An investigation of critical factors in medical device development through Bayesian networks

• Potential quantitative and qualitative success factors for new product development are identified.
• Using 2400 specific device data, significance of 20 factors is investigated.
• Investigation uses Smart Greedy+ algorithm for Bayesian network-based learning.
• Results point to the significance of company and FDA related factors.

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.