Predictive Maintenance Application in Healthcare

Shafiya Sabah, Mostafa Moussa, Abdulrahim Shamayleh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Effective maintenance management of medical technology influences healthcare service quality delivery and the profitability of healthcare facilities. The objective of this study is to review recent research focused on medical equipment predictive maintenance. Predictive maintenance is a term that describes the use of sensors and forecasting techniques to anticipate when a device or machine would fail, so that preemptive measures can be undertaken to lessen the effects of an impending failure and subsequently improve service quality. In healthcare, this mostly pertains to medical devices and can be an invaluable tool that would save medical institutions money and time, and more importantly, mitigate adverse effects to patients that require these devices. Predictive maintenance is a means of providing some control in an otherwise chaotic and arbitrary industry; doctors, nurses, and administrators often do not see the value in repairing or replacing a device before it fails, especially if it were not crucial in their day-to-day or emergency operation. In this paper, we provide a comprehensive review of work that pertains to predictive maintenance in healthcare published between 2014 and 2019. The works reviewed were published in scientific journals and conferences, and for each one, we were interested in (1) what predictive maintenance techniques were used, such as Internet of Things (IoT) or deep learning (DL), (2) what devices/equipment were used to obtain results and (3) whether or not they were medical devices/healthcare-related, if not, how could this application apply to healthcare and (4) whether or not predictive maintenance is proven to be more effective than simply waiting for the lifespan of the device to come to its end. The reviewed papers all showed great potential in the techniques used for predictive maintenance and showed promising results for a variety of medical devices. The significance of this work lies in helping healthcare professionals realize the potential benefit that predictive maintenance can bring to the table, and how it fits with the cost-sensitive nature of the industry.

Original languageBritish English
Title of host publication68th Annual Reliability and Maintainability Symposium, RAMS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665424325
DOIs
StatePublished - 2022
Event68th Annual Reliability and Maintainability Symposium, RAMS 2022 - Tucson, United States
Duration: 24 Jan 202227 Jan 2022

Publication series

NameProceedings - Annual Reliability and Maintainability Symposium
Volume2022-January
ISSN (Print)0149-144X

Conference

Conference68th Annual Reliability and Maintainability Symposium, RAMS 2022
Country/TerritoryUnited States
CityTucson
Period24/01/2227/01/22

Keywords

  • Big data
  • Data mining
  • Deep learning
  • Healthcare
  • IoT
  • Predictive maintenance
  • Remaining useful life

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