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How to Predict Medical Device Recalls using Publicly Available Data

A session by Mohammed (Bilash) Hossain and John Lorenc
Reed Tech and Reed Tech

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About this session

Data concerning medical device safety events and recalls is readily available, especially in the US market, accounting for about 39% of the global market share. With the medical device market expected to grow at a CAGR of *6.1% starting in 2021, understanding the trends in safety and quality is more important than ever. With the available history of adverse event reports, product design and performance defects captured and, in many cases, reported patient problems, a hypothetical question was posed: Can these data points be modeled to help understand if recall events are predictable in nature? To answer this and other questions, the Reed Tech team is leveraging data from the FDA and several other public sources to train a machine-learning algorithm to ‘predict’ recalls.

Learn more about the project, methodology and the latest update on the compelling results.

  • Predictive recall analytics methodology and overview of proprietary database
  • Which devices are at the highest risk for recall in the coming year across all industries
  • How to watch for leading indicators of a recall
  • How advance notice of a potential recall could help your business

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