On 17 March 2020, the President of the European Council, Charles Michel, and the President of the European Commission (hereinafter, Commission), Ursula von der Leyen, announced further European Union (EU) actions in response to the COVID-19 outbreak. Since the pandemic reached Europe, the EU has adopted a number of trade-related measures, including the issuance of guidelines for national border management, as well as export authorisation requirements. On 14 March 2020, the Commission adopted “Commission Implementing Regulation (EU) 2020/402 of 14 March 2020 making the exportation of certain products subject to the production of an export authorisation”, temporarily restricting exports of “personal protective equipment” to destinations outside of the EU. On 14 April 2020, the Commission announced that it would narrow down export authorisation requirements to protective masks only and extend the geographical and humanitarian exemptions. Governments around the world have been implementing trade-related measures in response to the COVID-19 pandemic, some trade restrictive, but a number of countries have also called for the elimination of export controls and restrictions on essential goods. As the greater implications of the COVID-19 pandemic on trade are still difficult to assess, the emergency measures taken by affected countries already require legal scrutiny. At the same time, it must be noted that, as noted above for the EU measures, measures around the world are subject to change dynamically in view of the evolution of the pandemic.
In this paper, we investigate the impact of product, company context and regulatory environment factors for their potential impact on medical device development (MDD). The presented work investigates the impact of these factors on the Food and Drug Administration's (FDA) decision time for submissions that request clearance, or approval to launch a medical device in the market. Our overall goal is to identify critical factors using historical data and rigorous techniques so that an expert system can be built to guide product developers to improve the efficiency of the MDD process, and thereby reduce associated costs. We employ a Bayesian network (BN) approach, a well-known machine learning method, to examine what the critical factors in the MDD context are. This analysis is performed using the data from 2400 FDA approved orthopedic devices that represent products from 474 different companies. Presented inferences are to be used as the backbone of an expert system specific to MDD.
This paper provides a deeper understanding on the Food and Drug Administration’s (FDA) role in relation to medical devices. Specific considerations are given to the data FDA reports to the public domain as part of the 510(k) clearances and PMA approvals. Scientific treatment of such considerations has been a void in the literature prior to the work presented here. Critical factors are defined at the product level, where an empirical investigation is performed to study the impact of various factors in FDA’s decision time. The critical factors identified include regulatory components, product characteristics and the historical reference. Significant regulatory components include the submission types and the different types of classifications (product codes, risk classification and regulation number). Significant product characteristics included the factors specific to hip devices (cemented, constrained) and generalized factors applicable to most medical devices (intended use, context of use, function and material). The importance of historical reference, as an indication of various types of experience, showed the significance of company’s experience with FDA, and FDA’s experience in terms of product codes and product characteristics (body part, function, material and et cetera).
Complexity metrics have been developed for multiple applications such as consumer products, software, trajectory selection and assembly systems. Although existing complexity metrics were developed to reduce product design and development costs, their lack of simplicity in formulation and robustness has limited their applicability. This paper proposes a standard methodology for comparing and evaluating these metrics and introduces dimensions of complexity that should be considered towards the goal of developing a generalizable product complexity measure. To this end, this paper introduces variables that integrate multiple facets of complexity into a single metric. A medical device case study is used to compare the efficiency and robustness of existing complexity measures. The medical device case study also serves as the motivation for the proposed complexity metric due to the complexity of the domain itself and the increasing importance of mitigating healthcare costs. Overall, product complexity metrics can aid medical device development by increasing the understanding about the design and its implications regarding development time and FDA approvals.
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