Building a New Predictor for Multiple Linear Regression Technique-based Corrective Maintenance Turnaround Time.

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,...

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Publicado en:Revista de Salud Pública Vol. 10; no. 5; pp. 808 - 818
Autores principales: Cruz, Antonio M., Barr, Cameron, Puñales-Pozo, Elsa
Formato: Artículo
Publicado: Universidad Nacional de Colombia dic2008
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Acceso en línea:Ver este registro en EBSCOhost
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      dt: dic2008
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      pub: Universidad Nacional de Colombia
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        10.1590/S0124-00642008000500013
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        atl: Building a New Predictor for Multiple Linear Regression Technique-based Corrective Maintenance Turnaround Time.
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          Cruz, Antonio M.
          Barr, Cameron
          Puñales-Pozo, Elsa
        affil:
          Escuela de Medicina, Universidad del Rosario Bogota D.C., Colombia
          Clinical Engineering Department, "Cira García" Hospital, Havana, Cuba
      su:
        Regression analysis
        Turnaround time
        Reaction time
        Cluster analysis (Statistics)
        Hospital engineering departments
        Biomedical engineering
        Colombia
      sug:
        subj:
          Colombia
          Regression analysis
          Turnaround time
          Reaction time
          Cluster analysis (Statistics)
          Hospital engineering departments
          Biomedical engineering
      keyword:
        biomedical technology
        decision support system
        management
        regression analysis
        estadística y datos numéricos
        gerencia
        Mantenimiento
      ab:
        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, 0.415 positive coefficient), stock service response time (Stock, 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, CE 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.
        Objetivos Construir un predictor que permita estimar los valores de tiempo de cambio de estado (del ingles TAT) y usar técnicas de conglomerados para encontrar las posibles causas de los valores no deseados de TAT. Materiales y Métodos Para llevar a cabo esta investigación se realizaron los siguientes pasos: Selección, reducción y caracterización de los datos contenidos en la base de datos bajo estudio y Construcción del Indicador bajo estudio. El indicador bajo estudio fue el tiempo de cambio de estado (por sus siglas en inglés TAT). Resultados Se construyó el nuevo predictor para TAT basado en técnicas de regresión múltiple. Las variables que más contribuyeron a la construcción del nuevo predictor fueron tiempo de respuesta del departamento de IC (CE), con un coeficiente 0.415 positivo, tiempo de respuesta de entrega de las piezas de repuesto (Stock), con un coeficiente de 0,734 positivo, nivel de prioridad del equipamiento (RL), con un coeficiente de 0,25 positivo, y tiempo de servicio de mantenimiento (ST), con un coeficiente de 0.06 positivo. La tecnica de regresión aplicada demostró una fuerte dependencia de las variables Stock CE, y PL en este orden. Las técnicas de conglomerados encontró las principales causas por las cuales el valor de TAT era demasiado alto. Conclusiones. El estudio demostró que es posible aplicar técnicas de minerías de datos para mejorar la eficiencia de las actividades que se desarrollan en los departamentos de Ingeniería de los hospitales.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Copyright of Revista de Salud Pública is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use.
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