On September 24, 2015, the FDA issued a Warning Letter to a manufacturer and distributor of medical devices for several violations. While the issues at hand may not seem severe, they do put the public at risk and reduce the confidence that the FDA has in the organization. Plus, they are inviting unwanted scrutiny in the future.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
While there are some egregious FDA findings, such as “a container housing gasoline was observed immediately next to orthopedic trays and other instrument trays,” most of the observances listed in the Warning Letter indicate the company has a lack of storage- and handling-related procedures that could affect the distribution or use of their medical products.
Similar to many other Warning Letters, it’s clear the FDA wants to see thorough procedures in place for all regulated products and activities. It’s also noted the company failed to “establish and maintain procedures for verifying or validating corrective and preventive actions (CAPA),” a topic that we continue to discuss due to its critical nature.
Most of the observances outlined can be corrected and prevented with proper documentation and IT systems, such as a quality management system (QMS). Alternatively, you can double-dip and leverage an existing solution, such as a drug safety and pharmacovigilance system, that’s already being used at your organization (read our ongoing blog series to learn how Oracle’s Argus Safety can be used to manage CAPAs).