Using an objective measurement model to determine the corrective maintenance demand in the field of hospital engineering

  • Francisco J. MoralEmail author
  • Francisco J. Rebollo
  • Luis Foz
  • Francisco Méndez
Original Article


In this work, the use of an objective method, the formulation of the Rasch measurement model, which synthesizes data from different susceptible elements for maintenance (SEM) and healthcare units (HU) into a uniform analytical framework, is considered to get representative measures of corrective maintenance demand in an hospital. Thus, information about 10 SEM and 33 HU were obtained from two hospital located in Badajoz (Spain) to be treated. A latent variable, denominated corrective maintenance demand, was defined. It is supposed, and later it is verified, that all SEM previously indicated have a marked influence on the latent variable. The adequate assignment of categorical values across SEM measures and the good fit of the data are checked as a previous phase to properly compute the Rasch measures. After applying the Rasch methodology, it was obtained that the mean corrective maintenance demand of HU is lower than expected, but significative differences between units are apparent. Those which care for high risk patients, as liver transplant, intensive care, and internal medicine, are the most influential units on corrective maintenance demand, getting moreover a ranking of all HU according to their corrective maintenance demand. Similarly, another ranking of all SEM is provided, being hospital furniture the item that exerts the highest influence on corrective maintenance demand. Moreover, the unexpected behaviors, called misfits, of some HU and SEM constitute a very useful information to better know the hospital requirements and correctly allocate the work hours in the maintenance management program. Consequently, the Rasch measurement model is a very useful tool for decision making related to corrective maintenance cost management and their correct attribution to each healthcare unit.


Rasch model Measurement Hospital Decision-making Objective method 



  1. Álvarez P (2005) Several noncategorical measures define air pollution construct. Rasch measurement in health science. JAM Press, Maple GroveGoogle Scholar
  2. Amendola J (2001) Gestión integral de mantenimiento de activos. Ed. Universidad Politécnica, ValenciaGoogle Scholar
  3. Bode RK, Wright BD (1999) Rasch measurement in higher education. In: Smart JC, Tierney WG (Eds) Higher education: handbook of theory and research, vol. XIV. Agathon Press, New York.Google Scholar
  4. Bond TG, Fox CM (2007) Applying the Rasch model: fundamental measurement in the human sciences, 2nd edn. Lawrence Erlbaum Associates Inc., MahwahGoogle Scholar
  5. Edwards A, Alcock L (2010) Using Rasch analysis to identify uncharacteristic responses to undergraduate assessments. Teach Math Appl 29:165–175Google Scholar
  6. EN 15341 (2007) Maintenance. Maintenance Key Performance Indicators.Google Scholar
  7. Guijarro F, López-Rodríguez F, Moral FJ, Mena A, Álvarez P (2012) Using an objective method for managing the implementation of quality certification in the industry. Comput Ind Eng 62:591–598CrossRefGoogle Scholar
  8. Gusmão CA (2001) Índices de desempenho da manutenção: um enfoque prático. Nova Manutenção y Qualidade 37(anno 8):7–11Google Scholar
  9. Hernández E, Navarrete E (2001) Sistema de cálculo de indicadores para el mantenimiento. Club de mantenimiento 6(Año 2):7–11Google Scholar
  10. Ishikawa K (1990) Introduction to quality control. Ed. 3A Corporation, TokyoGoogle Scholar
  11. Juran J (1967) The QC circle phenomenon industrial quality control. Society of Quality Control Engineers, New YorkGoogle Scholar
  12. Mari L, Wilson M (2014) An introduction to the Rasch measurement approach for metrologists. Measurement 51:315–327CrossRefGoogle Scholar
  13. Mondragón L (2003) La trampa de los indicadores. Énfasis Logística, Mexico, Edición No 37.Google Scholar
  14. Moral FJ, Álvarez P, Canito JL (2006) Mapping and hazard assessment of atmospheric pollution in a medium sized urban area using the Rasch model and geostatistics techniques. Atmos Environ 40:1408–1418CrossRefGoogle Scholar
  15. Moral FJ, Rebollo FJ, Valiente P, López F, Muñoz de la Peña A (2012) Modelling ambient ozone in an urban area using an objective model and geostatistical algorithms. Atmos Environ 63:86–93CrossRefGoogle Scholar
  16. Rasch G (1980) Probabilistic models for some intelligence and attainment tests, Revised and expanded edn. University of Chicago, ChicagoGoogle Scholar
  17. Ren W, Bradley KD, Lumpp JK (2008) Applying the Rasch model to evaluate an implementation of the Kentucy electronics educations education project. J Sci Educ Technol 17(6):618–625CrossRefGoogle Scholar
  18. Sekaran U (2000) Research methods for business: a skill building approach. John Wiley & Sons Inc, SingaporeGoogle Scholar
  19. Smith RM (1996) Polytomous mean-square statistics. Rasch measurement. Transactions 6:516–517Google Scholar
  20. Tavares LA (2001) Administración moderna de mantenimiento. Ed. Novo Polo, Santa Cruz de la SierraGoogle Scholar
  21. Tristán A (2002) Análisis de Rasch para todos. Ed. Ceneval, MéxicoGoogle Scholar
  22. Wireman F (2001) Desarrollo de indicadores de desempeño para administración de mantenimiento. Editorial Rojas Eberhard Editores, Bogotá, ColombiaGoogle Scholar
  23. Wright BD, Masters GN (1982) Rating scale analysis. MESA Press, ChicagoGoogle Scholar

Copyright information

© The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2019

Authors and Affiliations

  1. 1.Departamento de Expresión GráficaUniversidad de ExtremaduraBadajozSpain

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