Skip to main content
Log in

Prioritization of medical equipment for maintenance decisions

  • Theoretical Paper
  • Published:
Journal of the Operational Research Society

Abstract

Clinical engineering departments in hospitals are responsible for establishing and regulating a Medical Equipment Management Program to ensure that medical devices are safe and reliable. In order to mitigate functional failures, significant and critical devices should be identified and prioritized. In this paper, we present a multi-criteria decision-making model to prioritize medical devices according to their criticality. Devices with lower criticality scores can be assigned a lower priority in a maintenance management program. However, those with higher scores should be investigated in detail to find the reasons for their higher criticality, and appropriate actions, such as ‘preventive maintenance’, ‘user training’, ‘redesigning the device’, etc, should be taken. In this paper,we also describe how individual score values obtained for each criterion can be used to establish guidelines for appropriate maintenance strategies for different classes of devices. The information of 26 different medical devices is extracted from a hospital's maintenance management system to illustrate an application of the proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  • Al Harbi KM (2001). Application of the AHP in project management. Int J Proj Mngt 19 (4): 19–27.

    Article  Google Scholar 

  • Atles LR (2008). A Practicum for Biomedical Technology & Management Issues. Kendall-Hunt Publishing: Dubuque, IA.

    Google Scholar 

  • Bevilacqua M and Braglia M (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliab Eng Syst Safe 70 (1): 71–83.

    Article  Google Scholar 

  • Dekker R, Kleijn MJ and De Rooij PJ (1998). A spare parts stocking policy based on equipment criticality. Intl J Prod Econ 56/57: 69–77.

    Article  Google Scholar 

  • DeRosier J, Stalhandske E, Bagian JP and Nudell T (2002). Using health care failure mode and effect analysis: The VA national center for patient safety's prospective risk analysis system. Joint Comm J Qual Im 28 : 248–267.

    Google Scholar 

  • Dhillion BS (2000). Medical Device Reliability and Associated Areas. CRC Press: Boca Raton, FL.

    Book  Google Scholar 

  • Durkin J (1994). Expert Systems Design and Development. Macmillan: New York.

    Google Scholar 

  • Fennigkoh L and Smith B (1989). Clinical equipment management. Jcaho Ptsm Ser 2: 5–14.

    Google Scholar 

  • Fong SW and Choi SKY (2000). Final contractor selection using the analytical hierarchy process. Constr Mngt Econ 18 : 547–557.

    Article  Google Scholar 

  • Gentles B et al (2010). http://CESOData.ca, accessed 27 April 2010.

  • Health Canada (1998). Guidance for the Risk-Based Classification System. Therapeutic Products Directorate: Ottawa, http://web.invima.gov.co/portal/documents/BVSalud/IVC/anexo5rbcsmdhc.pdf, accessed 1 July 2010.

  • Herath G and Prato T (eds) (2006). Using Multi-criteria Decision Analysis in Natural Resource Management. Ashgate Publishing Limited: England.

    Google Scholar 

  • Ho W (2008). Integrated analytic hierarchy process and its applications—a literature review. Eur J Opl Res 186 : 211–228.

    Article  Google Scholar 

  • Hyman W (2003). The theory and practice of preventive maintenance. J Clin Eng 28 (1): 31–36.

    Article  Google Scholar 

  • Jardine AKS and Tsang AHC (2006). Maintenance, Replacement, and Reliability Theory and Applications. CRC Press: Boca Raton, FL.

    Google Scholar 

  • Joint Commission on Accreditation of Healthcare Organizations (JACAHO) (2004). 2004 Hospital Accreditation Standards. Joint Commission on Accreditation: Oakbrook Terrace, IL.

  • Joint Commission on Accreditation of Healthcare Organizations (JACAHO) (2005). Failure Mode and Effects Analysis in Health Care: Proactive Risk Reduction. 2nd edn, Joint Commission on Accreditation: Oakbrook Terrace, IL.

  • Kwong CK and Bai H (2002). A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. J Intell Manuf 13 : 367–377.

    Article  Google Scholar 

  • Lalib AW, Williams GB and O'Conner RF (1998). An intelligent maintenance model (system): An application of analytic hierarchy process and a fuzzy logic rule-based controller. J Opl Res Soc 49: 745–757.

    Article  Google Scholar 

  • Leung LC and Cao D (2001). On the efficacy of modeling multi-attribute decision problems using AHP and Sinarchy. Eur J Opl Res 132: 39–49.

    Article  Google Scholar 

  • Mahdi IM, Riley MJ, Fereig SM and Alex AP (2002). A multi-criteria approach to contractor selection. Eng Constrarchit Mngt 9 (1): 29–37.

    Google Scholar 

  • Meados M (2006). The FDA and product recalls. FDA Consum 40 (3): 30–35.

    Google Scholar 

  • Mobley RK (2002). Introduction to Predictive Maintenance. 2nd edn, Butterworth-Heinemann: England.

    Google Scholar 

  • Modarres M (2006). Risk Analysis in Engineering: Techniques, Tools, and Trends. CRC Press: Boca Raton, FL.

    Google Scholar 

  • Moubray J (1997). Reliability-centered Maintenance II. 2nd edn, Industrial Press: New York.

    Google Scholar 

  • Partovi FY, Burton J and Banerjee A (1989). Application of analytic hierarchy process in operations management. Int J Opns Prod Mngt 10 (3): 5–19.

    Article  Google Scholar 

  • Ramadhan RH, Wahhab HIA and Duffuaa SO (1999). The use of an analytical hierarchy process in pavement maintenance priority ranking. J Qual Maint Eng 5 (1): 25–39.

    Article  Google Scholar 

  • Rice W (2007). Medical device risk based evaluation and maintenance using fault tree analysis. Biomed Instrum Techn 41 : 76–82.

    Article  Google Scholar 

  • Ridgway M (2001). Classifying medical devices according to their maintenance sensitivity: A practical, risk-based approach to PM program management. Biomed Instrum Techn 35 : 167–176.

    Google Scholar 

  • Ridgway M (2009). Optimizing our PM programs. Biomed Instrum Techn 43 : 244–254.

    Article  Google Scholar 

  • Rzevsky G (1995). Mechatronics: Designing Intelligent Machines. Butterworth-Heinemann: England.

    Google Scholar 

  • Saaty TL (1980). The Analytic Hierarchy Process. McGraw Hill: New York.

    Google Scholar 

  • Saaty TL (1986). Absolute and relative measurement with the AHP. The most livable cities in the United States. Socio Econ Plan Sci 20 : 327–331.

    Article  Google Scholar 

  • Saaty TL (1988). Mathematical Methods of Operations Research. Dower Publications: New York.

    Google Scholar 

  • Saaty TL (1990). How to make a decision: The analytic hierarchy process. Eur J Opl Res 48: 9–26.

    Article  Google Scholar 

  • Saaty TL (2008). Decision making with the analytical hierarchy process. Int J Serv Sci 1 (1): 83–98.

    Google Scholar 

  • Simpson GW and Cochran JK (1987). An analytic approach to prioritizing construction projects. Civil Eng Syst 4 : 185–190.

    Article  Google Scholar 

  • Stiefel RH (2009). Medical Equipment Management Manual. 7th edn, AAMI: Annapolis Junction, MD.

    Google Scholar 

  • Swanson L (2001). Linking maintenance strategies to performance. Int J Prod Econ 70 : 237–244.

    Article  Google Scholar 

  • Taylor K (2005). A medical equipment replacement score system. J Clin Eng 30 (1): 26–30.

    Article  Google Scholar 

  • Triantaphyllou E (2000). Multi-criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers: The Netherlands.

    Book  Google Scholar 

  • Vaidya OS and Kumar S (2006). Analytic hierarchy process: An overview of applications. Eur J Opl Res 169 : 1–29.

    Article  Google Scholar 

  • Vellani KH (2006). Strategic Security Management: A Risk Assessment Guide for Decision Makers. Butterworth-Heinemann: England.

    Google Scholar 

  • Wang B (2006). Fennigkoh and Smith model for inclusion criteria: 15-year retrospective. J Clin Eng 31 (1): 26–30.

    Article  Google Scholar 

  • Wang B and Levenson A (2000). Equipment inclusion criteria— a new interpretation of JCAHO's medical equipment management standard. J Clin Eng 25 (1): 26–35.

    Article  Google Scholar 

  • Wang B and Rice W (2003). JCAHO's equipment inclusion criteria revisited— Application of statistical sampling technique. J Clin Eng 28 (1): 37–48.

    Article  Google Scholar 

  • Wang B, Furst E, Cohen T, Keil OR, Ridgway M and Stiefel R (2006). Medical equipment management strategies. Biomed Instrum Techn 40 : 233–237.

    Article  Google Scholar 

  • Yoon KP and Hwang CL (1981). Multi Attribute Decision Making. Springer: New York.

    Google Scholar 

Download references

Acknowledgements

We acknowledge the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Ontario Centre of Excellence (OCE), and the C-MORE Consortium members for their financial support. We are thankful to the referees whose constructive criticism and comments have helped us to improve the presentation of the paper and to augment our literature review with new references. Specifically, the remarks concerning the demonstration of the proposed model were very helpful.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S Taghipour.

Appendices

Appendix A

Qualitative grades and intensities for criteria/sub-criteria

We explain how the intensities for grades of criterion ‘Function’ can be obtained; the intensities of other criteria's grades are obtained using the same method.

Step 1: Pairwise comparison matrix of the grades is constructed using expert opinion (α ij for i=1, …, 5, j=1, …, 5). (Table A1).

Table a1 Pairwise comparison matrix for the grades of criterion ‘Function’

Step 2: The weight of each grade can be obtained as follows:

Step 3: The intensity of each grade can be obtained as follows:

See Table A2.

Table a2 Calculating intensities for the grades of criterion ‘Function’

The same method is employed for calculating the intensities of other criteria (Tables A3, A4, A5, A6, A7, A8, A9, A10, A11, A12 and A13).

Table a3 Function grades and intensities
Table a4 Utilization grades and intensities
Table a5 Availability of alternatives grades and intensities
Table a6 Age grades and intensities
Table a7 Failure frequency grades and intensities
Table a8 Failure detectability grades and intensities
Table a9 Downtime grades and intensities
Table a10 Cost of repair grades and intensities
Table a11 Safety and environment grades and intensities
Table a12 Recalls and hazards grades and intensities
Table a13 Maintenance requirements grades and intensities

Appendix B

Calculating the lower bound for the total score value

Table B1 shows how a score value of 0.1050 is obtained when a device gets the lowest intensity value with respect to all assessment criteria.

Table b1 Calculating the minimum total score value

Thus, the minimum total score value is,

Rights and permissions

Reprints and permissions

About this article

Cite this article

Taghipour, S., Banjevic, D. & Jardine, A. Prioritization of medical equipment for maintenance decisions. J Oper Res Soc 62, 1666–1687 (2011). https://doi.org/10.1057/jors.2010.106

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2010.106

Keywords

Navigation