Medical device risk management

Singh, Karnika; Selvam, Praveen

doi:10.1016/b978-0-12-820960-8.00005-8

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…The success of the Primary Functions of an autoinjector can be ensured by establishing a comprehensive risk management framework (Table 1) for the autoinjector's entire lifecycle. It is vital to use the conventional risk management tools such as Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to ensure the robustness of the design, and Process Failure Mode and Effects Analysis (PFMEA) to overcome the manufacturing challenges (44)(45)(46)(47). The conventional autoinjector risk management tools like FMEA, FTA, and PFMEA can serve as valuable mechanism for addressing both the identified device problems (Figure 3) and potential problems that may arise as a result of cybersecurity breaches, software failure, poor design, manufacturing challenges, insufficient user training, and inadequate IFU for AI-powered autoinjectors.…”

Section: Risk Associated With Autoinjectors and Ai Powered Autoinjectorsmentioning

confidence: 99%

Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors

Machal

2024

Front. Med. Technol.

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ObjectivesThis research aims to present and assess the Primary Functions of autoinjectors introduced in ISO 11608-1:2022. Investigate the risks in current autoinjector technology, identify and assess risks and benefits associated with Artificial Intelligence (AI) powered autoinjectors, and propose a framework for mitigating these risks. ISO 11608-1:2022 is a standard that specifies requirements and test methods for needle-based injection systems intended to deliver drugs, focusing on design and function to ensure patient safety and product effectiveness. ‘KZH’ is an FDA product code used to classify autoinjectors, for regulatory purposes, ensuring they meet defined safety and efficacy standards before being marketed.MethodA comprehensive analysis of autoinjectors problems is conducted using data from the United States Food and Drug Administration (FDA) database. This database records medical device reporting events, including those related to autoinjectors, reported by various sources. The analysis focuses on events associated with the product code KZH, covering data from January 1, 2008, to September 30, 2023. This research employs statistical frequency analysis and incorporates pertinent the FDA, United Kingdom, European Commission regulations, and ISO standards.Results500 medical device reporting events are assessed for autoinjectors under the KZH code. Ultimately, 188 of these events are confirmed to be associated with autoinjectors, all 500 medical devices were seen to lack AI capabilities. An analysis of these events for traditional mechanical autoinjectors revealed a predominant occurrence of malfunctions (72%) and injuries (26%) among event types. Device problems, such as breakage, defects, jams, and others, accounted for 45% of incidents, while 10% are attributed to patient problems, particularly missed and underdoses.ConclusionTraditional autoinjectors are designed to assist patients in medication administration, underscoring the need for quality control, reliability, and design enhancements. AI autoinjectors, sharing this goal, bring additional cybersecurity and software risks, requiring a comprehensive risk management framework that includes standards, tools, training, and ongoing monitoring. The integration of AI promises to improve functionality, enable real-time monitoring, and facilitate remote clinical trials, timely interventions, and tailored medical treatments.

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Section: Risk Associated With Autoinjectors and Ai Powered Autoinjectorsmentioning

confidence: 99%

Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors

Machal

2024

Front. Med. Technol.

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“…That both applies to risks related to efficacy, safety, quality as well as regulatory requirements. The application of risk management to medical devices is essentially governed by the international standard ISO 14971 recently revised in 2019 8 . In this standard, risk assessment mainly relies on the application of the failure modes, effects and criticality analysis (FMECA) or equivalent methods detailed in ICH Q9 9 .…”

Section: Introductionmentioning

confidence: 99%

iQbD: A Technological Readiness Level-Indexed Quality-by-Design Paradigm for Medical Device Engineering

Bastogne

2022

Journal of Medical Devices

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Risk assessment is a critical step in the roadmap of medical device development. Failure modes, effects and criticality analysis is a common approach based on declarative prior information that proved beneficial in the risk assessment of well established processes. But at early steps of development when innovative materials or technologies are embedded, the lack of experience on those innovations introduces too much subjectivity in FMECA for a robust risk assessment. Since mid-2000, the Quality-by-Design guideline has been proposed within the pharmaceutical industry as a proactive engineering approach of drug development. This paradigm enables a data-driven risk assessment throughout the development workflow, which completes risk assessment provided by FMECA. Nevertheless, its implementation guide is unclear and not flexible enough to be efficiently applied to the development of medical devices. To address this issue, a new QbD paradigm indexed on the technological readiness level of the innovative product is proposed. It covers the development of medical devices throughout the whole preclinical phase and is composed of at least nine learning cycles. The first part of this medical device QbD layout, composed of three consecutive risk assessment cycles, is evaluated through a real study case with the objective to demonstrate the proof of concept of a photobleaching controller in photodynamic therapy. Beyond this experimental result, this application has confirmed practical ability of the iQbD approach to complete FMECA and to provide an alternative solution to risk assessment when prior knowledge on the technological innovation is not available.

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“…However, as the number of patients suffering from old medical device failures increases, managing the life cycles, the period during which medical devices can be used, is required. The safety of patients can be secured by controlling the risks associated with the period of use of the medical device, and reducing the side effects ( 8 , 9 ).…”

Section: Introductionmentioning

confidence: 99%

How to calculate the life cycle of high-risk medical devices for patient safety

Seo¹,

Park

Lee

2022

Front. Public Health

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In this study, we analyzed Korean and foreign systems, focusing on high-risk medical devices that urgently need to be managed, and we present an life cycle calculation method for determining replacement time. A literature review was conducted to confirm the regulations of the medical device management system and life cycle by country, and a case analysis was performed to verify the replacement evaluation criteria of actual medical institutions. In addition, durability data from the Public Procurement Service, American Hospital Association, and Samsung Medical Center were used to calculate the life cycle of high-risk medical devices. The analysis showed that in the case of Korean and foreign medical device regulatory agencies, there were no specific life cycle regulations for high-risk medical devices. In addition, the important items in the medical device replacement evaluation were found to be the year of introduction, repair cost, component discontinuation, and several failures. On calculating the life cycle of high-risk medical devices revealed that the replacement time is 13 years for anesthesia machines, 14 years for defibrillators, 16 years for heart-lung machines, and 13 years for ventilators. To introduce a uniform medical device replacement standard and life cycle calculation method, the government will need to reorganize the medical device replacement laws and systems. In addition, in the case of medical institutions, it is necessary to secure patient safety by using expert groups to prepare specific life cycle standards that consider the characteristics of medical devices.

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Medical device risk management

Cited by 6 publications

References 1 publication

Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors

Risks and benefits associated with the primary functions of artificial intelligence powered autoinjectors

iQbD: A Technological Readiness Level-Indexed Quality-by-Design Paradigm for Medical Device Engineering

How to calculate the life cycle of high-risk medical devices for patient safety

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