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 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.

• 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
• License: Abierto (Texto Completo)