The medical device development process has become increasingly complex in recent years. The advent of new technology concepts, stricter regulatory requirements, and the ever increasing importance of reimbursement decisions for successful device commercialization require careful planning and strategy-setting, coordinated decisions, and consistent, rigorous business processes. The design and implementation of such processes, often captured in development models and accompanying standard operating procedures, have become a key determinant of the success of device commercialization. While various models may exist in the device industry, no comprehensive development model has been published. This paper reviews existing model representations and presents a new comprehensive development model that captures all aspects of device development and commercialization from early-concept selection to postmarket surveillance. This model was constructed based on best-practice analysis and in-depth interviews with more than 80 seasoned experts actively involved in the development, commercialization, and regulation of medical devices. The stage-gate process includes the following five phases: (1) initiation - opportunity and risk analysis, (2) formulation - concept and feasibility, (3) design and development - verification and validation, (4) final validation - product launch preparation, and (5) product launch and postlaunch assessment. The study results suggest that stage-gate processes are the predominant development model used in the medical device industry and that regulatory requirements such as the food and drug adminstration (FDA’s) Quality Systems Regulation play a substantive role in shaping activities and decisions in the process. The results also underline the significant differences between medical device innovation and drug discovery and development, and underscore current challenges associated with the successful development of the increasing number of combination products.
Organizational errors are often at the root of failures of critical engineering systems. Yet, when searching for risk management strategies, engineers tend to focus on technical solutions, in part because of the way risks and failures are analyzed. Probabilistic risk analysis allows assessment of the safety of a complex system by relating its failure probability to the performance of its components and operators. In this article, some organizational aspects are introduced to this analysis in an effort to describe the link between the probability of component failures and relevant features of the organizaton. Probabilities are used to analyze occurrences of organizational errors and their effects on system safety. Coarse estimates of the benefits of certain organizational improvements can then be derived. For jacket-type offshore platforms, improving the design review can provide substantial reliability gains, and the corresponding expense is about two orders of magnitude below the cost of achieving the same result by adding steel to structures.
The model presented here provides early-stage decision-support to industry, but also benefits regulators and payers in their later assessment of new devices and associated procedures.
Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system-based for high-consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward-looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high-consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.
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