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

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dc.creatorMiguel Cruz A.spa
dc.creatorGuarín M.R.spa
dc.date.accessioned2020-05-25T23:56:19Z
dc.date.available2020-05-25T23:56:19Z
dc.date.created2017spa
dc.description.abstractTo 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.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1080/03091902.2016.1243168
dc.identifier.issn3091902
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22396
dc.language.isoengspa
dc.publisherTaylor and Francis Ltdspa
dc.relation.citationEndPage164
dc.relation.citationIssueNo. 2
dc.relation.citationStartPage151
dc.relation.citationTitleJournal of Medical Engineering and Technology
dc.relation.citationVolumeVol. 41
dc.relation.ispartofJournal of Medical Engineering and Technology, ISSN:3091902, Vol.41, No.2 (2017); pp. 151-164spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84992084122&doi=10.1080%2f03091902.2016.1243168&partnerID=40&md5=36b998b6cb67b18fc0edb7b403107889spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordBiomedical engineeringspa
dc.subject.keywordHospitaleng
dc.subject.keywordHospitalsspa
dc.subject.keywordIndustrial managementspa
dc.subject.keywordMaintenancespa
dc.subject.keywordMultivariant analysisspa
dc.subject.keywordPersonnel selectionspa
dc.subject.keywordClinical engineeringspa
dc.subject.keywordHealthcare organisationsspa
dc.subject.keywordHealthcare technology managementsspa
dc.subject.keywordHuman resource planningspa
dc.subject.keywordMaintenance departmentsspa
dc.subject.keywordMultivariate regression analysisspa
dc.subject.keywordMultivariate regression modelsspa
dc.subject.keywordOR in health servicesspa
dc.subject.keywordRegression analysisspa
dc.subject.keywordArticlespa
dc.subject.keywordBenchmarkingspa
dc.subject.keywordBiomedical engineeringspa
dc.subject.keywordBivariate analysisspa
dc.subject.keywordComputer assisted tomographyspa
dc.subject.keywordControlled studyspa
dc.subject.keywordCross-sectional studyspa
dc.subject.keywordHealth care organizationspa
dc.subject.keywordHealth servicespa
dc.subject.keywordHospital dischargespa
dc.subject.keywordHospital personnelspa
dc.subject.keywordHumanspa
dc.subject.keywordIndependent variablespa
dc.subject.keywordMedical informationspa
dc.subject.keywordMultivariate logistic regression analysisspa
dc.subject.keywordPriority journalspa
dc.subject.keywordRadiotherapyspa
dc.subject.keywordSample sizespa
dc.subject.keywordBiomedical engineeringspa
dc.subject.keywordHospital personnelspa
dc.subject.keywordHospital servicespa
dc.subject.keywordManpowerspa
dc.subject.keywordStatistical modelspa
dc.subject.keywordStatistics and numerical dataspa
dc.subject.keywordBiomedical Engineeringspa
dc.subject.keywordCross-Sectional Studiesspa
dc.subject.keywordHumansspa
dc.subject.keywordMaintenance and Engineeringeng
dc.subject.keywordModelseng
dc.subject.keywordPersonneleng
dc.subject.keywordClinical engineeringspa
dc.subject.keywordHuman resource planningspa
dc.subject.keywordMaintenancespa
dc.subject.keywordMultiple criteria analysisspa
dc.subject.keywordOR in health servicesspa
dc.titleDeterminants in the number of staff in hospitals’ maintenance departments: a multivariate regression analysis approachspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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