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The growing number of emerging medical technologies and sophistication of modern medical devices (MDs) that improve both survival and quality of life indexes are often challenged by alarming cases of vigilance data cover-up and lack of sufficient pre-and post-authorization controls. Combining Quality with Risk Management processes and implementing them as early as possible in the design of MDs has proven to be an effective strategy to minimize residual risk. This article aims to discuss how the design of MDs interacts with their safety profile and how this dipole of intended performance and safety may be supported by Human Factors Engineering (HFE) throughout the Total Product Life-Cycle (TPLC) of an MD in order to capitalize on medical technologies without exposing users and patients to unnecessary risks.

Companion diagnostics (CDx) hail promise of improving the drug development process and precision medicine. However, there are various challenges involved in the clinical development and regulation of CDx, which are considered high-risk in vitro diagnostic medical devices given the role they play in therapeutic decision-making and the complications they may introduce with respect to their sensitivity and specificity. The European Union (E.U.) is currently in the process of bringing into effect in vitro Diagnostic Medical Devices Regulation (IVDR). The new Regulation is introducing a wide range of stringent requirements for scientific validity, analytical and clinical performance, as well as on post-market surveillance activities throughout the lifetime of in vitro diagnostics (IVD). Compliance with General Safety and Performance Requirements (GSPRs) adopts a risk-based approach, which is also the case for the new classification system. This changing regulatory framework has an impact on all stakeholders involved in the IVD Industry, including Authorized Representatives, Distributors, Importers, Notified Bodies, and Reference Laboratories and is expected to have a significant effect on the development of new CDx.

The need for sufficient clinical evidence and the collection of real-world evidence (RWE) is at the forefront of medical device and drug regulations, however, the collection of clinical data can be a time consuming and costly process. The advancement of Digital Health Technologies (DHTs) is transforming the way health data can be collected, analysed, and shared, presenting an opportunity for the implementation of DHTs in clinical research to aid with obtaining clinical evidence, particularly RWE. DHTs can provide a more efficient and timely way of collecting numerous types of clinical data (e.g., physiological, and behavioural data) and can be beneficial with regards to participant recruitment, data management and cost reduction. Recent guidelines and regulations on the use of RWE within regulatory decision-making processes opens the door for the wider implementation of DHTs. However, challenges and concerns remain regarding the use of DHT (such as data security and privacy). Nevertheless, the implementation of DHT in clinical research presents a promising opportunity for providing meaningful and patient-centred data to aid with regulatory decisions.

The use of real-world evidence (RWE) to support international regulatory decisionmaking is reflected in the growing number of regulatory frameworks and guidelines published by Competent Authorities and international initiatives that accept real-world data (RWD) sources. RWD can be obtained from a range of sources, including electronic health/medical records, pharmacy and insurance claims, patient-reported outcomes, product and disease registries, biobanks, and observational studies. However, the availability of RWD sources depends on the processes/systems implemented by regional healthcare systems, which are limited by the potential of inconsistent data collection, heterogeneity of clinical practices, and an overall lack of standardization. As the analysis of RWD/RWE primarily evaluates association rather than causation, it is still often viewed as a supplement to, rather than a replacement of, data that derives from controlled environments, such as Randomized Controlled Trials (RCT). Despite this, RWE may still be used to support the assessment of safety and effectiveness in regulatory submissions and can facilitate regulatory decisions (including reimbursement) by providing longterm data on safety and performance that could not otherwise be collected during the limited duration of a RCT. However, available RWE frameworks reveal serious challenges to the use of RWE for the support of the assessment of safety and effectiveness, due to biases in data collection, lack of randomization, quality of data collection, and generalizability of results and endpoints. Patient privacy and the need to ensure confidentiality also hinders regulatory stakeholders from establishing and implementing concrete regulations. This is because the collection and management of RWD must be used in accordance with national, and often conflicting, laws on data protection and information governance. This article summarizes all currently available RWE frameworks and discusses potential solutions for future harmonization and cross-stakeholder collaborations. Such harmonization and collaboration will boost the integration of RWE, not only in the post-approval stages of a medicine's lifecycle but also in the development and lifelong post-market surveillance of medical devices (MDs).

Preference testing is a valuable source of information that can be provided by both healthcare professionals (HCPs) and patients (users). It can be used to improve the design and development of medical devices by feeding into device usability and, ultimately, risk management. Furthermore, it can aid with selecting the most appropriate clinical endpoints to be used in the clinical evaluation of a device and increase patient engagement by incorporating patient-relevant outcomes. Preference testing is widely conducted in the food industry but is not widespread in the medical field due to limited guidelines and a lack of regulatory framework. As such, manufacturers may be unaware of the benefits of preference testing and fail to take full advantage of it, or conversely, may use inappropriate methodology and/or analyses and consequently fail to collect meaningful data. In this position paper, we aim to highlight the benefits and uses of preference testing, along with potential methods that could be used for preference testing of medical devices. A key step towards the wider implementation of preference testing in medical devices is for the publication of international standards and guidelines for the collection, assessment, and implementation of preference data into the life cycle of a medical device.