title: Determinants in the number of staff in hospitals' maintenance departments: a multivariate regression analysis approach.
abstract: 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.
authors.affiliation: a Department of Occupational Therapy , Faculty of Rehabilitation Medicine, University of Alberta , Edmonton , Canada.|b School of Medicine and Health Sciences, Universidad del Rosario , Bogotá , Colombia.
authors.forename: Antonio
authors.initials: A
authors.lastname: Miguel Cruz
authors.affiliation: b School of Medicine and Health Sciences, Universidad del Rosario , Bogotá , Colombia.
authors.forename: Mayra R
authors.initials: MR
authors.lastname: Guarín
mesh_terms: D001698:Biomedical Engineering / Q000706:statistics & numerical data*; D003430:Cross-Sectional Studies; D006801:Humans; D008284:Maintenance and Engineering, Hospital / Q000706:statistics & numerical data*; D015233:Models, Statistical; D010564:Personnel, Hospital / Q000706:statistics & numerical data*; D000078329:Workforce
keywords: Maintenance; clinical engineering; human resource planning; multiple criteria analysis; OR in health services