The Food and Drug Administration (FDA) wants to develop standardized data supersets. The intent is to improve data interoperability, analysis, and management across the agency. The agency plans to gather information and seek public input on using real-world data in its decision-making processes.
The FDA's current objective is to decrease the burden on industry and allow organizations to deliver data to a broad scope of regulatory agencies and international collaborators. This way FDA aims to standardize the data that comes to them and create a superset of data that would allow the clinical trial data to flow seamlessly into the regulatory agencies. The FDA is also hoping to launch new pilot projects with industry partners. Besides, it is set to commence a pilot exploring new ways to submit data around product labeling requirements.
Furthermore, the FDA is also considering leveraging artificial intelligence and machine learning to help comb through the vast troves of data the agency receives through clinical trials, manufacturers, adverse event reporting, and from international partners.
The FDA’s efforts to standardize data come amid a push across the public health community to improve data interoperability for a complex and diverse network of IT systems worldwide.
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