Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach

To date, there are no broadly accepted or accurate models to determine appropriate staffing [levels] for clinical engineering departments (CEDs). The purpose of this study is to determine what the determinants of the staffing levels are (total number of full time equivalents (FTEs)) in CEDs in healt...

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Autores:
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/22396
Acceso en línea:
https://doi.org/10.1080/03091902.2016.1243168
https://repository.urosario.edu.co/handle/10336/22396
Palabra clave:
Biomedical engineering
Health care
Hospitals
Industrial management
Maintenance
Multivariant analysis
Personnel selection
Clinical engineering
Healthcare organisations
Healthcare technology managements
Human resource planning
Maintenance departments
Multivariate regression analysis
Multivariate regression models
OR in health services
Regression analysis
Article
Benchmarking
Biomedical engineering
Bivariate analysis
Computer assisted tomography
Controlled study
Cross-sectional study
Health care organization
Health service
Hospital discharge
Hospital personnel
Human
Independent variable
Medical information
Multivariate logistic regression analysis
Priority journal
Radiotherapy
Sample size
Biomedical engineering
Hospital personnel
Hospital service
Manpower
Statistical model
Statistics and numerical data
Biomedical Engineering
Cross-Sectional Studies
Humans
Clinical engineering
Human resource planning
Maintenance
Multiple criteria analysis
OR in health services
Hospital
Hospital
Statistical
Maintenance and Engineering
Models
Personnel
Rights
License
Abierto (Texto Completo)
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spelling dc42dfaf-7fb2-4603-9516-001a56e2500b-129801f13-67da-4abe-bdac-0d9d1e44b422-12020-05-25T23:56:19Z2020-05-25T23:56:19Z2017To date, there are no broadly accepted or accurate models to determine appropriate staffing [levels] for clinical engineering departments (CEDs). The purpose of this study is to determine what the determinants of the staffing levels are (total number of full time equivalents (FTEs)) in CEDs in healthcare organisations. In doing so, we used a cross-sectional exploratory approach by using a multivariate regression model over a secondary source of data information from the AAMI Benchmarking Solutions—Healthcare Technology Management database. Two hundred and one healthcare organisations were included in our study. Our study revealed that on average, there are almost 14 biomedical technicians (BMETs) per clinical engineer and one FTE per 1083.72 devices (SD 545.69). The results of this study also revealed that the total number of devices and the total technology management hours devoted to these devices positively affects the number of FTEs in a CED, whereas the hospital complexity, measured by healthcare organisation patient discharges matters inversely. The most important factor that matters in the number of FTEs in CEDs was the total technology management hours devoted to devices. A value of explained variance (i.e. R2) of 85% was obtained, indicating the strong power of the prediction accuracy of our multivariate regression model. © 2016 Informa UK Limited, trading as Taylor and Francis Group.application/pdfhttps://doi.org/10.1080/03091902.2016.12431683091902https://repository.urosario.edu.co/handle/10336/22396engTaylor and Francis Ltd164No. 2151Journal of Medical Engineering and TechnologyVol. 41Journal of Medical Engineering and Technology, ISSN:3091902, Vol.41, No.2 (2017); pp. 151-164https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992084122&doi=10.1080%2f03091902.2016.1243168&partnerID=40&md5=36b998b6cb67b18fc0edb7b403107889Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURBiomedical engineeringHealth careHospitalsIndustrial managementMaintenanceMultivariant analysisPersonnel selectionClinical engineeringHealthcare organisationsHealthcare technology managementsHuman resource planningMaintenance departmentsMultivariate regression analysisMultivariate regression modelsOR in health servicesRegression analysisArticleBenchmarkingBiomedical engineeringBivariate analysisComputer assisted tomographyControlled studyCross-sectional studyHealth care organizationHealth serviceHospital dischargeHospital personnelHumanIndependent variableMedical informationMultivariate logistic regression analysisPriority journalRadiotherapySample sizeBiomedical engineeringHospital personnelHospital serviceManpowerStatistical modelStatistics and numerical dataBiomedical EngineeringCross-Sectional StudiesHumansClinical engineeringHuman resource planningMaintenanceMultiple criteria analysisOR in health servicesHospitalHospitalStatisticalMaintenance and EngineeringModelsPersonnelDeterminants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approacharticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Miguel Cruz A.Guarín M.R.10336/22396oai:repository.urosario.edu.co:10336/223962022-05-02 07:37:14.141334https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
title Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
spellingShingle Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
Biomedical engineering
Health care
Hospitals
Industrial management
Maintenance
Multivariant analysis
Personnel selection
Clinical engineering
Healthcare organisations
Healthcare technology managements
Human resource planning
Maintenance departments
Multivariate regression analysis
Multivariate regression models
OR in health services
Regression analysis
Article
Benchmarking
Biomedical engineering
Bivariate analysis
Computer assisted tomography
Controlled study
Cross-sectional study
Health care organization
Health service
Hospital discharge
Hospital personnel
Human
Independent variable
Medical information
Multivariate logistic regression analysis
Priority journal
Radiotherapy
Sample size
Biomedical engineering
Hospital personnel
Hospital service
Manpower
Statistical model
Statistics and numerical data
Biomedical Engineering
Cross-Sectional Studies
Humans
Clinical engineering
Human resource planning
Maintenance
Multiple criteria analysis
OR in health services
Hospital
Hospital
Statistical
Maintenance and Engineering
Models
Personnel
title_short Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
title_full Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
title_fullStr Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
title_full_unstemmed Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
title_sort Determinants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approach
dc.subject.keyword.spa.fl_str_mv Biomedical engineering
Health care
Hospitals
Industrial management
Maintenance
Multivariant analysis
Personnel selection
Clinical engineering
Healthcare organisations
Healthcare technology managements
Human resource planning
Maintenance departments
Multivariate regression analysis
Multivariate regression models
OR in health services
Regression analysis
Article
Benchmarking
Biomedical engineering
Bivariate analysis
Computer assisted tomography
Controlled study
Cross-sectional study
Health care organization
Health service
Hospital discharge
Hospital personnel
Human
Independent variable
Medical information
Multivariate logistic regression analysis
Priority journal
Radiotherapy
Sample size
Biomedical engineering
Hospital personnel
Hospital service
Manpower
Statistical model
Statistics and numerical data
Biomedical Engineering
Cross-Sectional Studies
Humans
Clinical engineering
Human resource planning
Maintenance
Multiple criteria analysis
OR in health services
topic Biomedical engineering
Health care
Hospitals
Industrial management
Maintenance
Multivariant analysis
Personnel selection
Clinical engineering
Healthcare organisations
Healthcare technology managements
Human resource planning
Maintenance departments
Multivariate regression analysis
Multivariate regression models
OR in health services
Regression analysis
Article
Benchmarking
Biomedical engineering
Bivariate analysis
Computer assisted tomography
Controlled study
Cross-sectional study
Health care organization
Health service
Hospital discharge
Hospital personnel
Human
Independent variable
Medical information
Multivariate logistic regression analysis
Priority journal
Radiotherapy
Sample size
Biomedical engineering
Hospital personnel
Hospital service
Manpower
Statistical model
Statistics and numerical data
Biomedical Engineering
Cross-Sectional Studies
Humans
Clinical engineering
Human resource planning
Maintenance
Multiple criteria analysis
OR in health services
Hospital
Hospital
Statistical
Maintenance and Engineering
Models
Personnel
dc.subject.keyword.eng.fl_str_mv Hospital
Hospital
Statistical
Maintenance and Engineering
Models
Personnel
description To date, there are no broadly accepted or accurate models to determine appropriate staffing [levels] for clinical engineering departments (CEDs). The purpose of this study is to determine what the determinants of the staffing levels are (total number of full time equivalents (FTEs)) in CEDs in healthcare organisations. In doing so, we used a cross-sectional exploratory approach by using a multivariate regression model over a secondary source of data information from the AAMI Benchmarking Solutions—Healthcare Technology Management database. Two hundred and one healthcare organisations were included in our study. Our study revealed that on average, there are almost 14 biomedical technicians (BMETs) per clinical engineer and one FTE per 1083.72 devices (SD 545.69). The results of this study also revealed that the total number of devices and the total technology management hours devoted to these devices positively affects the number of FTEs in a CED, whereas the hospital complexity, measured by healthcare organisation patient discharges matters inversely. The most important factor that matters in the number of FTEs in CEDs was the total technology management hours devoted to devices. A value of explained variance (i.e. R2) of 85% was obtained, indicating the strong power of the prediction accuracy of our multivariate regression model. © 2016 Informa UK Limited, trading as Taylor and Francis Group.
publishDate 2017
dc.date.created.spa.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-05-25T23:56:19Z
dc.date.available.none.fl_str_mv 2020-05-25T23:56:19Z
dc.type.eng.fl_str_mv article
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_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1080/03091902.2016.1243168
dc.identifier.issn.none.fl_str_mv 3091902
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/22396
url https://doi.org/10.1080/03091902.2016.1243168
https://repository.urosario.edu.co/handle/10336/22396
identifier_str_mv 3091902
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 164
dc.relation.citationIssue.none.fl_str_mv No. 2
dc.relation.citationStartPage.none.fl_str_mv 151
dc.relation.citationTitle.none.fl_str_mv Journal of Medical Engineering and Technology
dc.relation.citationVolume.none.fl_str_mv Vol. 41
dc.relation.ispartof.spa.fl_str_mv Journal of Medical Engineering and Technology, ISSN:3091902, Vol.41, No.2 (2017); pp. 151-164
dc.relation.uri.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992084122&doi=10.1080%2f03091902.2016.1243168&partnerID=40&md5=36b998b6cb67b18fc0edb7b403107889
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Taylor and Francis Ltd
institution Universidad del Rosario
dc.source.instname.spa.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.spa.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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