In the rapidly evolving landscape of pharmaceutical documentation, the drug labeling workflow is undergoing its most significant transformation in history. The shift from traditional, cumbersome methods of document creation and approval, to streamlined, AI-driven processes is paving the way for a future where drug labeling is more efficient, precise, and adaptable than ever before.
This blog post will dive into the five steps of the drug labeling workflow: Ingest, Author, Collaborate, Translate, and Publish. We’ll explore how artificial intelligence (AI) is enhancing each step, making the process faster, more accurate, and future-proof. By understanding these steps, you’ll be better prepared to optimize your drug labeling workflow and stay ahead in a competitive industry.
1. Ingest: Automating the Foundation
The first step in the drug labeling workflow is ingestion, where existing documents are captured and processed into a structured format. Traditionally, this step required manual effort, with teams painstakingly entering data and formatting it according to specific guidelines. However, AI-driven tools have transformed this process, enabling automated ingestion that recognizes and parses metadata, components, and files with unprecedented accuracy. These tools are capable of indexing key elements within the data, allowing for efficient retrieval and reference in the future. The indexing process ensures that each piece of information is easily searchable and categorized according to its unique attributes.
With AI, the ingestion process becomes a “set up once, use many” scenario. The system learns from each document it processes, improving its accuracy over time. This approach not only speeds up the ingestion process but also ensures that the content is consistently structured, making it easier to manage and reuse across different projects. Structured data enhances the ability to perform cross-referencing and linkage between documents, streamlining compliance checks and version control, while maintaining the integrity of the original dataset throughout its lifecycle.
2. Author: Structured Content for Maximum Reusability
Once the documents are ingested into structured components, the next step is authoring. In a traditional workflow, authoring often involves creating new content from scratch, copying and pasting from old documents, or modifying existing documents, leading to duplications and inconsistencies. By leveraging the previously ingested, indexed, and structured data, authors can generate new documents on the fly by pulling in components from any number of documents, allowing them to create multiple outputs almost instantaneously.
AI tools help authors generate content that fits within this framework, ensuring that every new document is built on a foundation of consistent, reusable components. In this structured content environment, every piece of information is treated as a reusable component that can be assembled dynamically. This will enable authors to generate new documents without needing to manually retype or copy content. This flexibility allows for rapid scaling of content production while enhancing the overall quality by maintaining reference to the original location of the data.
3. Collaborate: Moving Beyond Document-Level Collaboration
Collaboration is a critical aspect of the drug labeling workflow, involving multiple stakeholders, including authors, approvers, health authorities, and translators. Traditionally, collaboration took place at the document level, with each stakeholder reviewing and approving entire documents. This approach was time-consuming and often led to delays and miscommunication.
AI-driven collaboration tools are changing this by enabling collaboration at the component level. Instead of reviewing entire documents, stakeholders can now focus on specific components, making the process more efficient and reducing the risk of errors. This shift towards componentized collaboration is especially important in a global context, where different health authorities may have varying requirements.
With structured content AI, version control and version management are easier than ever. Automatic naming conventions according to your organization’s practices ensure consistency and reliability across content. System notifications are customizable to document actions, such as checking documents in and out, to ensure traceability and compliance with a full audit trail and e-signatures.
4. Translate: AI-Powered Autotranslation for Global Reach
In today’s globalized pharmaceutical industry, translation is a crucial step in the drug labeling workflow. However, manual translation processes are time-consuming and prone to errors, especially when dealing with large volumes of content.
AI-powered autotranslation is revolutionizing this process by using machine learning algorithms to translate content quickly and accurately. These systems use translation memory, meaning they only translate new components, while previously translated content is reused. As the system learns from each translation, the accuracy and quality of the translations improve over time, ensuring that your labeling documents are ready for submission to health authorities worldwide.
5. Publish: Ready for the Future with Agency-Neutral Schema
The final step in the drug labeling workflow is publishing. Traditionally, this step involved formatting documents according to the specific requirements of different health authorities, which could vary significantly. This often led to delays and additional work as documents had to be reformatted and resubmitted multiple times.
With AI-driven tools, publishing becomes a streamlined process. By using an “agency-neutral” schema, your documents are structured in a way that allows them to be easily adapted to different standards as they emerge. This ensures that your labeling documents are future-proof, ready to be submitted to any health authority without the need for extensive reformatting.
Conclusion: Preparing for the Future of Drug Labeling
The drug labeling workflow is evolving, driven by AI and new structured content standards in the pharmaceutical industry. Adopting these technologies and approaches ensures that your labeling outputs are compliant and always ready for future developments.
Understanding the power of AI in the drug labeling workflow can help you stay ahead of the curve. By preparing your workflow today, you can adapt to new standards more quickly than your competitors, positioning your organization for success in an ever-changing industry.
Wondering how structured content AI can fit into your labeling workflow? Contact Glemser today to get started.
