Unlocking the Potential of Regulatory Data Requires Adoption of Artificial Intelligence and Machine Learning

Health authorities around the globe are moving toward structured data submissions. The progression of the Identification of Medicinal Products (IDMP) standard is a great example of key product information that can be used throughout the product lifecycle. IDMP has motivated regulatory teams to start looking at where their data is stored, how it can be accessed, what formats it is in, and who owns it.

The Challenge

Critical regulatory submission documents like the Summary of Product Characteristics (SmPC) and the Package Insert (PI) possess large amounts of important product data but it is stored within the unstructured format of standard documents. This makes it difficult to access and even more difficult to use broadly, across the organization.

Adding complexity, product lifecycle data is dynamic. It changes regularly as a product moves through development and commercialization. Submission documents are updated with new data and new data elements. This data could be used to inform processes and support emerging data submission standards like IDMP if the data can be extracted and made more easily accessible.

Manually identifying these critical data elements within R&D documents is extremely time consuming. It also requires a knowledgeable resource who can review the content and discern where the data is located while understanding the context in which it is used. This situation creates a circular challenge. A knowledgeable resource is needed to accomplish this task but any knowledgeable resource on the team is going to be working on higher priority, and frankly, more compelling tasks.

This type of content review is tedious, time consuming, and error prone. When an experienced resource is employed for this task, it is also expensive.

A New Approach

This scenario is begging for a technology solution that frees knowledge workers to do more valuable work while also creating the additional value of making Regulatory data more readily available and accessible across the organization.

Emerging technologies such as artificial intelligence (AI) and machine learning (ML) can be applied to this business case to process unstructured content and identify key data elements. Technology offers speed, greater capacity, and potentially better quality of data. But AI alone is not the answer. Artificial intelligence that has Regulatory domain knowledge built into the technology is a much more compelling and viable solution. Translating Regulatory expertise into rules and automated processes substantially increases the data quality. It is a complex effort that yields a significant pay off.

AI technologies that are trained on Regulatory content and informed by Regulatory expertise deliver the highest quality data. When data is accurate, reliable, and accessible, it can be used in more meaningful ways. Broader views of products, therapeutic areas, and portfolios can support decision-making. The newly available information can begin to inform operations across R&D and commercialization practices.

Making Regulatory data more accessible will transform the way Life Sciences teams do business. A comprehensive approach that infuses domain expertise into emerging technologies offers new opportunities for efficiency and compliance. These opportunities are necessary for Life Sciences companies to utilize to truly step into the future of regulatory affairs.

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