Maintenance Model for Minimizing Risk and Optimizing Cost-effectiveness of Medical Equipment in Palestine

Khalaf, Abdelbaset

doi:10.1097/00004669-200410000-00048

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…• Management and monitoring of training provided by manufacturers or technical staff including biomedical engineer or clinical engineer [14] The risk assessment was divided into four main areas: clinical function, failure avoidance probability, history of incidents and regulatory or manufacturer requirements. Devices would be evaluated on the aforementioned criteria and be assigned a score.…”

Section: Classification and Prioritization Of Medical Equipment For Maintenance Activitiesmentioning

confidence: 99%

A Modern Approach for Maintenance Prioritization of Medical Equipment

Corciovă¹,

Andriţoi²,

Luca³

2021

Operations Management - Emerging Trend in the Digital Era

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Maintenance is a crucial topic in the life cycle management of medical equipment. Evidence-based maintenance consists of continuous monitoring of equipment performance, starting from evidence—the current state from the point of view of fault history—and improving its efficiency through the necessary modifications. This process is very important for optimizing the use and allocation of the resources available by the clinical engineering departments. Maintenance of medical equipment consists of two basic activities: scheduled maintenance and corrective maintenance. The purpose of this chapter is to present document-based methods to evaluate every aspect of the medical equipment maintenance process and to provide a correct, objective and standardized approach that supports clinical engineering activities. Following the analysis, the results show that the combination of the use of the two methods provides an overview, in a periodic manner, of maintenance performance that indicates the use of the most appropriate procedures.

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Section: Classification and Prioritization Of Medical Equipment For Maintenance Activitiesmentioning

confidence: 99%

A Modern Approach for Maintenance Prioritization of Medical Equipment

Corciovă¹,

Andriţoi²,

Luca³

2021

Operations Management - Emerging Trend in the Digital Era

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“…Evidence in the literature points that mathematical modeling is much more flexible than empirical approaches, and medical maintenance would benefit from optimization modeling [25]. Khalaf et al [26,27] proposed a mathematical model that adopted a mixed integer based approach maintenance scheduling technique a software maintainability for maintenance predication purposes was formulated based on both experience in software architecture development and haemodialysis machine architecture. The key performance indicators for the software design included maintainability, re-usability, safety, real-timeliness and demonstrability [28].…”

Section: Optimization Of Maintenance Strategy For Critical Medical Eqmentioning

confidence: 99%

Review in Maintenance Strategies for Haemodialysis Machine in Healthcare Facilities

Mutia¹

2018

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Hemodialysis machines are critical medical equipment in healthcare facilities for renal replacement therapy in form of dialysis treatment on solving chronic kidney diseases in Sub Sahara Africa. It is a vital machine which acts as human kidney by incorporating electromechanical controlled extracorporeal blood paths that leverage pumps and semi permeable dialyzer membranes to filter the patient's blood. The biggest challenge to the biomedical engineers in most African hospitals is to maintain the manufacturer's safety and performance specification of the haemodialysis equipment. There is a need for effective maintenance strategy for haemodialysis medical equipment in order to maintain the manufacturer's set specification to meet clinical expectations and hence improve its reliability. The overall goal of the research paper is therefore to analyze the influence of different maintenance strategies and subsequently improve on the reliability of hemodialysis equipment in healthcare institutions in Kenya. The research will prioritize hemodialysis machine as critical medical equipment and use comprehensive secondary data to review and analyze the strategic maintenance applied in health institutions to optimize the best and cost effective strategic maintenance for the hemodialysis medical equipment. The ant colony optimization (ACO) algorithms may be less expert reliant and avoid uncertainty and ambiguity to determine the best strategic maintenance management to manage hemodialysis medical equipment in the hospitals. The results will provide an opportunity to technical engineers to develop a predictive and intelligent management system in the hospitals to minimize or remove the Mean Downtime (MDT) and Mean time to repair (MTTR) for a failed hemodialysis machines and improve the reliability of the hemodialysis machine.

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“…The last coronavirus disease 2019 (Covid-19) pandemic (2020) might be the best demonstration of this fact, as hundreds of lives were lost and thousands more became critically ill as a direct result of the unavailability of the equipment required for diagnosis and treatment (mainly testing, respiratory aid and resuscitation devices), whether due to lack or failures. In developing countries, the situation is far worse, as the inoperative devices rate reaches 50% and sometimes up to 75% of the supplied equipment in normal conditions (Khalaf, 2004), regardless of the heavy workload associated with such pandemics and crises. This can get even more critical if the device ceases working while in use.…”

Section: Introductionmentioning

confidence: 99%

An ontology-driven model for hospital equipment maintenance management: a case study

Titah,

Bouchaala

2024

JQME

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Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.

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Maintenance Model for Minimizing Risk and Optimizing Cost-effectiveness of Medical Equipment in Palestine

Cited by 6 publications

References 4 publications

A Modern Approach for Maintenance Prioritization of Medical Equipment

A Modern Approach for Maintenance Prioritization of Medical Equipment

Review in Maintenance Strategies for Haemodialysis Machine in Healthcare Facilities

An ontology-driven model for hospital equipment maintenance management: a case study

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