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.
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.
Atles LR (2008). A Practicum for Biomedical Technology & Management Issues. Kendall-Hunt Publishing: Dubuque, IA.
Bevilacqua M and Braglia M (2000). The analytic hierarchy process applied to maintenance strategy selection. Reliab Eng Syst Safe 70 (1): 71–83.
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.
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.
Dhillion BS (2000). Medical Device Reliability and Associated Areas. CRC Press: Boca Raton, FL.
Durkin J (1994). Expert Systems Design and Development. Macmillan: New York.
Fennigkoh L and Smith B (1989). Clinical equipment management. Jcaho Ptsm Ser 2: 5–14.
Fong SW and Choi SKY (2000). Final contractor selection using the analytical hierarchy process. Constr Mngt Econ 18 : 547–557.
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.
Ho W (2008). Integrated analytic hierarchy process and its applications—a literature review. Eur J Opl Res 186 : 211–228.
Hyman W (2003). The theory and practice of preventive maintenance. J Clin Eng 28 (1): 31–36.
Jardine AKS and Tsang AHC (2006). Maintenance, Replacement, and Reliability Theory and Applications. CRC Press: Boca Raton, FL.
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.
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.
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.
Mahdi IM, Riley MJ, Fereig SM and Alex AP (2002). A multi-criteria approach to contractor selection. Eng Constrarchit Mngt 9 (1): 29–37.
Meados M (2006). The FDA and product recalls. FDA Consum 40 (3): 30–35.
Mobley RK (2002). Introduction to Predictive Maintenance. 2nd edn, Butterworth-Heinemann: England.
Modarres M (2006). Risk Analysis in Engineering: Techniques, Tools, and Trends. CRC Press: Boca Raton, FL.
Moubray J (1997). Reliability-centered Maintenance II. 2nd edn, Industrial Press: New York.
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.
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.
Rice W (2007). Medical device risk based evaluation and maintenance using fault tree analysis. Biomed Instrum Techn 41 : 76–82.
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.
Ridgway M (2009). Optimizing our PM programs. Biomed Instrum Techn 43 : 244–254.
Rzevsky G (1995). Mechatronics: Designing Intelligent Machines. Butterworth-Heinemann: England.
Saaty TL (1980). The Analytic Hierarchy Process. McGraw Hill: New York.
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.
Saaty TL (1988). Mathematical Methods of Operations Research. Dower Publications: New York.
Saaty TL (1990). How to make a decision: The analytic hierarchy process. Eur J Opl Res 48: 9–26.
Saaty TL (2008). Decision making with the analytical hierarchy process. Int J Serv Sci 1 (1): 83–98.
Simpson GW and Cochran JK (1987). An analytic approach to prioritizing construction projects. Civil Eng Syst 4 : 185–190.
Stiefel RH (2009). Medical Equipment Management Manual. 7th edn, AAMI: Annapolis Junction, MD.
Swanson L (2001). Linking maintenance strategies to performance. Int J Prod Econ 70 : 237–244.
Taylor K (2005). A medical equipment replacement score system. J Clin Eng 30 (1): 26–30.
Triantaphyllou E (2000). Multi-criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers: The Netherlands.
Vaidya OS and Kumar S (2006). Analytic hierarchy process: An overview of applications. Eur J Opl Res 169 : 1–29.
Vellani KH (2006). Strategic Security Management: A Risk Assessment Guide for Decision Makers. Butterworth-Heinemann: England.
Wang B (2006). Fennigkoh and Smith model for inclusion criteria: 15-year retrospective. J Clin Eng 31 (1): 26–30.
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.
Wang B and Rice W (2003). JCAHO's equipment inclusion criteria revisited— Application of statistical sampling technique. J Clin Eng 28 (1): 37–48.
Wang B, Furst E, Cohen T, Keil OR, Ridgway M and Stiefel R (2006). Medical equipment management strategies. Biomed Instrum Techn 40 : 233–237.
Yoon KP and Hwang CL (1981). Multi Attribute Decision Making. Springer: New York.
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
Corresponding author
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).
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.
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).
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.
Thus, the minimum total score value is,
Rights 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/jors.2010.106