A MULTI-METRIC ANALYSIS TO QUANTIFY AND ASSESS THE DESIGN COMPLEXITY OF MEDICAL DEVICES

Open Access
Author:
Crespo-Varela, Josue R.
Graduate Program:
Industrial Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
None
Committee Members:
  • Gul Kremer, Thesis Advisor
Keywords:
  • Design
  • Medical Devices
  • Complexity
Abstract:
Design complexity (DC) is a decisive factor for a feasible and cost-effective product development. As product complexity increases, product development becomes more costly given the impact of complexity in the development time, manufacturing processes and development environment. The more complex the product, the more information will be needed and generated to develop it, and a longer development time is expected. The demands on manufacturing processes also increase with a greater number of manufacturing steps, components and interactions. The development environment is also impacted, in general, with a higher cost of patents. Greater implications of product complexity are observed in the development environment of highly regulated products. This is the case for medical devices that are regulated by the Food and Drug Administration (FDA) in the Unites States, and hence medical devices are chosen as the focus. The FDA regulation requirements have a strong influence in the overall landscape of medical device development, with control over the development process, manufacturing and surveillance of medical devices in the market. DC issues cannot be addressed without the proper measures to understand and quantify complexity. The literature has paid particular attention to the complexity of products, systems and systems of systems. Complexity metrics have been developed for multiple applications that include the development of products, software, trajectory selection, electrical components and assemblies. Although many of these complexity metrics were developed to reduce product complexity, existing metrics are not necessarily easy to implement or appropriate for any scenario. Therefore, this research takes a step back to perform a comprehensive analysis on the implementation of selected DC metrics for medical devices. Assessing selected complexity metrics provides a better appraisal of complexity to reduce the development time and cost. As part of this implementation, existing metrics are first filtered to down-select from a much larger set using the following as criteria: 1) ease of use, 2) data requirements, 3) development stages impacted, and 4) overall applicability to medical devices. Three metrics were selected as per this filtering to assess the complexity of medical devices for a set of FDA cleared hip devices that are currently in the market. The impact of product complexity on the FDA’s decision time of approval is also tested as assessed the three metrics chosen. This research’s findings show that current metrics are focused on different aspects of complexity, such as functions, components and their interactions. Supporting this, the statistical analysis of hip devices show few correlations between the complexity metrics found to be applicable for medical devices. Accordingly, a more standardized and generalized metrics should be developed to integrate all the complexity aspects in one measure. On the other hand, this research shows a relationship between product complexity and the development environment, where a combination of the complexity metrics explain FDA’s decision time for similar devices with the same risk classification. Overall, product complexity metrics can aid medical device development to increase an understanding about the design and its implications in the development time, cost and environment. Furthermore, decreasing design complexity should be considered to make design upgrades easier, and reduce the development time and cost.