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

Antonio Miguel Cruz, Cameron Barr, Elsa P. Pozo Puñales

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.
Translated title of the contributionMejora de la eficacia del mantenimiento correctivo en los departamentos de ingeniería clínica - Técnicas de regresión lineal múltiple y de agrupación para analizar la calidad y la eficacia de los servicios técnicos
Original languageEnglish (US)
Pages (from-to)60-65
Number of pages6
JournalIEEE Engineering in Medicine and Biology Magazine
Volume26
Issue number3
DOIs
StatePublished - May 29 2007

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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