forecasting daily attendances at hospital emergency department. IEEE symposium series on computational intelligence (SSCI), Honolulu, HI, p 1–6. https://doi.org/10.1109/SSCI.2017.8280850 “Title VI of the Civil Rights Act of 1964,” 25-May-2016. [Online]. Available: https://www.justice.gov/crt/fcs/titlevi. Accessed 20 Feb 2020 Nas S, Koyuncu M (2019) Emergency department capacity planning: a recurrent neural network and simulation approach. Comput Math Method M. https://doi.org/10.1155/2019/4359719 ChiamTCHooverSMosbyDCaplanRDolmanSGbadeboAJacksonEMeeting demand: a multi-method approach to optimizing hospital language interpreter staffingJ Hosp Admin2017622110.5430/jha.v6n2p21 TyralisHPapacharalampousGVariable selection in time series forecasting using random forestsAlgorithms201710411410.3390/a10040114 Awadallah MA, Khader AT, Al-Betar MA, Bolaji AL (2011) Nurse rostering using modified harmony search algorithm. International Conference on Swarm, Evolutionary, and Memetic Computing. Springer, Berlin, Heidelberg, pp 27–37 U. S. C. Bureau “American FactFinder.” [Online]. Available: https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml. Accessed 12 Feb 2020 DiviCKossRGSchmaltzSPLoebJMLanguage proficiency and adverse events in US hospitals: a pilot studyInt J Qual Health Care2007192606710.1093/intqhc/mzl069 Yousefi M, Yousefi M, Fathi M, Fogliatto FS (2019) Patient visit forecasting in an emergency department using a deep neural network approach. Kybernetes 49(9):2335–2348 Wu H, Cai Y, Wu Y, Zhong R, Li Q, Zheng J, Lin D, Li Y (2017) Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. Biosci Trends 11(3) 292–296 Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. ArXiv Prepr. ArXiv160500003 MitchellSOSullivanMDunningIPuLP: a linear programming toolkit for python2011AucklandThe University of Auckland FloresGRabke-VeraniJPineWSabharwalAThe importance of cultural and linguistic issues in the emergency care of childrenPediatr Emerg Care200218427128410.1097/00006565-200208000-00010 ZhangCMaYEnsemble machine learning: methods and applications2012New YorkSpringer10.1007/978-1-4419-9326-7 PedregosaFVaroquauxGGramfortAMichelVThirionBGriselOBlondelMPrettenhoferPWeissRDubourgVVanderplasJScikit-learn: machine mearning in PythonJ Mach Learn Res20111228252830 RongAMonthly tour scheduling models with mixed skills considering weekend off requirementsComput Ind Eng201059233434310.1016/j.cie.2010.05.005 YangK-KWebsterSRubenRAAn evaluation of worker cross training and flexible workdays in job shopsIIE Trans200739773574610.1080/07408170701244687 TsaiC-CLiSHA two-stage modeling with genetic algorithms for the nurse scheduling problemExpert Syst Appl20093659506951210.1016/j.eswa.2008.11.049 Gaiba F (1998) The origins of simultaneous interpretation: the Nuremberg trial. University of Ottawa Press, Ottawa, Canada KaneMJPriceNScotchMRabinowitzPComparison of ARIMA and random Forest time series models for prediction of avian influenza H5N1 outbreaksBMC Bioinforma201415127610.1186/1471-2105-15-276 ChoudhuryAUrenaEForecasting hourly emergency department arrival using time series analysisBr J Healthc Manag2020261344310.12968/bjhc.2019.0067 EstradaRDMessiasDKHLanguage co-construction and collaboration in interpreter-mediated primary care encounters with hispanic adultsJ Transcult Nurs201729649850510.1177/1043659617747523 BreimanLBagging predictorsMach Learn1996242123140 Wilson-StronksAGalvezEExploring cultural and linguistic services in the nation’s hospitals: a report of findings2007Oakbrook TerraceThe Joint Commission McInroy B (2016) Smote and merformance measures for machine learning applied to real-time bidding. Doctoral dissertation, Trent university JuangW-CHuangS-JHuangF-DChengP-WWannS-RApplication of time series analysis in modelling and forecasting emergency department visits in a medical Centre in southern TaiwanBMJ Open201771110.1136/bmjopen-2017-018628 CordeauJ-FLaporteGPasinFRopkeSScheduling technicians and tasks in a telecommunications companyJ Sched201013439340910.1007/s10951-010-0188-7 L Breiman (9536_CR42) 1996; 24 S Mitchell (9536_CR49) 2011 9536_CR25 TC Chiam (9536_CR38) 2017; 6 9536_CR28 9536_CR29 9536_CR7 A Wilson-Stronks (9536_CR8) 2007 SC Chu (9536_CR13) 2007; 177 L Ku (9536_CR6) 2005; 24 ML De Grano (9536_CR12) 2008; 12 RD Estrada (9536_CR36) 2017; 29 NV Chawla (9536_CR45) 2002; 16 9536_CR2 9536_CR1 AL Cohen (9536_CR3) 2005; 116 F Pedregosa (9536_CR48) 2011; 12 J Van den Bergh (9536_CR11) 2013; 226 C Wilson (9536_CR9) 2013; 1 K-K Yang (9536_CR19) 2007; 39 9536_CR15 G Flores (9536_CR5) 2002; 18 PM Koeleman (9536_CR14) 2012; 219 9536_CR10 MJ Kane (9536_CR33) 2014; 15 H Tyralis (9536_CR40) 2017; 10 K Tatsumi (9536_CR35) 2015; 115 P Shahnazari-Shahrezaei (9536_CR16) 2013; 64 S Helber (9536_CR18) 2010; 32 C Zhang (9536_CR43) 2012 9536_CR46 A Choudhury (9536_CR30) 2020; 26 W Jakob (9536_CR47) 2014; 7 A Rong (9536_CR20) 2010; 59 OM Araz (9536_CR27) 2019; 208 C Divi (9536_CR4) 2007; 19 9536_CR41 9536_CR44 9536_CR34 W-C Juang (9536_CR32) 2017; 7 9536_CR39 AN Avramidis (9536_CR17) 2010; 200 9536_CR31 C-C Tsai (9536_CR21) 2009; 36 G Quan (9536_CR22) 2007; 177 EA Jacobs (9536_CR37) 2004; 94 HK Alfares (9536_CR24) 2007; 27 J-F Cordeau (9536_CR26) 2010; 13 M Bagheri (9536_CR23) 2016; 96 |
| References_xml | – reference: Van den BerghJBeliënJDe BrueckerPDemeulemeesterEDe BoeckLPersonnel scheduling: a literature reviewEur J Oper Res2013226336738510.1016/j.ejor.2012.11.029 – reference: KaneMJPriceNScotchMRabinowitzPComparison of ARIMA and random Forest time series models for prediction of avian influenza H5N1 outbreaksBMC Bioinforma201415127610.1186/1471-2105-15-276 – reference: U. S. C. Bureau “American FactFinder.” [Online]. Available: https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml. Accessed 12 Feb 2020 – reference: KoelemanPMBhulaiSvan MeersbergenMOptimal patient and personnel scheduling policies for care-at-home service facilitiesEur J Oper Res2012219355756310.1016/j.ejor.2011.10.046 – reference: RongAMonthly tour scheduling models with mixed skills considering weekend off requirementsComput Ind Eng201059233434310.1016/j.cie.2010.05.005 – reference: MitchellSOSullivanMDunningIPuLP: a linear programming toolkit for python2011AucklandThe University of Auckland – reference: AvramidisANChanWGendreauML’ecuyerPPisacaneOOptimizing daily agent scheduling in a multiskill call centerEur J Oper Res2010200382283210.1016/j.ejor.2009.01.042 – reference: Kadri F, Harrou F, Sun Y (2017) A multivariate time series approach to forecasting daily attendances at hospital emergency department. IEEE symposium series on computational intelligence (SSCI), Honolulu, HI, p 1–6. https://doi.org/10.1109/SSCI.2017.8280850 – reference: Wu H, Cai Y, Wu Y, Zhong R, Li Q, Zheng J, Lin D, Li Y (2017) Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. Biosci Trends 11(3) 292–296 – reference: TatsumiKYamashikiYCanales TorresMATaipeCLRCrop classification of upland fields using random forest of time-series Landsat 7 ETM+ dataComput Electron Agric201511517117910.1016/j.compag.2015.05.001 – reference: FloresGRabke-VeraniJPineWSabharwalAThe importance of cultural and linguistic issues in the emergency care of childrenPediatr Emerg Care200218427128410.1097/00006565-200208000-00010 – reference: Wilson-StronksAGalvezEExploring cultural and linguistic services in the nation’s hospitals: a report of findings2007Oakbrook TerraceThe Joint Commission – reference: ChiamTCHooverSMosbyDCaplanRDolmanSGbadeboAJacksonEMeeting demand: a multi-method approach to optimizing hospital language interpreter staffingJ Hosp Admin2017622110.5430/jha.v6n2p21 – reference: BreimanLBagging predictorsMach Learn1996242123140 – reference: PedregosaFVaroquauxGGramfortAMichelVThirionBGriselOBlondelMPrettenhoferPWeissRDubourgVVanderplasJScikit-learn: machine mearning in PythonJ Mach Learn Res20111228252830 – reference: Shahnazari-ShahrezaeiPTavakkoli-MoghaddamRKazemipoorHSolving a new fuzzy multi-objective model for a multi-skilled manpower scheduling problem by particle swarm optimization and elite tabu searchInt J Adv Manuf Technol2013649–121517154010.1007/s00170-012-4119-y – reference: Gaiba F (1998) The origins of simultaneous interpretation: the Nuremberg trial. University of Ottawa Press, Ottawa, Canada – reference: TsaiC-CLiSHA two-stage modeling with genetic algorithms for the nurse scheduling problemExpert Syst Appl20093659506951210.1016/j.eswa.2008.11.049 – reference: TyralisHPapacharalampousGVariable selection in time series forecasting using random forestsAlgorithms201710411410.3390/a10040114 – reference: Kalita S, Biswas M (2018) Recent developments in machine learning and data analytics. Springer, New York – reference: ChoudhuryAUrenaEForecasting hourly emergency department arrival using time series analysisBr J Healthc Manag2020261344310.12968/bjhc.2019.0067 – reference: Ahmed A, Hamasha MM (2018) Scheduling language interpreters at a medical center: An integer programming approach. IISE Annual Conference and Expo, Orlando, FL, pp 312–317 – reference: YangK-KWebsterSRubenRAAn evaluation of worker cross training and flexible workdays in job shopsIIE Trans200739773574610.1080/07408170701244687 – reference: Khaidem L, Saha S, Dey SR (2016) Predicting the direction of stock market prices using random forest. ArXiv Prepr. ArXiv160500003 – reference: “Title VI of the Civil Rights Act of 1964,” 25-May-2016. [Online]. Available: https://www.justice.gov/crt/fcs/titlevi. Accessed 20 Feb 2020 – reference: “Immigrants in Minnesota | American Immigration Council.” [Online]. Available: https://www.americanimmigrationcouncil.org/research/immigrants-in-minnesota. Accessed 03 Feb 2020 – reference: ChuSCGenerating, scheduling and rostering of shift crew-duties: applications at the Hong Kong international airportEur J Oper Res200717731764177810.1016/j.ejor.2005.10.008 – reference: Yousefi M, Yousefi M, Fathi M, Fogliatto FS (2019) Patient visit forecasting in an emergency department using a deep neural network approach. Kybernetes 49(9):2335–2348 – reference: JakobWBlumeCPareto optimization or cascaded weighted sum: a comparison of conceptsAlgorithms20147116618510.3390/a7010166 – reference: QuanGGreenwoodGWLiuDHuSSearching for multiobjective preventive maintenance schedules: combining preferences with evolutionary algorithmsEur J Oper Res200717731969198410.1016/j.ejor.2005.12.015 – reference: CohenALRivaraFMarcuseEKMcPhillipsHDavisRAre language barriers associated with serious medical events in hospitalized pediatric patients?Pediatrics2005116357557910.1542/peds.2005-0521 – reference: De GranoMLMedeirosDJEitelDAccommodating individual preferences in nurse scheduling via auctions and optimizationHealth Care Manag Sci200812322824210.1007/s10729-008-9087-2 – reference: ZhangCMaYEnsemble machine learning: methods and applications2012New YorkSpringer10.1007/978-1-4419-9326-7 – reference: KuLFloresGPay now or pay later: providing interpreter services in health careHealth Aff200524243544410.1377/hlthaff.24.2.435 – reference: HelberSHenkenKProfit-oriented shift scheduling of inbound contact centers with skills-based routing, impatient customers, and retrialsSpectr2010321109134 – reference: CordeauJ-FLaporteGPasinFRopkeSScheduling technicians and tasks in a telecommunications companyJ Sched201013439340910.1007/s10951-010-0188-7 – reference: ArazOMOlsonDRamirez-NafarrateAPredictive analytics for hospital admissions from the emergency department using triage informationInt J Prod Econ201920819920710.1016/j.ijpe.2018.11.024 – reference: EstradaRDMessiasDKHLanguage co-construction and collaboration in interpreter-mediated primary care encounters with hispanic adultsJ Transcult Nurs201729649850510.1177/1043659617747523 – reference: AlfaresHKA simulation approach for stochastic employee days-off schedulingInt J Model Simul200727191510.1080/02286203.2007.11442393 – reference: ChawlaNVBowyerKWHallLOKegelmeyerWPSMOTE: synthetic minority over-sampling techniqueJ Artif Intell Res20021632135710.1613/jair.953 – reference: Nas S, Koyuncu M (2019) Emergency department capacity planning: a recurrent neural network and simulation approach. Comput Math Method M. https://doi.org/10.1155/2019/4359719 – reference: DiviCKossRGSchmaltzSPLoebJMLanguage proficiency and adverse events in US hospitals: a pilot studyInt J Qual Health Care2007192606710.1093/intqhc/mzl069 – reference: JuangW-CHuangS-JHuangF-DChengP-WWannS-RApplication of time series analysis in modelling and forecasting emergency department visits in a medical Centre in southern TaiwanBMJ Open201771110.1136/bmjopen-2017-018628 – reference: BagheriMDevinAGIzanlooAAn application of stochastic programming method for nurse scheduling problem in real word hospitalComput Ind Eng20169619220010.1016/j.cie.2016.02.023 – reference: JacobsEAShepardDSSuayaJAStoneE-LOvercoming language barriers in health care: costs and benefits of interpreter servicesAm J Public Health200494586686910.2105/AJPH.94.5.866 – reference: McInroy B (2016) Smote and merformance measures for machine learning applied to real-time bidding. Doctoral dissertation, Trent university – reference: Rajeswari M, Amudhavel J, Pothula S, Dhavachelvan P (2017) Directed bee colony optimization algorithm to solve the nurse rostering problem. Comput Intell Neurosci, https://www.hindawi.com/journals/cin/2017/6563498/abs/. Accessed 26 Nov. 2018 – reference: Awadallah MA, Khader AT, Al-Betar MA, Bolaji AL (2011) Nurse rostering using modified harmony search algorithm. International Conference on Swarm, Evolutionary, and Memetic Computing. Springer, Berlin, Heidelberg, pp 27–37 – reference: WilsonCPatient safety and healthcare quality: the case for language accessInt J Health Policy Manag20131425125310.15171/ijhpm.2013.53 – ident: 9536_CR2 – volume: 36 start-page: 9506 issue: 5 year: 2009 ident: 9536_CR21 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2008.11.049 – volume: 12 start-page: 2825 year: 2011 ident: 9536_CR48 publication-title: J Mach Learn Res – volume: 15 start-page: 276 issue: 1 year: 2014 ident: 9536_CR33 publication-title: BMC Bioinforma doi: 10.1186/1471-2105-15-276 – volume: 1 start-page: 251 issue: 4 year: 2013 ident: 9536_CR9 publication-title: Int J Health Policy Manag doi: 10.15171/ijhpm.2013.53 – volume: 115 start-page: 171 year: 2015 ident: 9536_CR35 publication-title: Comput Electron Agric doi: 10.1016/j.compag.2015.05.001 – volume: 59 start-page: 334 issue: 2 year: 2010 ident: 9536_CR20 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2010.05.005 – volume: 18 start-page: 271 issue: 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367 issue: 3 year: 2013 ident: 9536_CR11 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2012.11.029 – volume: 64 start-page: 1517 issue: 9–12 year: 2013 ident: 9536_CR16 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-012-4119-y – ident: 9536_CR29 doi: 10.1155/2019/4359719 – ident: 9536_CR34 doi: 10.5582/bst.2017.01035 – volume: 19 start-page: 60 issue: 2 year: 2007 ident: 9536_CR4 publication-title: Int J Qual Health Care doi: 10.1093/intqhc/mzl069 – volume: 32 start-page: 109 issue: 1 year: 2010 ident: 9536_CR18 publication-title: Spectr – volume-title: Exploring cultural and linguistic services in the nation’s hospitals: a report of findings year: 2007 ident: 9536_CR8 – volume: 94 start-page: 866 issue: 5 year: 2004 ident: 9536_CR37 publication-title: Am J Public Health doi: 10.2105/AJPH.94.5.866 – volume: 116 start-page: 575 issue: 3 year: 2005 ident: 9536_CR3 publication-title: Pediatrics doi: 10.1542/peds.2005-0521 – volume: 12 start-page: 228 issue: 3 year: 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