Problem
Many in Regulatory will know this scenario all too well: A response letter is received from a regulatory body, containing an inquiry regarding a recent submission. The question posed in the letter is likely similar to one that was previously received and answered, with agency acceptance. Many organizations already store and track such agency questions, so one should be able to search that correspondence repository and find the previous question. However, actually doing so is nearly impossible given that the new question, although similar, is not quite phrased the same way, or perhaps when it was filed internally, was mistagged or placed in the wrong location. Sound familiar?
Solution
The good news is that none of those hurdles are problems for Docxonomy. Our deep analysis of both structured and unstructured content in your repositories, and our ability to recognize similarity within natural language, are the solution to this problem. A user can simply perform a similarity search at either the document or phrase level to find previous agency correspondence. What could have taken hours, days, or even weeks to locate now takes only seconds.
Comparison of Promotional Material to Product Documentation
Problem
Promotional materials fall under enormous scrutiny by health authorities. Violations regarding language, missing or misleading content can be extremely costly, leading to an extended time-to-market and requiring change management, updates in multiple languages and re-approval cycles.
Solution
Docxonomy’s ability to understand context, compare content and find similarity (or lack thereof) allows our system to identify promotional material problems before their even sent to the agency, saving time and money, not to mention avoiding citations.
Predictive Label Change Identification
Problem
Given a proposed labeling change to a product, assist change managers in identifying and locating label components and/or other affected labels that may also require an update.
Solution
Docxonomy can crawl, analyze and index disparate labeling repositories and use a predictive, artificially intelligent model to identify labeling components affected by a proposed change to increase compliance and efficiency. Since, through its deep analysis, Docxonomy understands the context of label content, it is well suited to this task and others.
A Step Further: Label Linking and Contextual Similarity
Problem
Life Science product labels require the same information be conveyed to different audiences. As an example, a Summary of Product Characteristics (SmPC) contains scientific information regarding a drug, and the Patient Information Leaflet (PIL) must contain many of the same attributes but is intended for a very different audience: the patient. Thus, when the SmPC changes, the associated PIL may also require an update. Given the often, large number of products with an organization and the volume of label changes taking place, this process of change can be daunting and costly.
Solution
Docxonomy’s abilities to both understand context and find similarity among content, combine beautifully in this respect as they can be used in concert to not only automatically link an SmPC to its associated PIL, but also identify and suggest the sections and phrases within those documents that may require a change. The cognitive assistance provided by Docxonomy in this regard can save millions of dollars in change management costs, reduce complexity and increase compliance.