Improving corrective maintenace efficiency in clinical engineering departments - Multiple linear regression and clustering techniques for analyzing quality and effectiveness of technical services

Cruz, Antonio Miguel; Barr, Cameron; Punales, Elsa P. Pozo

doi:https://doi.org/10.1109/MEMB.2007.364931

Ítem

Solo Metadatos

Improving corrective maintenace efficiency in clinical engineering departments - Multiple linear regression and clustering techniques for analyzing quality and effectiveness of technical services

https://repository.urosario.edu.co/handle/10336/26169
https://doi.org/10.1109/MEMB.2007.364931

Autores

Cruz, Antonio Miguel

Barr, Cameron

Punales, Elsa P. Pozo

Fecha

2007

Editor

Journal & Magazines

Buscar en:

Métricas alternativas

Abstract

Multiple linear regression and clustering techniques are tools that have been extensively applied in several financial, technical, and biomedical arenas, where vast quantities of data are produced and stored. These techniques show promise in analyzing the performance of departments responsible for and related to hospital equipment maintenance and, thereafter, identifying and improving areas of concern. As a contributory measure, this research is focused on the analysis of quality and effectiveness of corrective (nonscheduled) maintenance tasks in the healthcare environment and the improvement of those processes. The two main objectives of this research are to build a predictor for a TAT indicator to estimate its values and to use a numeric clustering technique to find possible causes of undesirable values of TAT.

Keywords

Algorithm , Article , Biomedical engineering , Cluster analysis , Device , Health care quality , Health service , Mathematical analysis , Medical audit , Multiple linear regression analysis , Policy