Application of Multilineal Regression Model for Measuring the Impact of Maintenance Service Providers’ Quality on Overall Healthcare Service Quality
Author(s) -
Antonio Miguel-Cruz,
Adriana Ríos Rincón
Publication year - 2014
Publication title -
biomedical engineering research
Language(s) - English
Resource type - Journals
eISSN - 2306-6008
pISSN - 2306-5990
DOI - 10.5963/ber0304001
Subject(s) - quality (philosophy) , healthcare service , service quality , service provider , health care , regression analysis , business , service (business) , computer science , marketing , economics , philosophy , epistemology , economic growth , machine learning
This research was aimed at examining how transaction performance regarding medical equipment maintenance affected overall healthcare service quality, understood as healthcare input and/or process quality. A cross-sectional, retrospective, exploratory study was thus designed for examining 719 maintenance transactions. Such sample size guaranteed 0.8 statistical power, having 0.2 effect size at =0.05. Secondary source data were taken from databases kept at two hospitals in Bogota, Colombia. A multivariate linear regression model was used for analysing the effect of turnaround time (TAT, the independent variable) in terms of hours on healthcare institution service quality (decreased overall quality, the dependent variable), controlling for the type of service provider carrying out the maintenance transaction (i.e. in-house or outsourcing) and the maintenance type concerned (i.e. preventive or corrective) in healthcare service quality. The results provided sufficient statistically significant support (p<0.001), good CI precision (+ 1.156, + 1.432) and low standard error (standard error=0.070), as healthcare service quality became negatively affected as TAT value increased due to low maintenance service provider quality or performance (i.e. TAT=+1.473). KeywordsMultivariate Linear Regression Model; Quality Management; Healthcare Service Operation Research; Maintenance; Medical Engineering; Clinical Engineering
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