title: Clustering techniques: measuring the performance of contract service providers.
abstract: This paper investigates the use of clustering technique to characterize the providers of maintenance services in a health-care institution according to their performance. A characterization of the inventory of equipment from seven pilot areas was carried out first (including 264 medical devices). The characterization study concluded that the inventory on a whole is old [exploitation time (ET)/useful life (UL) average is 0.78] and has high maintenance service costs relative to the original cost of acquisition (service cost /acquisition cost average 8.61%). A monitoring of the performance of maintenance service providers was then conducted. The variables monitored were response time (RT), service time (ST), availability, and turnaround time (TAT). Finally, the study grouped maintenance service providers into clusters according to performance. The study grouped maintenance service providers into the following clusters. Cluster 0: Identified with the best performance, the lowest values of TAT, RT, and ST, with an average TAT value of 1.46 days; Clusters 1 and 2: Identified with the poorest performance, highest values of TAT, RT, and ST, and an average TAT value of 9.79 days; and Cluster 3: Identified by medium-quality performance, intermediate values of TAT, RT, and ST, and an average TAT value of 2.56 days.
authors.affiliation: Facultadde Medicina, Dpto. de Ciencias Básicas Universidad del Rosario, Calle 63D # 24-31, 7 de Agosto, Bogotá DC, Colombia. antonio.cruz43@urosario.edu.co
authors.forename: Antonio Miguel
authors.initials: AM
authors.lastname: Cruz
authors.forename: Sandra Patricia Usaquén
authors.initials: SP
authors.lastname: Perilla
authors.forename: Nidia Nelly Vanegas
authors.initials: NN
authors.lastname: Pabón
mesh_terms: D016000:Cluster Analysis; D003284:Contract Services / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D038622:Internationality; D008283:Maintenance / Q000706:statistics & numerical data*