The Future of Self-Service Drug Label Information with Natural Language Processing

Drug labels are a rich source of information for many different uses across the pharmaceutical continuum. To explore the full landscape of drug labels – a large amount of manual effort is usually required. However, NLP applications offer a streamlined process. Users can extract relevant data across multiple sources of labels to find what they need for better decision-making. Hywel Evans, Director of Linguamatics, an IQVIA company, shares his insights about how natural language processing (NLP) can help in accurately processing self-service drug label information.

Drug labels are a rich source of information for many different uses across the pharmaceutical continuum. From bench to bedside, making clinical decisions is a massive task, and the increasing volumes of data available from new and disparate sources continue to create challenges for the industry. 

Natural language processing (NLP) technology is helping life science companies tap into drug label information and increase the efficiency of targeted processes while drawing actionable research insights healthcare providers (HCPs) can use to make informed clinical decisions. 

The Challenges of Drug Label Analysis

Regulatory, medical, and labeling teams are constantly analyzing drug label content gleaned from many different data sources and languages. This creates considerable challenges, such as:

• The perpetuation of manual processes: These are typically time-consuming and potentially inaccurate, especially when a team must work within a time-box fashion to get a deliverable out. 
• A plethora of international sources: Some teams focus on sources such as the U.S. Food and Drug Administration (FDA) and the European Medicines Association (EMA). Others look to a broader set of English language sources, such as the U.K.’s Medication and Healthcare Regulatory Authority (MHRA), Canada, Australia, and additional markets, to get more data points in and around English language labels. Many teams also look to find precedents in specific country sources and other languages. 
• The large number of platforms in use: The proliferation of platforms used for drug labeling information requires users to develop proficiency in different techniques across multiple platforms. This is another costly and time-consuming exercise that delivers limited value. 
• Varying data terminologies: The many different forms of pharmaceutical terminology in use make searching datasets and analysis of labels a complex and challenging task. 

Given these challenges, NLP is emerging as a solution to rapidly identify relevant documents, details, and data gathered from drug label information and dedicated sources. 

Delivering Better Access to Labeling Information 

Today, NLP technology can facilitate access to drug label information by helping teams identify the correct product names, diseases, symptom terms, medical terms, and safety concepts. In addition, NLP can uncover the digital metadata associated with labels, such as country, language, category, and company name. 

Technology enables organizations to quickly extract the relevant data needed to carry out analyses or to look through annotated versions of the labels to find what they need. Simultaneously, the software’s NLP capabilities improve information accuracy and facilitate better decision-making. 

Companies can move from a document to a data-driven approach by implementing NLP technology. The use of sophisticated software permits users to access individual pieces of synthesized label data. As a result, they can reduce the time their teams spend on the repetitive, non-profitable task of manually reading labels and quickly glean the information they need to make critical clinical decisions.

Implementing NLP Text Mining Techniques

NLP text mining techniques facilitate the rapid finding of labels within a single label source or across multiple sources, regardless of whether the user searches for a class of drug, a group, or a basket of drugs. HCPs can deploy text mining techniques to search long, complex drug labels for key data elements using NLP technology. This ability offers several benefits, including: 

1. Identifying and highlighting risks and safety factors while reducing the workload required for the HCP to perform these actions manually. 
2. Workflow automation powered by artificial intelligence, machine learning, and NLP programs delivering better insights. 
3. Faster, more accurate label exploration minimizes the rework required for HCPs and saves time and costs. 
4. Identifying content for groupings of events and different types of drugs helping teams author the most suitable content for regulatory approval and competitive positioning. 
5. Finding comparisons and identifying suppliers of a particular drug or product with similar attributes like mechanisms of action or pharmacokinetics. 
6. Specifying characteristics to get the precise analysis needed.
7. Understanding company drug labels and changes required in response to external factors. 
8. Reviewing label terminology across jurisdictions and implementing technology that supports flexibility and consistency. 
9. Extracting contraindications, in which a treatment, procedure, or surgery should not be used due to potential harm to the patient based on precedents, such as vaccines or antiviral drugs in children.
10. Developing search rules that enable users to set up repeatable alerts for items of interest. 

Any analysis performed is simpler to understand at an aggregate level. By making the most of NLP technology, teams can truly capture all the different data points needed to provide a rich label analysis. 

Reaping the Benefits of NLP Technology

Labeling teams within regulatory environments are primary users of NLP because they are authoring product documents to position them competitively on the market. For these teams, it is essential to be informed and data-driven when managing the lifecycle of existing labels and products. Even more so, teams are charged with ensuring that the products can be used safely by patients and aligning local regulatory strategies with their overall global product strategy. 

In terms of safety, drug labels contain summaries of adverse events ranging from preclinical and clinical development to post-marketing surveillance. The drug safety landscape is critically important, so teams must use multiple data sources to track events. Communicating real-world benefits and usage of products to stakeholders such as prescribers, key opinion leaders, and others forms an important baseline of information. This complements existing real-world evidence, published research, safety information, data, and other sources. 

See More: Unlock Stronger Customer Relationships With Natural Language Processing

The Bottom Line

NLP technology offers new ways for HCPs and life sciences companies to mine and analyze drug label information. This method can help eliminate inconsistencies, improve understanding of medication usage, and develop documentation that adequately answers questions about substances and their effects. At the same time, NLP usage gives HCPs and their colleagues the information needed to facilitate rich conversations that deliver value and power the decisions they need to make. 

Technology holds tremendous promise for health and medicine. Harnessing NLP improves access to drug label information and streamlines its usage to benefit patients and providers.

Which other sectors could actively harness the power of NLP to improve processes? Tell us what you think on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear what you have to say!

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