RESUMO
En este trabajo se exponen los resultados obtenidos de la aplicación de técnicas de descubrimiento de asociaciones y de agrupamiento para resolver el problema de la baja eficiencia presentado en un servicio de esterilización de un hospital en estudio. El objetivo fue detectar y discriminar las causas fundamentales que contribuyeron al surgimiento del problema presentado para luego solucionarlo. Para realizar esta investigación se recabó la información contenida en las solicitudes de servicio de mantenimiento correctivo y las órdenes de trabajos durante el período 2002-2004. Primeramente se segmentó la información contenida en el indicador en estudio: razón de las solicitudes de servicio de mantenimiento correctivo vs. cantidad de equipos por tipos de equipos, por servicios, por fabricante (OEM, del inglés Original Equipment Manufacturer) y por modelos. Luego con las técnicas de descubrimiento de asociaciones aplicadas se encontraron las causas fundamentales por las cuales se solicitaban los reportes de servicios. Éstas fueron: falta de entrenamiento en usuarios, fallos intrínsecos en los dispositivos médicos y malas políticas en el establecimiento de la frecuencia del mantenimiento programado. Las técnicas de agrupamientos pudieron discriminar las causas fundamentales por las cuales los dispositivos médicos del servicio de esterilización fallaban. Éstas fueron debido a fallos en el sistema de suministro de vapor y agua que alimenta las unidades de esterilización (en un 75% de los casos). Se tomaron medidas correctoras durante el período 2005-2006, que contribuyeron a que el indicador bajo estudio disminuyera de 6,4 a 0,4 unidades.
In this research association discovering and clustering techniques for the resolution of the low efficiency problem in the sterilization service in a hospital under study were used. The aim was to find and to discriminate the main causes of the problem under study and then to apply corrective solutions. To conduct this research the information contained in corrective maintenance work orders and service requests in the period under study (2002-2004) was collected. First a segmentation of the information was carried out using the indicator: corrective service request versus number of medical devices. The levels of the information segmentation were: equipment types, services or cost centre, original equipment manufacturer and models. Then the association discovery technique was used. It revealed that the main causes of low efficiency in sterilization service were: users' training (errors in operation procedures), intrinsic failures in medical devices, and bad scheduled maintenance policies. Clustering technique uncovered the main causes of failures: malfunctioning of the power supply system (steam and water, in 75% of all cases). With the evidence obtained corrective actions were taken. The service requests dropped dramatically from 6.4 to 0.4 during the period 2005-2006.
RESUMO
OBJECTIVES: This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. MATERIALS AND METHODS: The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). RESULTS: Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. CONCLUSIONS: This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.