What Is Content Automation and Why it is Important?
Content automation is the practice of using systems to perform repetitive tasks to create content; such as formatting, data retrieval, and document assembly. Content automation in the pharmaceutical industry is often implemented for the following reasons:
- Reduce the time it takes to develop and deliver content
- Mitigate risk by eliminating human error from tasks
Traditionally, pharma content automation was limited to what could be done with Microsoft Word. There are various plugins, macros, and other solutions that automate some level of content creation. Because these automations are limited by what word processing software can do, pharma has not been able to automate as much of its content creation as other industries.
More recently, pharma companies are looking toward structured content authoring (SCA) as a way to employ automation on a much larger scale. These companies are automating content creation by implementing content reuse and conditional processing, using smart templates, developing common metadata, sharing variable definitions, and other content structures. They are fetching data from source rather than relying on humans to copy/paste. Generative AI (GenAI) is already proving valuable in creating deriviative documents such as summaries and clinical study reports.
Structuring pharma content is a crucial step on the journey toward enabling digital submissions and implementing emerging technologies such as LLMs, GenAI, and other types of artificial intelligence.
What Are the Benefits of Content Automation?
Content automation provides several benefits of particular interest to pharma organizations. Each one of these benefits helps pharma companies reduce the time it takes to produce content throughout the drug development lifecycle and beyond.
- Reduce risk by eliminating manual operations such as copy/paste and table formatting
- Save time by automating content creation through content reuse, dynamic content assembly, and data integration
- Enable faster responses to new and changing regulatory requirements by speeding up the process of finding, updating, and delivering content
- Ensure consistency across a large volume of documents by verifying content against defined content standards
- Streamline translation process and improve quality of translations by reducing the amount of unnecessary variations across a large body of content
- Improve accuracy by automating document creation through use of authoring templates and output formats
One of the main advantages of content automation for pharma is that it reduces the amount of time authors, reviewers, and subject-matter experts (SMEs) spend on tasks not related to content creation.
Content reuse enables authors and SMEs to focus on creating new content rather than spending time finding content to copy, paste, and reformat. Automated formatting frees up a significant portion of time from authors, editors, and publishers. Integrating data sources with structured content authoring tools enables data to be retrieved automatically rather than copied and pasted, formatted, and reviewed.
Pharma organizations will see these content automation advantages when developing content at scale. It takes some work to design, develop, and implement the various types of content automation. The initiative involves a cross-functional team that includes people from content, technical, and product teams.
Disadvantages of Content Automation
When done well, we don’t see a lot of disadvantages to content automation.
However, a poorly designed (or non-existent) content automation strategy can result in one or more of the following drawbacks:
- It takes longer to maintain the automation than it did to develop content manually
- Too many systems in place to be cost-effective
- Scope is too narrow to show value or too broad to implement effectively
Some of the pitfalls that pharma organizations fall into on the journey to content automation include:
- Automation is applied to legacy content without first structuring or curating the content
- Attempt to automate on too large of a scope all at once
- Tendency to re-create legacy processes which then reduces the automation capabilities of the new technology
- Tendency to automate legacy processes instead of adopting structured content authoring
- Tradition of requesting customizations to new tools rather than adopt new ways of working
- Authors are not trained in structured content authoring or to write with reuse in mind, so the content does not fit together well when automatically assembled or reused
Content automation cannot solve all content-related challenges on its own. You still need skilled writers to produce excellent content. You still need subject-matter experts to create, review, and sign off on content.
The promise of content automation is to take the repetitive, high-risk tasks out of the content development workflow so that all stakeholders can focus on the tasks that require human expertise. Content automation increases accuracy of content and data while decreasing the amount of time humans have to spend searching for, creating, revising, or reviewing content.
Examples of Automated Content Creation
Content automation manifests in many different ways throughout the content lifecycle.
Automated content creation is currently implemented within pharma teams across the industry.
- Assemble a specific working document from a repository of structured components
- Generate regional report variants from a single “super” document
- Retrieve LIMS information from a data lake and format as a table in a structured component within a document
- Populate master data throughout a document set from a single source of truth
- Apply formatting, layout, and design elements to documents
- Reuse content based on assembly rules and authoring templates
There are many opportunities for content automation in the pharma industry. Content reuse is one of the most common, most impactful ways to automate content creation. With content reuse, pharma companies can create and deliver documents and other outputs extremely quickly.
Medical writers don’t have to waste time with copy and paste or with formatting (and reformatting) content across documents. Revision cycles become much more efficient, as it’s easier to find and update information when it is reused than when it has been pasted or created repetitively throughout a set of documents.
Structured content management systems can receive master data from RIM systems to autopopulate components and documents. This master data can also trigger automated assembly of content, workflow tasks, or other repetitive processes that can be entrusted to systems.
Many pharma companies are considering the benefits of artificial intelligence (AI) engines to automate content generation and improve findability across large volumes of content.
How to Use Artificial Intelligence to Create Content
Generative AI (GenAI), where the AI engine creates new content, is a rapidly growing field. Advances are being made literally every day. If you are considering incorporating GenAI in your content ecosystem, it is important to keep up with the latest developments.
One thing that has not changed about including GenAI as a tool is the need for your content to be AI-ready. AI readiness is perhaps the most important step that should not be skipped in any application of AI technology, particularly with complex and critical information.
AI engines must be trained and, in particular, they need to be trained using your specific content corpus. Without the right training, you cannot be sure that content or data generated from AI is accurate. In fact, unless you closely monitor how you train the AI, you might never be able to trust AI to write factual information.
We have all heard of situations where current AI solutions have “gone off on their own” and woven fantasy answers for basic questions. With the need for explicit accuracy paramount in pharma content, great care must be taken in training the engine and monitory the results.
The best way to train AI is using standardized, structured content. Curating the training set to ensure it is accurate and understandable is an important step in AI readiness. Companies that think they can skip this step will be in danger of having the AI create imaginative answers to very important questions.
The benefits of applying artificial intelligence in the pharmaceutical industry are based on two things:
- The speed that AI can process data
- The ability for AI to analyze data and hypothesize results
AI can process enormous amounts of information at lightning speed. The quantity and speed of information processing allows AI to deliver more statistically significant analysis of massive amounts of data. In addition to the analysis, AI engines can sort through the data in different ways, providing insights that human analysis might miss and predictions that would be impossible to make otherwise.
You need three sets of content to train an AI engine:
- One for the AI to ingest
- One to use for training the AI
- One to validate the training you have done
The most effective way to train and AI engine is to use standardized, structured content. Using componentized chunks of “clean” content. The cleaner and easier to understand your training corpus, the better your results will be. If you train the AI with messy, long form content, it is possible that you will introduce information that results in confused or incorrect responses.
By componentizing and curating your content, you can ensure that the learning done by your machine contains accurate results from your data. Skip this step at your peril. To learn more about implementing AI, download our free eBook, AI in Your Pocket: The Four Stages of a Successful AI Implementation.
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