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...
- 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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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 |
_version_ |
1814167641722454016 |